Information processing method, processing method, display method, display device, information processing device, computer program, and recording medium

Abstract

The information processing method is an information processing method for generating a difference model representing a difference between an object model representing a three-dimensional shape of an object and a target model representing a target shape of the object, the information processing method comprising the steps of: acquiring a plurality of unit regions, a plurality of unit regions each corresponding to a part of the target model, the distance between the plurality of unit regions and the object model being equal to or greater than a first threshold value set by a first input by a user; acquiring, as a clustering region, a group of unit regions in which the distance between the two unit regions is equal to or less than a second threshold value set by a second input by the user; and outputting, as a difference model, at least one of the clustering regions acquired after the reception of the first input and the reception of the second input.

Description

Technical Field

[0001] The present invention relates to the technical fields of, for example, an information processing method, an information processing apparatus, and a computer program for performing information processing related to a three-dimensional model, a processing method for generating processing control information for machining an object, and a display method, a display apparatus, and a computer program for performing display related to a three-dimensional model. Background Art

[0002] An example of a processing apparatus for processing an object is described in Patent Document 1. As one of the technical problems of such a processing apparatus, appropriately generating processing control information for controlling the processing of an object can be cited.

[0003] Prior Art Documents

[0004] Patent Documents

[0005] Patent Document 1: U.S. Patent Application Publication No. 2018 / 0029298 Summary of the Invention

[0006] According to a first aspect, there is provided an information processing method for generating a differential model representing a part added to an object in order to machine the object into a target shape, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object, wherein the information processing method includes the following steps: receiving a first input as an input from a user; obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model and the distance between the plurality of unit regions and the object model being equal to or greater than a first threshold set by the first input; receiving a second input as an input from the user; taking a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the second input as one clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

[0007] According to the second method, an information processing method is provided. This information processing method is used to generate a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Among them, this information processing method includes the following steps: displaying a first display image, which displays a plurality of unit regions and a first operation object that can be operated by the user to adjust a first threshold. The plurality of unit regions are obtained by subdividing the target model, and the distance between the plurality of unit regions and the object model is greater than or equal to the first threshold; displaying a second display image, taking a set of unit regions whose distance between two unit regions among the plurality of unit regions is less than or equal to a second threshold as a clustering region. The second display image displays at least one of the clustering regions and a second operation object that can be operated by the user to adjust the second threshold; and outputting at least one of the clustering regions obtained after operating the first operation object and the second operation object as the difference model or as information for generating the difference model.

[0008] According to the third method, an information processing method is provided. This information processing method is used to generate a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Among them, this information processing method includes the following steps: accepting a first input as the input of the user; obtaining a plurality of unit regions, each of which corresponds to a part of the target model, and the distance between the plurality of unit regions and the object model is greater than or equal to a first threshold set by the first input; taking a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold as a clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained according to the second threshold as the difference model or as information for generating the difference model.

[0009] According to the fourth method, an information processing method is provided. This information processing method is used to generate a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Among them, this information processing method includes the following steps: obtaining a plurality of unit regions, each of the plurality of unit regions corresponds to a part of the target model, and the distance between the plurality of unit regions and the object model is equal to or greater than a first threshold; accepting an input as the input of the user; taking a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the input as a clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained after accepting the input as the difference model or as information for generating the difference model.

[0010] According to the fifth method, an information processing method is provided. This information processing method is used to generate a difference model representing the part removed from the object to process the object into the target shape. The difference model is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of the object, and the target model is a three-dimensional model representing the target shape of the object. Among them, the information processing method includes the following steps: accepting a first input as the input of the user; obtaining a plurality of unit regions, each of the plurality of unit regions corresponds to a part of the object model, and the distance between the plurality of unit regions and the target model is equal to or greater than a first threshold set by the first input; accepting a second input as the input of the user; taking a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the second input as a clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained after accepting the first input and the second input as the difference model or as information for generating the difference model.

[0011] According to the sixth mode, an information processing method is provided. This information processing method is used to generate a differential model representing the part to be processed in order to process an object into a target shape. The differential model is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of the object, and the target model is a three-dimensional model representing the target shape of the object. Wherein, the information processing method includes the following steps: receiving a first input as the input of the user; obtaining a plurality of unit regions, each of the plurality of unit regions corresponds to a part of one of the object model and the target model, and the distance between the plurality of unit regions and the other of the object model and the target model is greater than or equal to a first threshold set by the first input; receiving a second input as the input of the user; taking a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold set by the second input as a clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

[0012] According to the seventh mode, a processing method is provided. This processing method generates processing control information for using a processing device capable of processing the object to process the shape of the object into the target shape according to the differential model generated by using the information processing method provided by any one of the first mode to the sixth mode.

[0013] According to the eighth mode, an information processing method is provided. This information processing method is used to generate a differential model, which is a three-dimensional model representing the difference between a first model and a second model. Wherein, the information processing method includes the following steps: receiving a first input as the input of the user; obtaining a plurality of unit regions, each of the plurality of unit regions corresponds to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model is greater than or equal to a first threshold set by the first input; receiving a second input as the input of the user; taking a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold set by the second input as a clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

[0014] According to the ninth mode, an information processing method is provided. The information processing method is used to generate a difference model, which is a three-dimensional model representing the difference between a first model and a second model. The information processing method includes the following steps: obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model being equal to or greater than a first threshold; taking a set of unit regions with the distance between two unit regions in the obtained plurality of unit regions being equal to or less than a second threshold as a clustering region, and obtaining at least one of the clustering regions; and outputting at least one of the clustering regions obtained according to the second threshold as the difference model or as information for generating the difference model.

[0015] According to the tenth mode, a display method is provided. The display method is used to display a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. The display method includes the following steps: displaying a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being equal to or greater than a first threshold set by a first input as the input of the user; taking a set of unit regions with the distance between two unit regions in the obtained plurality of unit regions being equal to or less than a second threshold set by a second input as the input of the user as a clustering region, and displaying at least one of the clustering regions; and displaying at least one of the clustering regions obtained after accepting the first input and the second input as the difference model.

[0016] According to the eleventh aspect, a display device is provided. The display device is configured to display a differential model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. The display device has an input device. The display device displays a plurality of unit regions as a first display image. Each of the plurality of unit regions corresponds to a part of the target model, and the distance between the plurality of unit regions and the object model is equal to or greater than a first threshold set by the input device. The display device takes a set of unit regions, where the distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the input device, as a clustering region, and displays at least one of the clustering regions as a second display image. The display device displays at least one of the clustering regions obtained according to the second threshold as the differential model.

[0017] According to the twelfth aspect, an information processing device is provided. The information processing device uses the information processing method provided by any one of the first to sixth aspects and the eighth to ninth aspects to generate the differential model.

[0018] According to the thirteenth aspect, a computer program is provided. The computer program causes a computer to execute the information processing method provided by any one of the first to sixth aspects and the eighth to ninth aspects.

[0019] According to the fourteenth aspect, a computer program is provided. The computer program causes a computer to execute the display method provided by the tenth aspect.

[0020] The effects and other advantages of the present invention can be understood from the embodiments described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a block diagram showing the overall structure of the processing system of the present embodiment.

[0022] Figure 2 It is a block diagram showing the system structure of the processing device of the present embodiment.

[0023] Figure 3 It is a cross-sectional view showing the structure of the processing device of the present embodiment.

[0024] Figure 4 It is a block diagram showing the structure of the measurement system.

[0025] Figure 5 It is a block diagram showing the structure of the control information generation device.

[0026] Figure 6 (a) to Figure 6 (e) are cross-sectional views showing a case where a modeling light is irradiated onto a certain area on a workpiece and a modeling material is supplied.

[0027] Figure 7 (a) to Figure 7 (c) are cross-sectional views showing the process of modeling a three-dimensional structure.

[0028] Figure 8 is a flowchart showing the overall flow of the control information generation operation.

[0029] Figure 9 Schematically shows an object model and a target model.

[0030] Figure 10 Schematically shows an object model, a target model, and a difference model.

[0031] Figure 11 Schematically shows a certain point included in the object model and a certain point included in the target model.

[0032] Figure 12 Schematically shows a plurality of difference points.

[0033] Figure 13 Schematically shows a point group clustering in which a plurality of difference points are respectively classified.

[0034] Figure 14 is a flowchart showing the flow of the difference model generation operation.

[0035] Figure 15 Shows a difference point display image.

[0036] Figure 16 Shows a point group clustering display image.

[0037] Figure 17 Shows a point group clustering display image.

[0038] Figure 18 (a) to Figure 18 (c) respectively show the file formats of files storing the original model and the reduced model.

[0039] Figure 19 Shows a first specific example of the difference model generation operation using the reduced model.

[0040] Figure 20 Shows a second specific example of the difference model generation operation using the reduced model.

[0041] Figure 21 Shows a third specific example of the difference model generation operation using the reduced model.

[0042] Figure 22 Shows a fourth specific example of generating an action of a differential model using a reduced model.

[0043] Figure 23 Shows a scenario where the model - to - model distance threshold and the clustering threshold are followed.

[0044] Figure 24 Schematically shows a target model in the case where a workpiece is deformed as the workpiece is used and an object model in the case where a workpiece is deformed as the workpiece is used.

[0045] Figure 25 Schematically shows a differential model generated based on a target model in the case where a workpiece is deformed as the workpiece is used and an object model in the case where a workpiece is deformed as the workpiece is used.

[0046] Figure 26 Schematically shows an undeformed target model and a deformed target model.

[0047] Figure 27 Schematically shows a differential model generated based on a target model deformed based on an object model and an object model in the case where a workpiece is deformed as the workpiece is used.

[0048] Figure 28 Schematically shows an undeformed object model and a deformed object model.

[0049] Figure 29 Schematically shows a differential model generated based on a target model and an object model generated by deforming the object model.

[0050] Figure 30 Shows an extraction point display image of the fifth modification example.

[0051] Figure 31 Shows a point - group clustering display image of the fifth modification example.

[0052] Figure 32 Shows a merging display image of the sixth modification example.

[0053] Figure 33 Shows an extraction point display image of the seventh modification example.

[0054] Figure 34 Shows a point - group clustering display image of the eighth modification example.

[0055] Figure 35 Shows a merging display image of the ninth modification example.

[0056] Figure 36Shows the machine learning model used in the 10th modification example.

[0057] Figure 37 Shows an outline of the machine learning of the machine learning model used in the 10th modification example.

[0058] Figure 38 Schematically shows an object model, a target model, and a difference model. Detailed implementation

[0059] Hereinafter, embodiments of an information processing method, a processing method, a display method, a display device, an information processing device, and a computer program will be described with reference to the drawings. Hereinafter, a processing system SYS that can process a workpiece W as an example of an object will be used to describe embodiments of the information processing method, the processing method, the display method, and the display device.

[0060] (1) Structure of the processing system SYS

[0061] (1-1) Overall structure of the processing system SYS

[0062] First, refer to Figure 1 to describe the overall structure of the processing system SYS. Figure 1 is a block diagram showing the overall structure of the processing system SYS.

[0063] As Figure 1 shown, the processing system SYS includes a processing device 1, a measurement system 2, and a transfer device 3. In addition, in the example shown in Figure 1 , the processing system SYS has a single processing device 1, but may also have a plurality of processing devices 1. The processing system SYS has a single measurement system 2, but may also have a plurality of measurement systems 2. The processing system SYS has a single transfer device 3, but may also have a plurality of transfer devices 3. However, the processing system SYS may not have a transfer device 3.

[0064] When the processing system SYS has a plurality of processing devices 1, the number of measurement systems 2 may be less than the number of processing devices 1. For example, the processing system SYS may have two or more processing devices 1 and one measurement system 2. In addition, when the processing system SYS has a plurality of measurement systems 2, the number of processing devices 1 may be less than the number of measurement systems 2. For example, the processing system SYS may have one processing device 1 and two or more measurement systems 2.

[0065] The processing device 1 can process the workpiece W. In the present embodiment, an example of the processing device 1 that can process the workpiece W by irradiating the workpiece W with processing light EL (i.e., an energy beam in the form of light) will be described. However, the processing device 1 can also process the workpiece W without using the processing light EL.

[0066] The processing device 1 can perform additional processing on the workpiece W. That is, the processing device 1 can create a modeled object on the workpiece W by performing additional processing on the workpiece W. In this case, the processing device 1 can also create a modeled object that can be integrated with or separated from the workpiece W by performing additional processing on the workpiece W. The modeled object created by the processing device 1 can also refer to any object created by the processing device 1. For example, as an example of the modeled object, the processing device 1 can also create a three-dimensional structure ST (i.e., a three-dimensional structure that has dimensions in any direction in the three-dimensional direction, is a three-dimensional object, in other words, is a structure that has dimensions in the X-axis direction, Y-axis direction, and Z-axis direction). In addition, the processing device 1 that can perform additional processing can also be referred to as an additional processing device.

[0067] The processing device 1 can also perform additional processing using any additional processing method (i.e., modeling method) that can create a modeled object. As an example of the additional processing method, at least one of a powder bed fusion method such as a laser metal deposition method (LMD: Laser Metal Deposition), a selective laser sintering method (SLS: Selective Laser Sintering), a binder jetting method (adhesive jetting method), a material jetting method (material jetting method), a stereolithography method, and a laser metal fusion method (LMF: Laser Metal Fusion) can be cited. In addition, the laser metal deposition method can also be referred to as a directed energy deposition method (DED: Directed Energy Deposition).

[0068] The workpiece W can also be a defective item to be repaired. In this case, the processing device 1 can also perform repair processing (in other words, restoration) of the defective item to be repaired by performing additional processing to create a modeled object for filling the defective part. That is, the additional processing performed by the processing device 1 can also include additional processing of attaching a three-dimensional structure ST equivalent to the modeled object for filling the defective part to the workpiece W. The additional processing performed by the processing device 1 can also be at least a part of the repair process of the workpiece W having a defective part.

[0069] As an example of a workpiece to be repaired with a defective part, at least a part of a worn turbine can be cited. For example, as an example of a workpiece to be repaired with a defective part, a turbine blade constituting a turbine can be cited. As an example of a turbine, at least one of a power generation turbine and an aircraft engine turbine, etc. can be cited. In this case, the processing device 1 can also repair (in other words, restore) the worn turbine. As another example of a workpiece to be repaired with a defective part, a worn propeller-shaped part can be cited. As another example of a workpiece to be repaired with a defective part, body parts of vehicles such as automobiles, motorcycles, electric vehicles, and railway vehicles can be cited. As another example of a workpiece to be repaired with a defective part, parts of engines such as automobile engines, motorcycle engines, and spacecraft engines can be cited. As another example of a workpiece to be repaired with a defective part, parts of an electric vehicle battery can be cited. The processing device 1 can also repair these workpieces to be repaired.

[0070] The workpiece W can also be a base for shaping a three-dimensional structure ST. In this case, the processing device 1 can also manufacture the three-dimensional structure ST from the beginning by performing additional processing for shaping the three-dimensional structure ST on the workpiece W. As an example, the processing device 1 can also manufacture a turbine from the beginning by performing additional processing for shaping a three-dimensional structure ST equivalent to a turbine on the workpiece W.

[0071] The workpiece W can also be an intermediate product manufactured during the process of shaping the three-dimensional structure ST. In this case, the processing device 1 can also manufacture the three-dimensional structure ST from the intermediate product by performing additional processing for completing the three-dimensional structure ST on the workpiece W which is an intermediate product of the three-dimensional structure ST. As an example, the processing device 1 can also manufacture a finished product of a turbine from an intermediate product of a turbine by performing additional processing for completing the turbine on the workpiece W which is an intermediate product of the turbine.

[0072] The measurement system 2 measures the workpiece W before the processing device 1 actually starts processing the workpiece W. In the present embodiment, the measurement system 2 measures the three-dimensional shape of the workpiece W. In addition, if the three-dimensional shape of the workpiece W is determined, the position of the workpiece W in the three-dimensional space (for example, the position of the surface of the workpiece W) is determined. Therefore, measuring the three-dimensional shape of the workpiece W can be substantially regarded as equivalent to measuring the position of the workpiece W.

[0073] The measurement system 2 also generates machining control information. The machining control information is control information for controlling the machining apparatus 1 to machine the workpiece W. In particular, the machining control information is control information for controlling the machining apparatus 1 to machine the workpiece W so that the shape of the workpiece W becomes the target shape. For example, the machining control information may also include machining route information. The machining route information may also indicate the target irradiation position (e.g., the position of the target irradiation area EA described later) where the machining light EL should be irradiated to machine the workpiece W. Specifically, the machining route information may also indicate the path of the target irradiation position (e.g., the moving path of the target irradiation area EA described later) where the machining light EL should be irradiated to machine the workpiece W, i.e., the target moving path. This target moving path may also be referred to as the machining route or the tool path. In this case, the measurement system 2 may also generate a G code representing the machining route or the tool path as the machining control information. The measurement system 2 may also generate a file having an extension of gcode or gco as the machining control information. The machining control information generated by the measurement system 2 is sent from the measurement system 2 to the machining apparatus 1 via a communication network (not shown).

[0074] The machining apparatus 1 receives (i.e., acquires) the machining control information sent from the measurement system 2. The machining apparatus 1 that has received the machining route information machines the workpiece W according to the received machining control information. Therefore, after the measurement system 2 measures the three-dimensional shape of the workpiece W, the workpiece W is transported from the measurement system 2 to the machining apparatus 1. Specifically, the workpiece W is unloaded from the measurement system 2, and the unloaded workpiece W is transported to the machining apparatus 1. For example, the workpiece W may be transported from the measurement system 2 to the machining apparatus 1 by the transport device 3. For example, the workpiece W may also be transported from the measurement system 2 to the machining apparatus 1 by the user of the machining system SYS. The workpiece W transported to the machining apparatus 1 is set (in other words, placed or mounted) on the machining apparatus 1. As a result, the machining apparatus 1 can machine the workpiece W.

[0075] In addition, in Figure 1 the example shown, the machining system SYS has the machining apparatus 1 and the measurement system 2 as separate devices. However, the machining system SYS may also have a device in which the machining apparatus 1 and the measurement system 2 are integrated. That is, the machining apparatus 1 and the measurement system 2 may also be integrated.

[0076] The machining system SYS may also have a control server 4. The control server 4 may also control the operation of the entire machining system SYS. For example, the control server 4 may also control the operation of the machining apparatus 1. For example, the control server 4 may also control the operation of the measurement system 2. For example, the control server 4 may also control the operation of the transport device 3. However, the machining system SYS may not have the control server 4.

[0077] The control server 4 can also function as a cloud server. In this case, the control server 4 can also communicate with at least one of the processing device 1, the measurement system 2, and the transfer device 3 via a communication network including the Internet. Alternatively, the control server 4 can function as an edge server. In this case, the control server 4 can also communicate with at least one of the processing device 1, the measurement system 2, and the transfer device 3 via a communication network including an intranet or a local area network.

[0078] The processing system SYS can also have a first computer that controls the processing device 1 as part of the processing device 1 based on or instead of the control server 4 that controls the processing device 1. That is, the processing device 1 can also have a first computer. The first computer can be a laptop computer or other types of computers. The first computer can also function as a control unit 17 (refer to Figure 2 ). The processing system SYS can also have a second computer that controls the measurement system 2 as part of the measurement system 2 based on or instead of the control server 4 that controls the measurement system 2. That is, the measurement system 2 can also have a second computer. The second computer can be a laptop computer, a tablet terminal, a portable terminal such as a smart phone, or other types of computers. The second computer can also function as a control information generation device 22 (refer to Figure 4 ). The processing system SYS can also have a third computer that controls the transfer device 3 as part of the transfer device 3 based on or instead of the control server 4 that controls the transfer device 3. That is, the transfer device 3 can also have a third computer. The third computer can be a laptop computer or other types of computers.

[0079] (1-2) Structure of the processing device 1

[0080] Next, with reference to Figure 2 and Figure 3 the structure of the processing device 1 will be described. Figure 2 is a block diagram showing the system structure of the processing device 1. Figure 3 is a cross-sectional view showing the structure of the processing device 1.

[0081] In addition, in the following description, the XYZ orthogonal coordinate system defined by the mutually perpendicular X-axis, Y-axis, and Z-axis is used as the machining coordinate system to describe the positional relationship of various structural elements constituting the machining apparatus 1. In addition, in the following description, for ease of explanation, the X-axis direction and the Y-axis direction are respectively the horizontal directions (i.e., the specified directions in the horizontal plane), and the Z-axis direction is the vertical direction (i.e., the direction perpendicular to the horizontal plane, substantially the up-and-down direction). In addition, the rotational directions (in other words, the tilting directions) around the X-axis, Y-axis, and Z-axis are respectively referred to as the θX direction, θY direction, and θZ direction. Here, the Z-axis direction can also be used as the direction of gravity. In addition, the XY plane can also be set as the horizontal direction.

[0082] In addition, in the following description, for ease of explanation, as an example of the structure of the machining apparatus 1, the structure of the machining apparatus 1 that performs additional machining using the laser cladding method is described.

[0083] The machining apparatus 1 that performs additional machining using the laser cladding method performs additional machining by machining the modeling material M using the machining light EL. The modeling material M is a material that can be melted by irradiation with the machining light EL having a specified intensity or higher. As such a modeling material M, for example, at least one of a metallic material and a resinous material can be used. However, as the modeling material M, other materials different from the metallic material and the resinous material can also be used. The modeling material M is a powdery or granular material. That is, the modeling material M is a powder or granule. However, the modeling material M may not be a powder or granule. For example, as the modeling material M, at least one of a linear modeling material and a gaseous modeling material can also be used.

[0084] The machining apparatus 1 that performs additional machining using the laser cladding method shapes a three-dimensional structure ST in which a plurality of structure layers SL are stacked (refer to Figure 7 ) by sequentially forming a plurality of structure layers SL. In this case, the machining apparatus 1 first sets the surface of the workpiece W as the modeling surface MS on which the model is actually shaped, and shapes the first structure layer SL on the modeling surface MS. Then, the machining apparatus 1 sets the surface of the first structure layer SL as a new modeling surface MS, and shapes the second structure layer SL on the modeling surface MS. After that, the machining apparatus 1 shapes a three-dimensional structure ST in which a plurality of structure layers SL are stacked by repeating the same operation.

[0085] For performing additional machining, as Figures 2 to 3As shown, the processing apparatus 1 includes a material supply source 11, a processing unit 12, a worktable unit 13, a light source 15, a gas supply source 16, and a control unit 17. The processing unit 12 and the worktable unit 13 may also be housed in the chamber space 183IN inside the housing 18. Additionally, at least one of the processing unit 12 and the worktable unit 13 may not be housed in the chamber space 183IN inside the housing 18.

[0086] The material supply source 11 supplies the modeling material M to the processing unit 12. The material supply source 11 supplies a desired amount of the modeling material M corresponding to the required amount in such a way as to supply the amount of the modeling material M required per unit time for additional processing to the processing unit 12.

[0087] The processing unit 12 processes the modeling material M supplied from the material supply source 11 to form a modeled object. In order to form a modeled object, the processing unit 12 includes a processing head 121 and a head drive system 122. Further, the processing head 121 includes an irradiation optical system 1211 and a material nozzle 1212. Additionally, in Figures 2 to 3 the example shown, the processing head 121 includes a single irradiation optical system 1211, but the processing head 121 may also include multiple irradiation optical systems 1211. Additionally, in Figures 2 to 3 the example shown, the processing head 121 includes a single material nozzle 1212, but the processing head 121 may also include multiple material nozzles 1212.

[0088] The irradiation optical system 1211 is an optical system (such as a condensing optical system) for emitting processing light EL. Specifically, the irradiation optical system 1211 is optically connected to the light source 15 that emits the processing light EL via a light transmission component 151 such as an optical fiber or a light pipe. The irradiation optical system 1211 emits the processing light EL propagated from the light source 15 via the light transmission component 151. The irradiation optical system 1211 irradiates the processing light EL downward (i.e., the -Z side) from the irradiation optical system 1211. A worktable 131 is disposed below the irradiation optical system 1211. When a workpiece W is placed on the worktable 131, the irradiation optical system 1211 irradiates the workpiece W with the emitted processing light EL. In this case, the irradiation optical system 1211 irradiates the workpiece W with the processing light EL from above the workpiece W. Specifically, the irradiation optical system 1211 can irradiate the processing light EL to a target irradiation area EA set on or near the workpiece W as an area for irradiating the processing light EL (typically condensing). And the state of the irradiation optical system 1211 can be switched between a state of irradiating the processing light EL to the target irradiation area EA and a state of not irradiating the processing light EL to the target irradiation area EA under the control of the control unit 17.

[0089] The material nozzle 1212 supplies the molding material M (e.g., injection, spraying, ejection, or coating). The material nozzle 1212 is physically connected to the material supply source 11, which is the supply source of the molding material M, via the supply pipe 111 and the mixing device 112. The material nozzle 1212 supplies the molding material M supplied from the material supply source 11 via the supply pipe 111 and the mixing device 112. The material nozzle 1212 may also pressure-feed the molding material M supplied from the material supply source 11 via the supply pipe 111. That is, the molding material M from the material supply source 11 and the gas for conveyance (i.e., the pressure-feeding gas, such as an inert gas like nitrogen, argon, etc.) may also be mixed in the mixing device 112 and then pressure-fed to the material nozzle 1212 via the supply pipe 111. As a result, the material nozzle 1212 supplies the molding material M together with the gas for conveyance. As the gas for conveyance, for example, the purge gas supplied from the gas supply source 16 is used. However, as the gas for conveyance, a gas supplied from a gas supply source different from the gas supply source 16 may also be used. The material nozzle 1212 supplies the molding material M downward (i.e., the -Z side) from the material nozzle 1212. The worktable 131 is disposed below the material nozzle 1212. When the workpiece W is placed on the worktable 131, the material nozzle 1212 supplies the molding material M to the workpiece W or the vicinity of the workpiece W.

[0090] In the present embodiment, the material nozzle 1212 supplies the molding material M to the irradiation position of the processing light EL (i.e., the target irradiation area EA irradiated with the processing light EL from the irradiation optical system 1211). Therefore, the material nozzle 1212 is aligned with the irradiation optical system 1211 such that the target supply area MA, which is set as the area where the material nozzle 1212 supplies the molding material M, on or near the workpiece W coincides with (or at least partially overlaps) the target irradiation area EA. In this case, the processing light EL emitted from the irradiation optical system 1211 irradiates the molding material M supplied from the material nozzle 1212. As a result, the molding material M melts. That is, a molten pool MP containing the molten molding material M is formed on the workpiece W.

[0091] In addition, the material nozzle 1212 may also supply the molding material M to the molten pool MP formed by the processing light EL emitted from the irradiation optical system 1211. Or, for example, the processing device 1 may also melt the molding material M from the material nozzle 1212 by the irradiation optical system 1211 before the molding material M reaches the workpiece W and attach the molten molding material M to the workpiece W.

[0092] The head driving system 122 moves the processing head 121 under the control of the control unit 17. That is, the head driving system 122 moves the irradiation optical system 1211 and the material nozzle 1212 under the control of the control unit 17. The head driving system 122 moves the processing head 121 in at least one direction among the X-axis, Y-axis, Z-axis, θX direction, θY direction, and θZ direction, for example. When the head driving system 122 moves the processing head 121, the relative positions of the processing head 121 with respect to the worktable 131 and the workpiece W placed on the worktable 131 change. As a result, the target irradiation area EA and the target supply area MA (and thus, the molten pool MP) move relative to the workpiece W.

[0093] The worktable unit 13 includes a worktable 131 and a worktable driving system 132.

[0094] The workpiece W is placed on the worktable 131. The worktable 131 can support the workpiece W placed on the worktable 131. The worktable 131 may also be able to hold the workpiece W placed on the worktable 131. In this case, in order to hold the workpiece W, the worktable 131 may have at least one of a mechanical chuck, an electrostatic chuck, a vacuum suction chuck, etc. Alternatively, the worktable 131 may not be able to hold the workpiece W placed on the worktable 131. In this case, the workpiece W may be placed on the worktable 131 by a jig. In addition, the workpiece W may be mounted on a holding fixture such as a jig, or the holding fixture on which the workpiece W is mounted may be placed on the worktable 31. In addition, the workpiece W may not be placed on the worktable 131, and may be placed on the ground, for example.

[0095] The holding fixture for mounting the workpiece W may also include beam members disposed around the workpiece W. As an example, the holding fixture may include beam members surrounding the outer periphery of the workpiece W. Multiple workpieces W may be mounted on the holding fixture. In this case, the holding fixture may include beam members disposed around at least two of the multiple workpieces W. As an example, the holding fixture may include beam members surrounding the outer periphery of at least two workpieces W. As another example, the holding fixture may include beam members passing through the space between at least two workpieces W. As another example, the holding fixture may include two beam members passing through the space between at least two workpieces W and crossing each other. In this case, at least a part of at least two workpieces W may be disposed outside the rectangular region formed by connecting the two ends of the two beam members. In addition, the holding fixture may be placed on the worktable 31 via a support member that can kinematically support the holding fixture.

[0096] The worktable drive system 132 moves the worktable 131 under the control of the control unit 17. The worktable drive system 132 moves the worktable 131, for example, along at least one of the X-axis, Y-axis, Z-axis, θX direction, θY direction, and θZ direction. When the worktable drive system 132 moves the worktable 131, the relative positions of the worktable 131 and the workpiece W placed on the worktable 131 with respect to the machining head 121 change. As a result, the target irradiation area EA and the target supply area MA (and thus, the molten pool MP) move relative to the workpiece W.

[0097] The light source 15 emits, for example, at least one of infrared light, visible light, and ultraviolet light as the processing light EL. However, other types of light may also be used as the processing light EL. The processing light EL may also include a plurality of pulsed lights (i.e., a plurality of pulsed light beams). The processing light EL may also include continuous light (CW: Continuous Wave). The processing light EL may also be a laser. In this case, the light source 15 may also include a semiconductor laser such as a laser light source (e.g., a laser diode (LD: Laser Diode)). The laser light source may also include at least one of a fiber laser, a CO 2 laser, a YAG laser, and an excimer laser, etc. However, the processing light EL may not be a laser. The light source 15 may also include any light source (e.g., at least one of an LED (Light Emitting Diode) and a discharge lamp, etc.).

[0098] The gas supply source 16 is a supply source for the purge gas for purging the chamber space 183IN inside the housing 18. The purge gas contains an inert gas. As an example of the inert gas, nitrogen or argon can be cited. The gas supply source 16 is connected to the chamber space 183IN via a supply port 182 formed in the partition member 181 of the housing 18 and a supply pipe 161 connecting the gas supply source 16 and the supply port 182. The gas supply source 16 supplies the purge gas to the chamber space 183IN via the supply pipe 161 and the supply port 182. As a result, the chamber space 183IN becomes a space purged by the purge gas. The purge gas supplied to the chamber space 183IN may also be discharged from an unillustrated discharge port formed in the partition member 181. In addition, the gas supply source 16 may also be a gas cylinder storing an inert gas. In the case where the inert gas is nitrogen, the gas supply source 16 may also be a nitrogen generation device that generates nitrogen from the atmosphere.

[0099] When the material nozzle 1212 supplies the molding material M together with the purge gas, the gas supply source 16 can also supply the purge gas to the mixing device 112 that is supplied with the molding material M from the material supply source 11. Specifically, the gas supply source 16 can also be connected to the mixing device 112 via a supply pipe 162 that connects the gas supply source 16 and the mixing device 112. As a result, the gas supply source 16 supplies the purge gas to the mixing device 112 via the supply pipe 162. In this case, the molding material M from the material supply source 11 can also be supplied (specifically, pressured) to the material nozzle 1212 through the supply pipe 111 by using the purge gas supplied from the gas supply source 16 via the supply pipe 162. That is, the gas supply source 16 can also be connected to the material nozzle 1212 via the supply pipe 162, the mixing device 112, and the supply pipe 111. In this case, the material nozzle 1212 supplies the molding material M together with the purge gas for pressuring the molding material M.

[0100] The control unit 17 controls the operation of the processing device 1. For example, the control unit 17 can also control the processing unit 12 (e.g., at least one of the processing head 121 and the head drive system 122) provided in the processing device 1 to process the workpiece W. For example, the control unit 17 can also control the worktable unit 13 (e.g., the worktable drive system 132) provided in the processing device 1 to process the workpiece W.

[0101] The control unit 17 can also have an arithmetic device and a storage device, for example. The arithmetic device can include at least one of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), for example. The storage device can also include a memory, for example. The control unit 17 functions as a device that controls the operation of the processing device 1 by executing a computer program with the arithmetic device. This computer program is a computer program for causing the arithmetic device to perform (i.e., execute) the actions of the control unit 17 described later. That is, this computer program is a computer program for causing the control unit 17 to function so that the processing device 1 performs the actions described later. The computer program executed by the arithmetic device can be recorded in the storage device (i.e., recording medium) of the control unit 17, or can be recorded in any storage medium (e.g., hard disk, semiconductor memory) built in or external to the control unit 17. Alternatively, the arithmetic device can download the computer program to be executed from a device external to the control unit 17 via the network interface.

[0102] The control unit 17 can also control the emission mode of the processing light EL of the irradiation optical system 1211. The emission mode can include, for example, at least one of the intensity of the processing light EL and the emission timing of the processing light EL. In the case where the processing light EL includes a plurality of pulsed lights, the emission mode can include, for example, at least one of the emission time of the pulsed light, the emission period of the pulsed light, and the ratio of the length of the emission time of the pulsed light to the emission period of the pulsed light (so-called duty ratio). Further, the control unit 17 can also control the movement mode of the processing head 121 based on the head drive system 122. The control unit 17 can also control the movement mode of the worktable 131 based on the worktable drive system 132. The movement mode can also include, for example, at least one of the movement amount, the movement speed, the movement direction, and the movement timing (movement period). Moreover, the control unit 17 can also control the supply mode of the modeling material M by the material nozzle 1212. The supply mode can include, for example, at least one of the supply amount (particularly the supply amount per unit time) and the supply timing (supply period).

[0103] The control unit 17 may not be provided inside the processing device 1. For example, the control unit 17 may be provided outside the processing device 1 as a server or the like. In this case, the control unit 17 and the processing device 1 may also be connected via a wired and / or wireless network (or a data bus and / or a communication line). As the wired network, for example, a network using an interface in a serial bus mode represented by at least one of IEEE1394, RS-232x, RS-422, RS-423, RS-485, and USB may be used. As the wired network, a network using an interface in a parallel bus mode may also be used. As the wired network, a network using an interface compliant with Ethernet (registered trademark) represented by at least one of 10BASE-T, 100BASE-TX, and 1000BASE-T may also be used. As the wireless network, a network using radio waves may also be used. As an example of the network using radio waves, a network compliant with IEEE802.1x (for example, at least one of wireless LAN and Bluetooth (registered trademark)) is listed. As the wireless network, a network using infrared rays may also be used. As the wireless network, a network using optical communication may also be used. In this case, the control unit 17 and the processing device 1 may also be configured to be able to transmit and receive various information via the network. In addition, the control unit 17 may also be able to transmit information such as instructions or control parameters to the processing device 1 via the network. The processing device 1 may also have a receiving device that receives information such as commands or control parameters from the control unit 17 via the above-mentioned network. The processing device 1 may also have a transmitting device (i.e., an output device that outputs information to the control unit 17) that transmits information such as instructions or control parameters to the control unit 17 via the above-mentioned network. Alternatively, a first control device that performs a part of the processing performed by the control unit 17 may be provided inside the processing device 1, while a second control device that performs another part of the processing performed by the control unit 17 may be provided outside the processing device 1.

[0104] An arithmetic model that can be constructed by machine learning by executing a computer program can also be installed in the control unit 17. As an example of an arithmetic model that can be constructed by machine learning, for example, an arithmetic model including a neural network (so-called artificial intelligence (AI: Artificial Intelligence)) can be cited. In this case, the learning of the arithmetic model can include the learning of parameters of the neural network (for example, at least one of weights and biases). The control unit 17 can also use the arithmetic model to control the operation of the processing device 1. That is, the operation of controlling the operation of the processing device 1 can also include the operation of controlling the operation of the processing device 1 using the arithmetic model. In addition, an arithmetic model constructed by offline machine learning using teacher data can also be installed in the control unit 17. In addition, the arithmetic model installed in the control unit 17 can also be updated by online machine learning on the control unit 17. Alternatively, the control unit 17 can control the operation of the processing device 1 based on or instead of the arithmetic model installed in the control unit 17, using the arithmetic model of an external device installed in the control unit 17 (that is, a device provided outside the processing device 1).

[0105] In addition, as a recording medium for recording the computer program executed by the control unit 17, at least one of optical discs such as CD-ROM, CD-R, CD-RW, floppy disk, MO, DVD-ROM, DVD-RAM, DVD-R, DVD+R, DVD-RW, DVD+RW, and Blu-ray (registered trademark), magnetic media such as magnetic tapes, magneto-optical disks, semiconductor memories such as USB memories, and any other medium capable of storing programs can also be used. The recording medium can also include a device capable of recording a computer program (for example, a general-purpose device or a dedicated device installed in a state where the computer program can be executed in at least one of software and firmware). Furthermore, each process or function included in the computer program can be implemented by a logic processing block implemented in the control unit 17 (that is, a computer) by executing the computer program, can also be implemented by hardware such as a predetermined gate array (FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit)) provided in the control unit 17, or can also be implemented in a form in which a logic processing block and a part of the hardware module that implements a part of the hardware coexist.

[0106] (1-3) Structure of the measurement system 2

[0107] Next, refer to Figure 4 The structure of the measurement system 2 will be described.Figure 4 is a block diagram showing the structure of the measurement system 2. As Figure 4 shown, the measurement system 2 has a shape measurement device 21 and a control information generation device 22.

[0108] The shape measurement device 21 can measure the three-dimensional shape of the measurement object. In the present embodiment, as described above, the measurement system 2 measures the workpiece W before the processing device 1 actually starts processing the workpiece W. Therefore, the measurement object of the shape measurement device 21 may also include the workpiece W.

[0109] The shape measurement device 21 can have any structure as long as it can measure the three-dimensional shape of the measurement object. For example, the shape measurement device 21 can also project a light pattern onto the surface of the measurement object by irradiating the surface of the measurement object with measurement light, and use a pattern projection method or a light cutting method for measuring the shape of the projected pattern to measure the three-dimensional shape of the measurement object. For example, the shape measurement device 21 can use the time-of-flight method to measure the three-dimensional shape of the measurement object. In this time-of-flight method, the measurement light is projected onto the surface of the measurement object, the time it takes for the projected measurement light to return from the measurement object to the shape measurement device 21 is calculated, and based on this time, the distance to the measurement object is measured at multiple positions on the measurement object. For example, the shape measurement device 21 can also use at least one of the Moiré topography method (specifically, the lattice irradiation method or the lattice projection method), the holographic interferometry method, the autocollimation method, the stereoscopic method, the astigmatism method, the critical angle method, and the knife-edge method to measure the three-dimensional shape of the measurement object.

[0110] In addition, the shape measurement device 21 is not limited to a device that measures the three-dimensional shape of the measurement object stored in its housing. For example, the shape measurement device 21 can also be mounted on a robot arm so as to be able to move around the measurement object.

[0111] The control information generation device 22 generates machining control information. Figure 5 An example of the structure of the control information generation device 22 that can generate machining control information is shown. As Figure 5 shown, the control information generation device 22 has an arithmetic device 221, a storage device 222, and a communication device 223. In addition, the control information generation device 22 can also have an input device 224 and a display device 225. The arithmetic device 221, the storage device 222, the communication device 223, the input device 224, and the display device 225 can also be connected via a data bus 226.

[0112] The arithmetic device 221 includes, for example, at least one of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The arithmetic device 221 reads in a computer program. For example, the arithmetic device 221 may also read in a computer program stored in the storage device 222. For example, the arithmetic device 221 may also use a recording medium reading device (not shown) to read in a computer program stored in a computer-readable and non-transitory recording medium. The arithmetic device 221 may acquire a computer program (that is, may download or read in) from a device (not shown) external to the control information generation device 22 via the communication device 223. That is, the arithmetic device 221 may acquire a computer program stored in the storage device of a device (not shown) external to the control information generation device 22 via the communication device 223 (that is, may download or read in). The arithmetic device 221 executes the read-in computer program. As a result, a logical functional block for executing the actions that the control information generation device 22 should perform (for example, actions for generating machining control information) is implemented within the arithmetic device 221. That is, the arithmetic device 221 can function as a controller for implementing a logical functional block for executing the actions that the control information generation device 22 should perform. In this case, any device (typically a computer) that has executed the computer program can function as the control information generation device 22.

[0113] Figure 5 An example of the logical functional block implemented within the arithmetic device 221 is shown. As Figure 5 shown, a model generation unit 2211 and a control information generation unit 2212 are implemented within the arithmetic device 221. The model generation unit 2211 generates a three-dimensional model representing the three-dimensional shape of the shaped object that the processing device 1 should shape by performing additional processing. That is, the model generation unit 2211 generates a three-dimensional model representing the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should shape by performing additional processing. The control information generation unit 2212 generates machining control information based on the three-dimensional model generated by the model generation unit 2211. In addition, the actions for generating the machining control information will be described in detail later.

[0114] An arithmetic model that can be constructed by machine learning through the execution of a computer program by the arithmetic device 221 may also be installed in the arithmetic device 221. As an example of an arithmetic model that can be constructed by machine learning, for example, an arithmetic model including a neural network (so-called artificial intelligence (AI: Artificial Intelligence)) can be cited. In this case, the learning of the arithmetic model may include the learning of parameters of the neural network (for example, at least one of weights and biases). The arithmetic device 221 may also use the arithmetic model to generate processing control information. In addition, an arithmetic model constructed by offline machine learning using teacher data may also be installed in the arithmetic device 221. In addition, the arithmetic model installed in the arithmetic device 221 may be updated by online machine learning on the arithmetic device 221. Alternatively, the arithmetic device 221 may control the operation of the processing device 1 based on or instead of the arithmetic model installed in the arithmetic device 221, using the arithmetic model of a device external to the arithmetic device 221 (that is, a device provided outside the control information generation device 22).

[0115] The storage device 222 can store desired data. For example, the storage device 222 may temporarily store the computer program executed by the arithmetic device 221. The storage device 222 may also temporarily store data temporarily used by the arithmetic device 221 when the arithmetic device 221 executes the computer program. The storage device 222 may also store data that the control information generation device 22 stores for a long time. In addition, the storage device 222 may include at least one of a RAM (Random Access Memory), a ROM (ReadOnly Memory), a hard disk device, a magneto-optical disk device, an SSD (Solid State Drive), and a disk array device. That is, the storage device 222 may include a non-transitory recording medium.

[0116] The communication device 223 can communicate with the processing device 1 via a communication network (not shown). In the present embodiment, the communication device 223 can send the processing control information generated by the control information generation device 22 to the processing device 1.

[0117] The input device 224 is a device that accepts the input of information from outside the control information generation device 22 with respect to the control information generation device 22. For example, the input device 224 may also include an operating device that can be operated by a user (for example, at least one of a keyboard, a mouse, and a touch panel). In this case, the input device 224 may also accept the input of the user. For example, the input device 224 may also include a reading device that can read information recorded as data on a recording medium that can be external to the control information generation device 22. For example, the input device 224 may also accept the input of information from a robot outside the control information generation device 22. For example, the input device 224 may also accept the input of information from a computer outside the control information generation device 22.

[0118] The display device 225 can display desired information as an image. That is, the display device 225 can display an image representing the information to be output.

[0119] The display device 225 may be installed on the robot. In this case, it may be that the robot equipped with the display device 225 outputs information outside the control information generation device 22. For example, it may be that the robot equipped with the display device 225 outputs information as an image.

[0120] (2) Operations of the processing system SYS

[0121] Next, the operations performed by the processing system SYS will be described. In the present embodiment, the processing system SYS may mainly use the processing device 1 to perform a processing operation for processing the workpiece W. In addition, the processing system SYS may mainly use the measurement system 2 to perform a control information generation operation for generating processing control information. Therefore, the processing operation and the control information generation operation will be described in sequence below.

[0122] (2-1) Processing operation

[0123] First, refer to Figures 6 to 7 The processing operation will be described. In particular, as an example of the processing operation, the additional processing operation performed by the processing device 1 will be described. As described above, the processing device 1 models the three-dimensional structure ST using the laser cladding method. Therefore, the processing device 1 may also model the three-dimensional structure ST by performing an existing additional processing operation based on the laser cladding method. Hereinafter, an example of the processing operation of modeling the three-dimensional structure ST using the laser cladding method will be briefly described.

[0124] The processing device 1 forms a three-dimensional structure ST. For example, it sequentially forms a plurality of layered partial structures (hereinafter referred to as "structural layers") SL arranged along the Z-axis direction. For example, the processing device 1 sequentially forms a plurality of structural layers SL obtained by slicing the three-dimensional structure ST into wafers along the Z-axis direction. As a result, a laminated structure in which a plurality of structural layers SL are laminated, that is, the three-dimensional structure ST, is formed. Hereinafter, the process of forming the three-dimensional structure ST by sequentially forming a plurality of structural layers SL layer by layer will be described.

[0125] First, refer to Figure 6 from (a) to Figure 6 (e) to describe the operation of forming each structural layer SL. The processing device 1 moves at least one of the processing head 121 and the workbench 131 under the control of the control unit 17 to set a target irradiation area EA in a desired area on the shaping surface MS corresponding to the surface of the workpiece W or the surface of the already formed structural layer SL. Then, the processing device 1 irradiates the target irradiation area EA with the processing light EL from the irradiation optical system 1211. At this time, the condensing surface that converges the processing light EL in the Z-axis direction may coincide with the shaping surface MS. Alternatively, in the Z-axis direction, the condensing surface may deviate from the shaping surface MS. As a result, as shown in Figure 6 (a), a molten pool (i.e., a pool of metal or the like melted by the processing light EL) MP is formed on the shaping surface MS irradiated with the processing light EL. Further, the processing device 1 supplies the shaping material M from the material nozzle 1212 under the control of the control unit 17. As a result, the shaping material M is supplied to the molten pool MP. The shaping material M supplied to the molten pool MP is melted by the processing light EL irradiated to the molten pool MP. Alternatively, the shaping material M supplied from the material nozzle 1212 may be melted by the processing light EL before reaching the molten pool MP, and the melted shaping material M is supplied to the molten pool MP. Then, when the processing light EL is not irradiated to the molten pool MP along with the movement of at least one of the processing head 121 and the workbench 131, the shaping material M melted in the molten pool MP is cooled and solidified (i.e., solidified). As a result, as shown in Figure 6 (c), a shaped object made of the solidified shaping material M accumulates on the shaping surface MS.

[0126] As shown in Figure 6As shown in (d) of [Figure ID], the processing apparatus 1 repeatedly performs a series of shaping processes including the formation of the molten pool MP by irradiating the processing light EL while moving the processing head 121 relative to the shaping surface MS in at least one of the X-axis direction and the Y-axis direction. At this time, the processing apparatus 1 irradiates the processing light EL to the area on the shaping surface MS where the shaped object is desired to be formed, and on the other hand, does not irradiate the processing light EL to the area on the shaping surface MS where the shaped object is not desired to be formed. That is, the processing apparatus 1 irradiates the processing light EL to the shaping surface MS at a timing corresponding to the distribution pattern of the area where the shaped object is desired to be formed while moving the target irradiation area EA on the shaping surface MS along a predetermined movement path.

[0127] The movement path of the target irradiation area EA on the shaping surface MS can also be referred to as a processing path (in other words, a tool path). The above-mentioned processing control information includes information related to this processing path as processing path information. Therefore, the control information generation device 22 can also generate processing control information including the processing path information. The processing apparatus 1 irradiates the processing light EL to the shaping surface MS at a timing corresponding to the distribution pattern of the area where the shaped object is desired to be formed while moving the target irradiation area EA on the shaping surface MS along a predetermined movement path according to the processing control information.

[0128] As a result, the molten pool MP also moves on the shaping surface MS along a movement path corresponding to the movement path of the target irradiation area EA. Specifically, the molten pool MP is sequentially formed on the part irradiated with the processing light EL in the area along the movement path of the target irradiation area EA on the shaping surface MS. As a result, as Figure 6 shown in (e) of [Figure ID], a structural layer SL corresponding to the shaped object, which is an aggregate of the shaped material M after melting and solidification, is formed on the shaping surface MS. That is, a structural layer SL corresponding to the aggregate of the shaped objects shaped on the shaping surface MS in a pattern corresponding to the movement path of the molten pool MP is formed (that is, a structural layer SL having a shape corresponding to the movement path of the molten pool MP in a top view). In addition, when the target irradiation area EA is set in the area where the shaped object is not desired to be formed, the processing apparatus 1 may irradiate the processing light EL to the target irradiation area EA and stop the supply of the shaping material M. In addition, when the target irradiation area EA is set in the area where the shaped object is not desired to be formed, the processing apparatus 1 may supply the shaping material M to the target irradiation area EA and irradiate the processing light EL with an intensity that cannot form the molten pool MP to the target irradiation area EA.

[0129] The processing apparatus 1 repeatedly performs an operation for forming such a structural layer SL under the control of the control unit 17 according to the processing control information. Specifically, first, the processing apparatus 1 performs an operation for forming the first structural layer SL#1 on the forming surface MS corresponding to the surface of the workpiece W based on the processing control information (for example, information related to the processing route for forming the structural layer SL#1). As a result, as shown in (a) of Figure 7 , the structural layer SL#1 is formed on the forming surface MS. Then, the processing apparatus 1 sets the surface (i.e., the upper surface) of the structural layer SL#1 as the new forming surface MS, and then forms the second structural layer SL#2 on the new forming surface MS. In order to form the structural layer SL#2, the control unit 17 first controls at least one of the head drive system 122 and the worktable drive system 132 so that the processing head 121 moves along the Z-axis with respect to the worktable 131. Specifically, the control unit 17 controls at least one of the head drive system 122 and the worktable drive system 132 to move the processing head 121 toward the +Z side and / or move the worktable 131 toward the -Z side to set the target irradiation area EA on the surface of the structural layer SL#1 (i.e., the new forming surface MS). Then, the processing apparatus 1, under the control of the control unit 17, forms the structural layer SL#2 on the structural layer SL#1 by the same operation as the operation for forming the structural layer SL#1, according to the processing control information (for example, the processing route information for forming the structural layer SL#2). As a result, as shown in (b) of Figure 7 , the structural layer SL#2 is formed. Thereafter, the same operation is repeated until all the structural layers SL that make up the three-dimensional structure ST to be formed on the workpiece W are formed. As a result, as shown in (c) of Figure 7 , the three-dimensional structure ST is formed by a stacked structure in which a plurality of structural layers SL are stacked.

[0130] (2-2) Control information generation operation

[0131] Next, the control information generation operation will be described.

[0132] (2-2-1) Overall flow of the control information generation operation

[0133] First, refer to Figure 8 to describe the overall flow of the control information generation operation. Figure 8 is a flowchart showing the overall flow of the control information generation operation.

[0134] As Figure 8As shown, first, the shape measurement device 21 measures the three-dimensional shape of the workpiece W (step S1). As a result, the shape measurement device 21 generates an object model OM (step S2). The object model OM is a three-dimensional model representing the three-dimensional shape of the workpiece W. Specifically, the object model OM is a three-dimensional model representing the actual three-dimensional shape of the workpiece W. That is, the object model OM is a three-dimensional model having the same three-dimensional shape as the actual three-dimensional shape of the workpiece W.

[0135] In the present embodiment, as the object model OM, a three-dimensional model including a plurality of unit regions is used. In other words, as the object model OM, a three-dimensional model that can be subdivided into a plurality of unit regions is used. For example, as the object model OM, a three-dimensional model including a plurality of points Pom (refer to Figure 11 ) can also be used as an example of the plurality of unit regions. That is, as the object model OM, a three-dimensional model can also be used as follows: a plurality of points Pom (i.e., a point group) are used to represent the three-dimensional shape of the workpiece W. In addition, a three-dimensional model using a point group can also be called a point group model. Or, for example, as the object model OM, a three-dimensional model including a plurality of meshes as an example of the plurality of unit regions can also be used. That is, as the object model OM, a three-dimensional model can also be used as follows: a plurality of meshes are used to represent the three-dimensional shape of the workpiece W. Each mesh has a polygonal shape. In addition, a three-dimensional model including a plurality of meshes can also be called a mesh model.

[0136] In addition, in the following description, for the sake of convenience of explanation, an example in which a three-dimensional model including a plurality of points Pom is used as the object model OM will be described. In this case, it can also be considered that the plurality of points Pom respectively correspond to a part of the object model OM. However, even in the case where a three-dimensional model including a plurality of arbitrary unit regions different from the plurality of points Pom is used as the object model OM, the processing system SYS can perform the control information generation operation described below.

[0137] The shape measurement device 21 outputs the generated object model OM to the control information generation device 22. Alternatively, the shape measurement device 21 may not generate the object model OM and output the measurement result of the three-dimensional shape of the workpiece W to the control information generation device 22. In this case, the control information generation device 22 may also generate the object model OM based on the measurement result of the three-dimensional shape of the workpiece W by the shape measurement device 21.

[0138] The control information generation device 22 (in particular, the model generation unit 2211) acquires the target model TM in parallel with the operations from step S1 to step S2, or acquires the target model TM independently of the operations from step S1 to step S2 (step S3). The target model TM is a three-dimensional model representing the target shape of the machined workpiece W. For example, a CAD (Computer Aided Design) model of the workpiece W having the target shape may be used as the target model TM. For example, a three-dimensional model obtained by actually measuring the three-dimensional shape of the workpiece W having the target shape may be used as the target model TM. In this case, a three-dimensional model represented by a file representing CAD data may be used as the target model TM. As an example of a file representing CAD data, at least one of a file having an STL extension, a file having a DWF extension, a file having a DXF extension, a file having a DWG extension, and a file having an STP extension is listed.

[0139] In the present embodiment, as the target model TM, a three-dimensional model including a plurality of unit regions is used. In other words, as the target model TM, a three-dimensional model that can be subdivided into a plurality of unit regions is used. For example, as the target model TM, a three-dimensional model including a plurality of points Ptm (see Figure 11 ) as an example of a plurality of unit regions may also be used. That is, as the target model TM, a three-dimensional model that uses a plurality of points Ptm (i.e., a point cloud) to represent the target shape of the workpiece W may also be used. Or, for example, as the target model TM, a three-dimensional model including a plurality of meshes as an example of a plurality of unit regions may also be used. That is, as the target model TM, a three-dimensional model that uses a plurality of meshes to represent the target shape of the workpiece W may also be used. Each mesh has a polygonal shape. In addition, a three-dimensional model including a plurality of meshes may also be referred to as a mesh model.

[0140] In addition, in the following description, for the sake of convenience of explanation, an example in which a three-dimensional model including a plurality of points Ptm (see Figure 11 ) is used as the target model TM will be described. In this case, it may also be considered that the plurality of points Ptm respectively correspond to a part of the target model TM. However, even when a three-dimensional model including a plurality of arbitrary unit regions different from the plurality of points Ptm is used as the target model TM, the machining system SYS can perform the control information generation operation described below.

[0141] Figure 9 Examples of the object model OM and the target model TM are shown. As Figure 9As shown, typically, the target shape of the workpiece W represented by the target model TM is different from the actual three-dimensional shape of the workpiece W represented by the object model OM. For example, as described above, when a defective item to be repaired is used as the workpiece W, as Figure 9 shown, the object model OM represents the three-dimensional shape of the workpiece W with a part missing due to the use of the workpiece W. On the other hand, as Figure 9 shown, the target model TM represents the three-dimensional shape of the workpiece W without defects. That is, the object model OM represents the three-dimensional shape of the actually used workpiece W, while the target model TM represents the three-dimensional shape of the workpiece W before actual use. As an example, when the workpiece W is a turbine blade with part of it worn, the object model OM represents the three-dimensional shape of the turbine blade with part of it worn, while the target model TM represents the three-dimensional shape of the unworn turbine blade.

[0142] In addition, the "use of the workpiece W" in this embodiment can also include the case of using the workpiece W by a usage method corresponding to the use of the workpiece W. When the workpiece W is used as a component of a product, the "use of the workpiece W" can also include the case of using the product containing the workpiece W by a usage method corresponding to the use of the product. For example, when the workpiece W includes a turbine blade, the use of the turbine blade can also include the case of using the turbine containing the turbine blade by a usage method corresponding to the use of the turbine.

[0143] In addition, if the scenario where a part of the workpiece W is damaged (e.g., worn) along with the use of the workpiece W as described above is an example of the scenario of using the processing system SYS of this embodiment, then the "use of the workpiece W" can also include the use of the workpiece W that causes damage to a part of the workpiece W. For example, the "use of the workpiece W" can also include the long-term use of the workpiece W to such an extent that it causes damage to a part of the workpiece W. Therefore, the actually used workpiece W can also include the workpiece W used to the extent that a part of it is damaged. On the other hand, the workpiece W before actual use can also include the workpiece W that has been used but not yet used to the extent that it causes damage to a part of the workpiece W. For example, the workpiece W before actual use can also include the short-term use of the workpiece W (e.g., trial operation of the workpiece W or the product containing the workpiece W) that does not cause damage to a part of the workpiece W. Of course, as the literal meaning indicates, the workpiece W before actual use can also include the workpiece W that has not been used. For example, the workpiece W before actual use can also include the workpiece W before leaving the factory as a product or a component. For example, the workpiece W before actual use can also include the workpiece W in the design stage.

[0144] Again at Figure 8Thereafter, the model generation unit 2211 generates a three-dimensional model representing the three-dimensional shape of the modeled object that the processing device 1 should model by performing additional processing based on the object model OM generated in step S2 and the target model TM acquired in step S3 (step S4). That is, the model generation unit 2211 generates a three-dimensional model representing the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should model (step S4). In other words, the model generation unit 2211 generates a three-dimensional model representing the three-dimensional shape of the part (modeled object) added to the workpiece W in order to process the workpiece W so that its shape becomes the target shape (step S4).

[0145] Specifically, as schematically shown in Figure 10 the target model TM represents the target shape of the workpiece W, and the object model OM represents the actual three-dimensional shape of the workpiece W. In this case, as Figure 10 shown, the difference between the target model TM and the object model OM corresponds to the three-dimensional model representing the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should model. Therefore, as Figure 10 shown, the model generation unit 2211 can also generate a three-dimensional model corresponding to the difference between the target model TM and the object model OM as the three-dimensional model representing the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should model. In the following description, the three-dimensional model corresponding to the difference between the target model TM and the object model OM is referred to as the difference model DM. Typically, the difference model DM is a three-dimensional model corresponding to a part of the target model TM.

[0146] Again in Figure 8 thereafter, the control information generation device 22 (specifically, the control information generation unit 2212) generates processing control information based on the difference model DM generated in step S4 (step S5). For example, the control information generation unit 2212 can also generate a plurality of slice data corresponding to the plurality of construction layers SL constituting the three-dimensional structure ST by performing a slicing process in which the difference model DM is divided into a plurality of layered models at a layer stacking interval corresponding to the thickness of the construction layer SL. Then, the control information generation unit 2212 can also generate a plurality of processing control information for modeling the plurality of construction layers SL based on the plurality of slice data.

[0147] (2-2-2) Difference model generation operation

[0148] Next, in Figure 8The action for generating the differential model DM in step S4, i.e., the differential model generation action, will be further described. Additionally, as described above, for the sake of convenience in the following description, the differential model generation action performed in the case of using a three-dimensional model containing multiple points Pom (point group) as the object model OM and using a three-dimensional model containing multiple points Ptm (point group) as the target model TM will be described.

[0149] (2-2-2-1) Outline of the differential model generation action

[0150] First, refer to Figures 11 to 13 The outline of the differential model generation action will be described.

[0151] Figure 11 One point Pom included in the object model OM and one point Ptm included in the target model TM are schematically shown. To generate the differential model DM, first, as Figure 11 shown, the model generation unit 2211 aligns the positions of the object model OM and the target model TM in a prescribed coordinate space. For example, the model generation unit 2211 may align the positions of the object model OM and the target model TM in such a way that the reference part of the object model OM and the reference part of the target model TM are located at the same position in the prescribed coordinate space. In other words, the model generation unit 2211 may align the positions of the object model OM and the target model TM in such a way that the position of the reference part of the object model OM in the prescribed coordinate space is the same as the position of the reference part of the target model TM in the prescribed coordinate space. For example, the model generation unit 2211 may align the positions of the object model OM and the target model TM in such a way that the reference part of the object model OM and the reference part of the target model TM are located at the same position in the prescribed coordinate space, whereby the object model OM and the target model TM at least partially overlap.

[0152] The reference parts of the object model OM and the target model TM may also be model parts corresponding to the reference part of the workpiece W. The reference part of the workpiece W may also include parts that are not easily worn due to the use of the workpiece W. The reference part of the workpiece W may also include parts that are not easily deformed due to the use of the workpiece W. For example, in the case where the workpiece W is a turbine blade, the reference part of the workpiece W may also include at least a part of the shank of the turbine blade.

[0153] After aligning the positions of the object model OM and the target model TM, the model generation unit 2211 extracts (in other words, calculates or obtains) multiple points Ptm that satisfy a specified distance condition from among the multiple points Ptm included in the target model TM as multiple extraction points Pext. The specified distance condition may also include the following condition: the distance D1 (hereinafter referred to as "inter-model distance D1") between a point Ptm of the target model TM and the closest point Pom of the object model OM to that point Ptm is equal to or greater than a specified inter-model distance threshold TH_M. When the part of the object model OM that is closest to a point Ptm of the target model TM on the surface of the object model OM is taken as the point Pom, the inter-model distance D1 may also be the distance between a point Ptm of the target model TM and that point Pom. In addition, in the following description, for the sake of convenience of explanation, a point Pom of the object model OM that is closest to a point Ptm of the target model TM is referred to as "closest point Pom_closest". In this case, when the inter-model distance D1 between a point Ptm and the closest point Pom_closest is equal to or greater than the inter-model distance threshold TH_M, the model generation unit 2211 extracts that point Ptm as a point Ptm that satisfies the distance condition (i.e., as an extraction point Pext). On the other hand, when the inter-model distance D1 between a point Ptm and the closest point Pom_closest is not equal to or greater than the inter-model distance threshold TH_M, the model generation unit 2211 may not extract that point Ptm as a point Ptm that satisfies the distance condition (i.e., as an extraction point Pext).

[0154] In addition, since the inter-model distance threshold TH_M is a threshold for comparing with the distance parameter of the inter-model distance D1, it may also be simply referred to as a distance threshold.

[0155] When the target model TM includes N points Ptm (specifically, points Ptm#1 to Ptm#N), the model generation unit 2211 repeatedly performs the action of determining whether the inter-model distance D1#k between the point Ptm#k (where k is a variable representing an integer from 1 to N) and the closest point Pom_closest#k of the object model OM to that point Ptm#k is equal to or greater than the inter-model distance threshold TH_M while changing the variable k from 1 to N. As a result, as schematically shown in Figure 12 shown, multiple points Ptm that satisfy the distance condition are extracted as multiple extraction points Pext.

[0156] As Figure 12 shown, typically, the multiple extraction points Pext that satisfy the distance condition correspond to the difference between the object model OM and the target model TM. As Figure 12As shown, a plurality of extraction points Pext represent the three-dimensional shape of the difference between the object model OM and the target model TM. Therefore, the model generation unit 2211 can also generate a difference model DM based on the plurality of extraction points Pext. For example, the model generation unit 2211 can also generate a three-dimensional model including the plurality of extraction points Pext as the difference model DM. For example, the model generation unit 2211 can also generate a three-dimensional model having the three-dimensional shape represented by the plurality of extraction points Pext as the difference model DM.

[0157] However, as Figure 12 shown, the plurality of extraction points Pext may include extraction points Pext that do not represent the difference between the object model OM and the target model TM. That is, the plurality of extraction points Pext may include extraction points Pext that become noise points that should not be used to generate the difference model DM. Therefore, in the present embodiment, after extracting the plurality of extraction points Pext, the model generation unit 2211 performs clustering of the plurality of extraction points Pext. Specifically, as shown in Figure 13 showing the plurality of extraction points Pext, the model generation unit 2211 performs clustering of the plurality of extraction points Pext in such a way that two extraction points Pext that satisfy the condition that the distance D2 between the two extraction points Pext is below a specified clustering threshold TH_C are classified into the same point group cluster PGC. In this case, it can also be regarded that the clustering of the plurality of extraction points Pext is a process of applying a connected region labeling process in an image to a point group, and is a process of classifying each extraction point Pext into a cluster on the basis of connecting two extraction points Pext considered to be at a distance D2 below the clustering threshold TH_C. In addition, the point group cluster PGC can also be referred to as a clustering region. However, the model generation unit 2211 does not necessarily perform clustering of the plurality of extraction points Pext.

[0158] The distance D2 between two extraction points Pext can also be a distance on the order of micrometers. The distance D2 between two extraction points Pext can also be a parameter that can be adjusted on the order of micrometers. For example, the distance D2 between two extraction points Pext can also be a distance of about several hundred micrometers. Or, the distance D2 between two extraction points Pext can also be a distance from submillimeter to centimeter level. The distance D2 between two extraction points Pext can also be a parameter that can be adjusted from submillimeter to centimeter level. Or, the distance D2 between two extraction points Pext can also be a distance that can be expressed using the number of pixels of an image.

[0159] In addition, since the clustering threshold TH_C is a threshold for comparing with the distance parameter of the distance D2, it can also be simply referred to as a distance threshold. That is, the clustering threshold TH_C can also be regarded as an example of a distance threshold for comparing with a distance parameter together with the above-mentioned inter-model distance threshold TH_M.

[0160] For example, when the distance D2 between the first extraction point Pext and the second extraction point Pext is equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the first extraction point Pext and the second extraction point Pext in such a way that they are classified into the first point group cluster PGC. Further, for example, when the distance D2 between the third extraction point Pext and at least one of the first extraction point Pext and the second extraction point Pext is equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the third extraction point Pext in such a way that it is classified into the first point group cluster PGC that classifies the first extraction point Pext and the second extraction point Pext. On the other hand, for example, when the distance D2 between the third extraction point Pext and each of the first extraction point Pext and the second extraction point Pext is not equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the third extraction point Pext in such a way that it is classified into a second point group cluster PGC different from the first point group cluster PGC that classifies the first extraction point Pext and the second extraction point Pext.

[0161] Alternatively, for example, when the distance D2 between the first extraction point Pext and the second extraction point Pext is not equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the first extraction point Pext and the second extraction point Pext in such a way that the first extraction point Pext is classified into the first point group cluster PGC and the second extraction point Pext is classified into a second point group cluster PGC different from the first point group cluster PGC. Further, for example, when the distance D2 between the third extraction point Pext and the first extraction point Pext is equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the third extraction point Pext in such a way that it is classified into the first point group cluster PGC that classifies the first extraction point Pext. On the other hand, for example, when the distance D2 between the third extraction point Pext and the second extraction point Pext is equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the third extraction point Pext in such a way that it is classified into the second point group cluster PGC that classifies the second extraction point Pext. On the other hand, for example, when the distance D2 between the third extraction point Pext and each of the first extraction point Pext and the second extraction point Pext is not equal to or less than the clustering threshold TH_C, the model generation unit 2211 clusters the third extraction point Pext in such a way that it is classified into a third point group cluster PGC different from the first point group cluster PGC and the second point group cluster PGC that respectively classify the first extraction point Pext and the second extraction point Pext.

[0162] As a result, as Figure 13 shown, the model generation unit 2211 generates (in other words, calculates or obtains) at least one point group cluster PGC that classifies at least one extraction point Pext. In Figure 13 the example shown, the model generation unit 2211 clusters a plurality of extraction points Pext in such a way as to generate three point group clusters PGC (specifically, point group cluster PGC#1, point group cluster PGC#2, and point group cluster PGC#3).

[0163] Then, the model generation unit 2211 generates a differential model DM based on at least one point group cluster PGC. In addition, regarding the action of generating the differential model DM based on at least one point group cluster PGC, it will be described in detail later with reference to Figure 14 and thus the description here is omitted.

[0164] (2-2-2) Flow of the differential model generation action

[0165] Next, with reference to Figure 14 the flow of the differential model generation action will be described. Figure 14 is a flowchart showing the flow of the differential model generation action.

[0166] As Figure 14 shown, the model generation unit 2211 first extracts a plurality of points Ptm that satisfy a specified distance condition from among the plurality of points Ptm included in the target model TM as a plurality of extraction points Pext (step S410). Specifically, the model generation unit 2211 extracts a point Ptm that satisfies the condition that the inter-model distance D1 between the point Ptm of the target model TM and the closest point Pom_closest is equal to or greater than the inter-model distance threshold TH_M as the extraction point Pext. In addition, in step S410, the model generation unit 2211 may also use the default inter-model distance threshold TH_M to extract a plurality of extraction points Pext.

[0167] Then, the model generation unit 2211 controls the display device 25 to display an extraction point display image 51 for displaying the plurality of extraction points Pext extracted by the model generation unit 2211 (step S411). As a result, the display device 25 displays the extraction point display image 51 under the control of the model generation unit 2211 (step S411).

[0168] Figure 15 shows an example of the extraction point display image 51. As Figure 15 shown, the extraction point display image 51 may also include a display image 511 for displaying the plurality of extraction points Pext extracted by the model generation unit 2211. In Figure 15In the example shown, in the display image 511, a plurality of extraction points Pext are three-dimensionally displayed within a prescribed coordinate space. However, as a display method for the plurality of extraction points Pext within the display image 511, a display method different from that Figure 15 shown may also be used.

[0169] In the display image 511, the plurality of extraction points Pext may also be displayed together with at least one of the target model TM and the object model OM. For example, in the display image 511, the plurality of extraction points Pext may be displayed in a state where they are aligned with the target model TM. In other words, in the display image 511, the plurality of extraction points Pext may be displayed in a state where they are associated with the target model TM. For example, in the display image 511, the plurality of extraction points Pext may be displayed in a state where they are aligned with the object model OM. In other words, in the display image 511, the plurality of extraction points Pext may be displayed in a state where they are associated with the object model OM.

[0170] In Figure 15 the example shown, in the display image 511, the plurality of extraction points Pext are displayed together with both the target model TM and the object model OM. In this case, the target model TM and the object model OM may also be displayed in a state where the target model TM and the object model OM are aligned with each other. For example, as described above, regarding the target model TM and the object model OM, it may also be that the reference part of the target model TM and the reference part of the object model OM are displayed at the same position within a prescribed coordinate space. For example, as described above, the target model TM and the object model OM may also be displayed in such a way that the reference part of the target model TM and the reference part of the object model OM are located at the same position within a prescribed coordinate space, whereby the target model TM and the object model OM at least partially overlap.

[0171] The model generation unit 2211 may also change the display mode of a set of display objects including a plurality of extraction points Pext displayed in the display image 511 according to the input of the user using the input device 24. For example, the model generation unit 2211 may also translate the display object in parallel within a specified coordinate space defined within the display image 511. For example, the model generation unit 2211 may also rotate the display object within a specified coordinate space defined within the display image 511. For example, the model generation unit 2211 may also enlarge or reduce the display object within a specified coordinate space defined within the display image 511. In addition, when a plurality of extraction points Pext are displayed in the display image 511 together with at least one of the target model TM and the object model OM, a set of display objects including the plurality of extraction points Pext may also include at least one of the target model TM and the object model OM.

[0172] In the present embodiment, the extraction point display image 51 may further include a display image 512 including an operation object 5121 that can be operated by the user to adjust the inter-model distance threshold TH_M for extracting the extraction point Pext. In this case, the user may also adjust (in other words, set or change) the inter-model distance threshold TH_M by operating the operation object 5121 using the input device 24.

[0173] In Figure 15 the example shown, a slider (in other words, a scroll bar) is used as the operation object 5121. In this case, the user may also adjust the inter-model distance threshold TH_M in such a way that the inter-model distance threshold TH_M becomes larger by performing an operation of moving the slider in the first direction (for example, Figure 15 the upward direction in Figure 15 ). On the other hand, the user may also adjust the inter-model distance threshold TH_M in such a way that the inter-model distance threshold TH_M becomes smaller by performing an operation of moving the slider in the second direction opposite to the first direction (for example,

[0174] the downward direction in

[0175] In addition, as the operation object 5121, a display object different from the slider may also be used. For example, as the operation object 5121, a display object that can specify one candidate value to be set as the inter-model distance threshold TH_M from a plurality of candidate values for the inter-model distance threshold TH_M may also be used. As an example of such a display object, at least one of a combo box, a drop-down list, and a radio button is listed.The input device 224 provided for the user to operate on the operation object 5121 can also be configured to accept the input of the user for operating on the operation object 5121. As an example, the input device 224 can also be capable of accepting at least one of a swipe input, a zoom input, and a rotation input. In this case, the user can also adjust the inter-model distance threshold TH_M by performing at least one of a swipe input, a zoom input, and a rotation input.

[0176] The user can also adjust the inter-model distance threshold TH_M at the micron level. The user can also adjust the inter-model distance threshold TH_M from sub-millimeter to centimeter level. The user can also use the number of pixels of the image to adjust the inter-model distance threshold TH_M.

[0177] Again in Figure 14 In step S412, the model generation unit 2211 determines whether the user has adjusted the inter-model distance threshold TH_M using the input device 24. That is, the model generation unit 2211 determines whether the user has performed an operation for adjusting the inter-model distance threshold TH_M using the input device 24 (step S412). In other words, the model generation unit 2211 determines whether the input device 24 has accepted the input of the user for adjusting the inter-model distance threshold TH_M (step S412).

[0178] If the result of the determination in step S412 is that it is determined that the user has adjusted the inter-model distance threshold TH_M (step S412: Yes), the model generation unit 2211 uses the inter-model distance threshold TH_M adjusted by the user to extract a plurality of extraction points Pext (step S413). That is, the model generation unit 2211 extracts a plurality of extraction points Pext again using the inter-model distance threshold TH_M adjusted by the user. Specifically, the model generation unit 2211 extracts the point Ptm of the target model TM that satisfies the condition that the inter-model distance D1 between the point Ptm and the nearest point Pom_closest is greater than or equal to the inter-model distance threshold TH_M adjusted by the user as the extraction point Pext (step S413). In other words, the model generation unit 2211 updates the plurality of extraction points Pext according to the operation of the user on the operation object 5121 (step S413).

[0179] Then, the model generation unit 2211 controls the display device 25 to display an extraction point display image 51 for displaying the plurality of extraction points Pext extracted in step S413 (step S414). As a result, the display device 25, under the control of the model generation unit 2211, displays an extraction point display image 51 for displaying the plurality of extraction points Pext extracted in step S413 (step S414).

[0180] In this case, it can also be considered that the model generation unit 2211 controls the display device 25 to update the extraction point display image 51. Specifically, it can also be considered that the model generation unit 2211 controls the display device 25 to update the extraction point display image 51 according to the multiple extraction points Pext extracted in step S413. In step S413, the multiple extraction points Pext are extracted according to the user's operation on the operation object 5121. Therefore, it can also be considered that the model generation unit 2211 controls the display device 25 to update the extraction point display image 51 according to the user's operation on the operation object 5121.

[0181] Then, each time the model generation unit 2211 determines that the user has adjusted the model distance threshold TH_M (step S412: Yes), it extracts multiple extraction points Pext (step S413) and updates the extraction point display image 51 (step S414). In addition, when the adjustment speed of the user on the model distance threshold TH_M is above a specified threshold, the model generation unit 2211 may not update the extraction point display image 51 each time the user adjusts the model distance threshold TH_M.

[0182] The model generation unit 2211 may also reflect the adjustment result of the user on the model distance threshold TH_M in the extraction point display image 51 in real time. That is, the model generation unit 2211 may, when the user has adjusted the model distance threshold TH_M, extract multiple extraction points Pext in real time and update the extraction point display image 51. As an example, when performing the operations from step S412 to step S414 above at a relatively fast cycle, the model generation unit 2211 may also reflect the adjustment result of the user on the model distance threshold TH_M in the extraction point display image 51 in real time. However, the model generation unit 2211 may also not reflect the adjustment result of the user on the model distance threshold TH_M in the extraction point display image 51 in real time.

[0183] Thus, in the present embodiment, the user can appropriately adjust the inter-model distance threshold TH_M using the operation object 5121 included in the extraction point display image 51. For example, when the plurality of extraction points Pext displayed on the extraction point display image 51 are inappropriate, the user can also adjust the inter-model distance threshold TH_M to extract appropriate extraction points Pext. At this time, the plurality of extracted extraction points Pext are displayed on the extraction point display image 51, so the user can easily confirm the plurality of extraction points Pext displayed on the extraction point display image 51. As a result, compared with the case where the plurality of extracted extraction points Pext are not displayed on the extraction point display image 51, the user can more easily adjust the inter-model distance threshold TH_M to extract appropriate extraction points Pext. In addition, compared with the case where the inter-model distance threshold TH_M is fixed, when the inter-model distance threshold TH_M can be adjusted, the model generation unit 2211 can extract more appropriate extraction points Pext. As a result, the model generation unit 2211 can generate a more appropriate differential model DM based on the appropriate extraction points Pext extracted in this way.

[0184] The model generation unit 2211 may also change the display mode of each extraction point Pext according to the inter-model distance D1 between each extraction point Pext and the closest point Pom_closest to each extraction point Pext. For example, the longer the inter-model distance D1 between each extraction point Pext and the closest point Pom_closest, the higher the possibility that each extraction point Pext is a point Ptm that appropriately represents the difference between the target model TM and the object model OM. This is because such an extraction point Pext is a point Ptm of the target model TM located relatively far from the object model OM, and thus is naturally regarded as a point Ptm that appropriately represents the difference between the target model TM and the object model OM. On the contrary, the shorter the inter-model distance D1 between each extraction point Pext and the closest point Pom_closest, the higher the possibility that each extraction point Pext is a noise point that does not appropriately represent the difference between the target model TM and the object model OM. Therefore, the model generation unit 2211 may also change the display mode of each extraction point Pext so that the display mode of the extraction point Pext whose inter-model distance D1 exceeds the specified noise threshold is different from the display mode of the extraction point Pext whose inter-model distance D1 is lower than the specified noise threshold. That is, the model generation unit 2211 may also change the display mode of each extraction point Pext so that the display mode of the extraction point Pext with a relatively low possibility of being noise is different from the display mode of the extraction point Pext with a relatively high possibility of being noise. In this case, the user may also adjust the inter-model distance threshold TH_M by confirming the extraction point display image 51 to reduce the number of extraction points Pext with a relatively high possibility of being noise. As a result, the model generation unit 2211 can appropriately extract the extraction point Pext that appropriately represents the difference between the target model TM and the object model OM. Therefore, the model generation unit 2211 can generate a more appropriate difference model DM based on such appropriately extracted extraction points Pext. In addition, as an example of the display mode, the display color is listed.

[0185] The model generation unit 2211 may also change all display modes of a plurality of extraction points Pext according to the inter-model distance D1. Alternatively, the model generation unit 2211 may change the display mode of a part of the plurality of extraction points Pext according to the inter-model distance D1, and not change the display mode of another part of the plurality of extraction points Pext according to the inter-model distance D1. For example, the model generation unit 2211 may change the display mode of a part of the plurality of extraction points Pext according to the inter-model distance D1, and display another part of the plurality of extraction points Pext in a specified color (for example, gray). Alternatively, the model generation unit 2211 may change the display mode of a part of the plurality of extraction points Pext according to the inter-model distance D1, and not display another part of the plurality of extraction points Pext.

[0186] The model generation unit 2211 may also display an extraction point display image 51 including a display object for displaying a plurality of points Ptm included in the target model TM. In this case, the model generation unit 2211 may also change the display mode of each point Ptm according to the inter-model distance D1 in the same way as in the case of changing the display mode of each extraction point Pext according to the inter-model distance D1.

[0187] In addition to changing the display mode of each extraction point Pext according to the inter-model distance D1, the model generation unit 2211 may also display an extraction point display image 51 including a histogram representing the appearance frequency of the extraction points Pext based on the inter-model distance D1. In addition to changing the display mode of each point Ptm according to the inter-model distance D1, the model generation unit 2211 may also display an extraction point display image 51 including a histogram representing the appearance frequency of the points Ptm based on the inter-model distance D1.

[0188] On the other hand, when the result of the determination in step S412 is that it is determined that the user has not adjusted the inter-model distance threshold TH_M (step S412: No), the model generation unit 2211 generates at least one point group cluster PGC by clustering a plurality of extraction points Pext (step S420). Specifically, the model generation unit 2211 classifies two extraction points Pext that satisfy the condition that the distance D2 between the two extraction points Pext is equal to or less than a specified clustering threshold TH_C into the same point group cluster PGC, thereby generating at least one point group cluster PGC. In addition, in step S420, the model generation unit 2211 may also use the default clustering threshold TH_C to generate the point group cluster PGC.

[0189] Then, the model generation unit 2211 controls the display device 25 to display a point group cluster display image 52 for displaying at least one point group cluster PGC generated by the model generation unit 2211 (step S421). As a result, the display device 25 displays the point group cluster display image 52 under the control of the model generation unit 2211 (step S421).

[0190] The model generation unit 2211 may also control the display device 25 to display the point cloud clustering display image 52 instead of the extracted point display image 51. That is, the model generation unit 2211 may also control the display device 25 to display either the extracted point display image 51 or the point cloud clustering display image 52, without displaying the other of the extracted point display image 51 and the point cloud clustering display image 52. In this case, it may also be regarded that the display device 25 alternatively switches the image displayed by the display device 25 between the extracted point display image 51 and the point cloud clustering display image 52. The same applies to the case where the extracted point display image 51 is displayed in the above step S410. The model generation unit 2211 may also control the display device 25 to display the extracted point display image 51 instead of the point cloud clustering display image 52.

[0191] Alternatively, the model generation unit 2211 may also control the display device 25 to display the point cloud clustering display image 52 in addition to the extracted point display image 51. That is, the model generation unit 2211 may also control the display device 25 to display both the extracted point display image 51 and the point cloud clustering display image 52. In this case, the display device 25 may also display both the extracted point display image 51 and the point cloud clustering display image 52 at the same time. That is, the display device 25 may also display the extracted point display image 51 and the point cloud clustering display image 52 in one display screen at the same time. The same applies to the case where the extracted point display image 51 is displayed in the above step S410. The model generation unit 2211 may also control the display device 25 to display both the extracted point display image 51 and the point cloud clustering display image 52.

[0192] Figure 16 An example of the point cloud clustering display image 52 is shown. As Figure 16 shown, the point cloud clustering display image 52 may also include a display image 521 for displaying at least one point cloud clustering PGC generated by the model generation unit 2211. In Figure 16 the example shown, in the display image 521, at least one point cloud clustering PGC is three-dimensionally displayed in a prescribed coordinate space. However, as a display method of at least one point cloud clustering PGC in the display image 521, a display method different from the Figure 16 display method shown may also be used.

[0193] In the display image 521, it is also possible to display at least one point group cluster PGC together with at least one of the target model TM and the object model OM. For example, in the display image 521, it is also possible to display at least one point group cluster PGC in a state where the at least one point group cluster PGC is position-aligned with the target model TM. In other words, in the display image 521, it is also possible to display at least one point group cluster PGC in a state where the at least one point group cluster PGC is associated with the target model TM. For example, in the display image 521, it is also possible to display at least one point group cluster PGC in a state where the at least one point group cluster PGC is position-aligned with the object model OM. In other words, in the display image 521, it is also possible to display at least one point group cluster PGC in a state where the at least one point group cluster PGC is associated with the object model OM.

[0194] In Figure 16 In the example shown, in the display image 521, at least one point group cluster PGC is displayed together with both the target model TM and the object model OM. In this case, the target model TM and the object model OM may also be displayed in a state where the target model TM and the object model OM are position-aligned with each other. For example, as described above, the target model TM and the object model OM may also be displayed in such a manner that the reference part of the target model TM and the reference part of the object model OM are located at the same position in a prescribed coordinate space. For example, as described above, the target model TM and the object model OM may also be displayed in such a manner that the reference part of the target model TM and the reference part of the object model OM are located at the same position in a prescribed coordinate space, whereby the target model TM and the object model OM are at least partially overlapped.

[0195] The model generation unit 2211 may also change the display mode of a set of display objects including at least one point group cluster PGC displayed in the display image 521 according to the input of the user using the input device 24. For example, the model generation unit 2211 may parallelly move the display object in a prescribed coordinate space defined within the display image 521. For example, the model generation unit 2211 may rotate the display object in a prescribed coordinate space defined within the display image 521. For example, the model generation unit 2211 may enlarge the display object in a prescribed coordinate space defined within the display image 521. Further, when at least one point group cluster PGC is displayed together with at least one of the target model TM and the object model OM in the display image 521, the set of display objects including at least one point group cluster PGC may also include at least one of the target model TM and the object model OM.

[0196] In the present embodiment, the point cloud clustering display image 52 may further include a display image 522 of an operation object 5221 that can be operated by the user to adjust the clustering threshold TH_C for generating the point cloud clustering PGC. In this case, the user can also adjust (in other words, set or change) the clustering threshold TH_C by operating the operation object 5221 using the input device 24.

[0197] In Figure 16 the example shown, a slider (in other words, a scroll bar) is used as the operation object 5221. In this case, the user can also adjust the clustering threshold TH_C in such a way that the clustering threshold TH_C becomes larger by performing an operation of moving the slider in the first direction (for example, Figure 16 the upward direction in Figure 16 ). On the other hand, the user can also adjust the clustering threshold TH_C in such a way that the clustering threshold TH_C becomes smaller by performing an operation of moving the slider in the second direction opposite to the first direction (for example,

[0198] the downward direction in

[0199] In addition, a display object different from the slider can also be used as the operation object 5221. For example, as the operation object 5221, a display object that can specify one candidate value to be set as the clustering threshold TH_C from among a plurality of candidate values for the clustering threshold TH_C can also be used. As an example of such a display object, at least one of a combo box, a drop-down list, and a radio button is listed.

[0199] The input device 224 for the user to operate the operation object 5221 can also be configured to be able to accept the user's input for operating the operation object 5221. As an example, the input device 224 can also be able to accept at least one of a swipe input, a zoom input, and a rotation input. In this case, the user can also adjust the clustering threshold TH_C by performing at least one of a swipe input, a zoom input, and a rotation input.

[0200] The user can also adjust the clustering threshold TH_C in microns. The user can also adjust the clustering threshold TH_C from sub-millimeters to centimeters. The user can also adjust the clustering threshold TH_C using the number of pixels of the image.

[0201] Again in Figure 14In this case, the model generation unit 2211 determines whether the user has adjusted the clustering threshold TH_C using the input device 24 (step S422). That is, the model generation unit 2211 determines whether the user has performed an operation for adjusting the clustering threshold TH_C using the input device 24 (step S422). In other words, the model generation unit 2211 determines whether the input device 24 has received the user's input for adjusting the clustering threshold TH_C (step S422).

[0202] When the result of the determination in step S422 is that it is determined that the user has adjusted the clustering threshold TH_C (step S422: Yes), the model generation unit 2211 extracts at least one point group cluster PGC using the clustering threshold TH_C adjusted by the user (step S423). That is, the model generation unit 2211 extracts at least one point group cluster PGC again using the clustering threshold TH_C adjusted by the user. Specifically, the model generation unit 2211 classifies two extraction points Pext that satisfy the condition that the distance D2 between the two extraction points Pext is less than or equal to the clustering threshold TH_C adjusted by the user into the same point group cluster PGC, thereby generating at least one point group cluster PGC (step S423). In other words, the model generation unit 2211 updates at least one point group cluster PGC according to the user's operation on the operation object 5221 (step S423).

[0203] Then, the model generation unit 2211 controls the display device 25 to display a point group cluster display image 52 for displaying at least one point group cluster PGC generated in step S423 (step S424). As a result, the display device 25 displays, under the control of the model generation unit 2211, a point group cluster display image 52 for displaying at least one point group cluster PGC generated in step S423 (step S424).

[0204] In this case, it can also be regarded that the model generation unit 2211 controls the display device 25 to update the point group cluster display image 52. Specifically, it can also be regarded that the model generation unit 2211 controls the display device 25 to update the point group cluster display image 52 according to at least one point group cluster PGC generated in step S423. Since at least one point group cluster PGC is generated according to the user's operation on the operation object 5221 in step S423, it can also be regarded that the model generation unit 2211 controls the display device 25 to update the point group cluster display image 52 according to the user's operation on the operation object 5221.

[0205] Then, each time it is determined that the user has adjusted the clustering threshold TH_C (step S422: Yes), the model generation unit 2211 generates at least one point group clustering PGC (step S423) and updates the point group clustering display image 52 (step S424). In addition, when the adjustment speed of the clustering threshold TH_C by the user is equal to or higher than a specified threshold, the model generation unit 2211 may not update the point group clustering display image 52 each time the user adjusts the clustering threshold TH_C.

[0206] The model generation unit 2211 may also reflect the adjustment result of the clustering threshold TH_C by the user in the point group clustering display image 52 in real time. That is, the model generation unit 2211 may also, when the user has adjusted the clustering threshold TH_C, generate at least one point group clustering PGC in real time and update the point group clustering display image 52. As an example, when the operations of steps S422 to S424 are performed at a relatively fast cycle, the model generation unit 2211 may also reflect the adjustment result of the clustering threshold TH_C by the user in the extraction point display image 51 in real time. However, the model generation unit 2211 may not reflect the adjustment result of the clustering threshold TH_C by the user in the point group clustering display image 52 in real time.

[0207] In this way, in the present embodiment, the user can appropriately adjust the clustering threshold TH_C using the operation object 5221 included in the point group clustering display image 52. For example, when at least one point group clustering PGC displayed in the point group clustering display image 52 is inappropriate, the user can also adjust the clustering threshold TH_C to generate an appropriate point group clustering PGC. At this time, the generated point group clustering PGC is displayed in the point group clustering display image 52, so the user can easily confirm the point group clustering PGC displayed in the point group clustering display image 52. As a result, compared with the case where the generated point group clustering PGC is not displayed in the point group clustering display image 52, the user can more easily adjust the clustering threshold TH_C to generate an appropriate point group clustering PGC. Furthermore, compared with the case where the clustering threshold TH_C is fixed, when the clustering threshold TH_C can be adjusted, the model generation unit 2211 can generate a more appropriate point group clustering PGC. As a result, the model generation unit 2211 can generate a more appropriate difference model DM based on the appropriately generated point group clustering PGC.

[0208] The model generation unit 2211 may also change the display mode of each point cloud cluster PGC according to the number of extracted points Pext included in each point cloud cluster PGC. For example, compared with a point cloud cluster PGC classified with a relatively small number of extracted points Pext, a point cloud cluster PGC classified with a relatively large number of extracted points Pext has a higher possibility of including extracted points Pext that become noise points. This is because the fewer the number of extracted points Pext classified into a point cloud cluster PGC, the higher the possibility that the extracted points Pext classified into this point cloud cluster PGC are points Ptm of the target model TM that are accidentally extracted as extracted points Pext for some reason. Therefore, the model generation unit 2211 may also change the display mode of each point cloud cluster PGC so that the display mode of a point cloud cluster PGC in which the number of classified extracted points Pext exceeds the lower threshold is different from the display mode of a point cloud cluster PGC in which the number of classified extracted points Pext is lower than the lower threshold. That is, the model generation unit 2211 may also change the display mode of each point cloud cluster PGC so that the display mode of a point cloud cluster PGC classified with extracted points Pext having a relatively high possibility of being noise points is different from the display mode of a point cloud cluster PGC classified with extracted points Pext having a relatively low possibility of being noise points. In this case, the user can also adjust the clustering threshold TH_C by confirming the point cloud cluster display image 52 so that no point cloud cluster PGC containing extracted points Pext with a relatively high possibility of being noise points is generated. As a result, the model generation unit 2211 can appropriately generate a point cloud cluster PGC containing extracted points Pext that appropriately represent the difference between the target model TM and the object model OM. As a result, the model generation unit 2211 can generate a more appropriate difference model DM based on such appropriately generated point cloud clusters PGC. In addition, as an example of the display mode, the display color is listed.

[0209] As a result of the clustering threshold TH_C being adjusted, the number of generated point cloud clusters PGC may also change. That is, the number of point cloud clusters PGC generated after the clustering threshold TH_C is adjusted may be different from the number of point cloud clusters PGC generated before the clustering threshold TH_C is adjusted. Or, as a result of the clustering threshold TH_C being adjusted, the number of generated point cloud clusters PGC may remain unchanged. That is, the number of point cloud clusters PGC generated after the clustering threshold TH_C is adjusted may be the same as the number of point cloud clusters PGC generated before the clustering threshold TH_C is adjusted.

[0210] On the other hand, when the result of the determination in step S422 is that it is determined that the user has not adjusted the clustering threshold TH_C (step S422: No), the model generation unit 2211 determines whether to end the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C (step S431). For example, when the user requests to continue adjusting at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C, the model generation unit 2211 may also determine not to end the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C. For example, when the user does not request to continue adjusting at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C, the model generation unit 2211 may also determine to end the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C. For example, when the user requests to end the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C, the model generation unit 2211 may also determine to end the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C.

[0211] When the result of the determination in step S431 is that it is determined not to end the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C (step S431: No), the operations after step S411 are repeated. For example, it may also be that the user adjusts the inter-model distance threshold TH_M as needed, and the model generation unit 2211 extracts a plurality of extraction points Pext and updates the extraction point display image 51 according to the user's adjustment of the inter-model distance threshold TH_M. For example, it may also be that the user adjusts the clustering threshold TH_C as needed, and the model generation unit 2211 generates a point group clustering PGC and updates the point group clustering display image 52 according to the user's adjustment of the clustering threshold TH_C.

[0212] That is, in the present embodiment, the user can also repeat the adjustment of the inter-model distance threshold TH_M and the adjustment of the clustering threshold TH_C as many times as needed. For example, the user can also perform the adjustment of the inter-model distance threshold TH_M once and the adjustment of the clustering threshold TH_C once, thereby ending the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C. For example, the user can also perform the adjustment of the inter-model distance threshold TH_M multiple times and the adjustment of the clustering threshold TH_C once, thereby ending the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C. For example, the user can also perform the adjustment of the inter-model distance threshold TH_M once and the adjustment of the clustering threshold TH_C multiple times, thereby ending the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C. For example, the user can also perform the adjustment of the inter-model distance threshold TH_M multiple times and the adjustment of the clustering threshold TH_C multiple times, thereby ending the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C.

[0213] In the case where the user makes multiple adjustments to the inter-model distance threshold TH_M, at least one of the multiple adjustments to the inter-model distance threshold TH_M by the user may also include a fine adjustment of the inter-model distance threshold TH_M for setting the inter-model distance threshold TH_M to a value desired by the user. In this case, when the user makes a fine adjustment to the inter-model distance threshold TH_M, the model generation unit 2211 may not extract a plurality of extraction points Pext in step S413, nor may it update the extraction point display image 51 in step S414. In other words, when the adjustment amount (in other words, the change amount) of the inter-model distance threshold TH_M by the user is within a specified range, the model generation unit 2211 may not extract a plurality of extraction points Pext in step S413, nor may it update the extraction point display image 51 in step S414. The model generation unit 2211 may also extract a plurality of extraction points Pext and update the extraction point display image 51 after the fine adjustment of the inter-model distance threshold TH_M by the user is completed and the value of the inter-model distance threshold TH_M is determined.

[0214] In the case where the user makes multiple adjustments to the clustering threshold TH_C, at least one of the multiple adjustments to the clustering threshold TH_C by the user may also include a fine adjustment of the clustering threshold TH_C for setting the clustering threshold TH_C to a value desired by the user. In this case, when the user makes a fine adjustment to the clustering threshold TH_C, the model generation unit 2211 may not generate a point group clustering PGC in step S423, nor may it update the point group clustering display image 52 in step S424. In other words, when the adjustment amount (in other words, the change amount) of the clustering threshold TH_C by the user is within a specified range, the model generation unit 2211 may not generate a point group clustering PGC in step S423, nor may it update the point group clustering display image 52 in step S424. The model generation unit 2211 may also generate a point group clustering PGC and update the point group clustering display image 52 after the fine adjustment of the clustering threshold TH_C by the user is completed and the value of the clustering threshold TH_C is determined.

[0215] On the other hand, when the result of the determination in step S431 is that the adjustment of the inter-model distance threshold TH_M and the clustering threshold TH_C is determined to be completed (step S431: Yes), the model generation unit 2211 generates a differential model DM based on at least one point group clustering PGC generated in step S423 (step S432). For example, the model generation unit 2211 may also generate a three-dimensional model including a plurality of extraction points Pext classified into at least one point group clustering PGC as the differential model DM. For example, the model generation unit 2211 may also generate a three-dimensional model having a three-dimensional shape represented by a plurality of extraction points Pext classified into at least one point group clustering PGC as the differential model DM. In this case, it can also be regarded that the point group clustering PGC is used as the differential model DM. It can also be regarded that the plurality of extraction points Pext included in the point group clustering PGC are used as the differential model DM.

[0216] When a single point group clustering PGC is generated in step S423, the model generation unit 2211 may also generate a differential model DM based on the single point group clustering PGC. When a plurality of point group clusterings PGC are generated in step S423, the model generation unit 2211 may also generate a differential model DM based on at least one of the plurality of point group clusterings PGC.

[0217] When a plurality of point group clusterings PGC are generated in step S423, the user may also specify at least one point group clustering PGC to be used for generating the differential model DM. For example, as Figure 17 shown, the user may also operate a pointer 529 for specifying at least one point group clustering PGC on the point group clustering display image 52 using the input device 24, thereby specifying at least one point group clustering PGC. In this case, the model generation unit 2211 may also generate a differential model DM based on at least one point group clustering PGC specified by the user. Alternatively, the model generation unit 2211 may automatically specify at least one point group clustering PGC to be used for generating the differential model DM. Alternatively, it may be that a clustering specifying device different from the model generation unit 2211 automatically specifies at least one point group clustering PGC to be used for generating the differential model DM. As an example of the clustering specifying device, a device using the following operation model is listed: an operation model that can determine at least one point group clustering PGC to be used for generating the differential model DM when the feature amounts of a plurality of point group clusterings PGC are input. Such an operation model may also be an operation model that can be learned by machine learning.

[0218] The clustering specifying device (or the arithmetic model used by the clustering specifying device, the same applies hereinafter in this paragraph) can also determine at least one point group clustering PGC that should be used to generate the differential model DM according to the type of the workpiece W to be additionally processed. For example, when the workpiece W is a turbine blade, the tip (in other words, the upper part) of the turbine blade is worn. Therefore, the clustering specifying device can also determine the point group clustering PGC located at the tip (in other words, the upper part) of the turbine blade or in its vicinity as at least one point group clustering PGC that should be used to generate the differential model DM. On the other hand, the frequency of wear on the side or bottom of the turbine blade is relatively low. Therefore, the clustering specifying device can also determine the point group clustering PGC located on the side or bottom of the turbine blade as noise points. That is, the point group clustering PGC located on the side or bottom of the turbine blade may not be determined as at least one point group clustering PGC that should be used to generate the differential model DM.

[0219] As described above, compared with the point group clustering PGC with a relatively large number of extracted points Pext classified, the point group clustering PGC with a relatively small number of extracted points Pext classified has a higher possibility of including the extracted points Pext that become noise points. Therefore, when multiple point group clusterings PGC are generated, the model generation unit 2211 can also generate the differential model DM according to the point group clustering PGC that satisfies the clustering selection condition based on the number of extracted points Pext classified into the point group clustering PGC. The clustering selection condition may also include the following first condition: the number of extracted points Pext classified into the point group clustering PGC is equal to or greater than the lower limit threshold. In this case, the model generation unit 2211 can also generate the differential model DM according to at least one point group clustering PGC whose number of extracted points Pext classified into the point group clustering PGC is equal to or greater than the lower limit threshold. The clustering selection condition may also include the following second condition: the number of extracted points Pext classified into the point group clustering PGC is the largest. In this case, the model generation unit 2211 can also generate the differential model DM according to one point group clustering PGC whose number of extracted points Pext classified into the point group clustering PGC is the largest. In this case, the model generation unit 2211 can generate the differential model DM without using the extracted points Pext that have a relatively high possibility of being noise points. As a result, the model generation unit 2211 can generate a differential model DM that accurately represents the difference between the object model OM and the target model TM.

[0220] In the case where the differential model DM is generated, the model generation unit 2211 may also generate a differential model file related to the generated differential model DM (step S433). The differential model file may also store the differential model DM. The differential model file may also store a plurality of slice data obtained by slicing the differential model DM as described above. The differential model file may also store at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C used to generate the differential model DM. The differential model file may also store at least one of the object model OM and the target model TM used to generate the differential model DM. Alternatively, the differential model file may be associated with another file that stores at least one of the object model OM and the target model TM used to generate the differential model DM. Such a differential model file may also be used for the purpose of traceability of the generation environment of the differential model DM. In this case, by referring to the differential model file, the user can easily confirm the generation environment when generating a certain differential model DM.

[0221] In addition, in the case where the differential model DM is generated, the model generation unit 2211 may also control the display device 25 to display the generated differential model DM. As a result, the display device 25 may also display the differential model DM generated by the model generation unit 2211 under the control of the model generation unit 2211.

[0222] In addition, in the case where the differential model DM is generated, the model generation unit 2211 outputs the generated differential model DM to the control information generation unit 2212. As a result, the control information generation unit 2212 generates machining control information based on the differential model DM generated by the model generation unit 2211.

[0223] However, in Figure 14 In step S432, the model generation unit 2211 may, in addition to or instead of generating the differential model DM based on at least one point group clustering PGC, output at least one point group clustering PGC as information for generating the differential model DM. In this case, it may be that a device different from the model generation unit 2211 generates the differential model DM based on the at least one point group clustering PGC generated by the model generation unit 2211. The control information generation unit 2212 may also generate machining control information based on the differential model DM generated by a device different from the model generation unit 2211.

[0224] (3) Technical effects

[0225] As described above, in the present embodiment, the user can repeat the adjustment of the inter-model distance threshold TH_M and the adjustment of the clustering threshold TH_C as many times as desired. Therefore, the user can adjust the inter-model distance threshold TH_M again after adjusting the clustering threshold TH_C. For example, when the point group clustering PGC displayed on the point group clustering display image 52 is inappropriate after the user adjusts the clustering threshold TH_C, the user can not only adjust the clustering threshold TH_C, but also adjust the inter-model distance threshold TH_M. Here, as the reason for the inappropriateness of the generated point group clustering PGC, not only the reason that the clustering threshold TH_C is inappropriate is considered, but also the reason that the multiple extraction points Pext that are originally the basis for calculating the point group clustering PGC are inappropriate is considered. In the present embodiment, when the point group clustering PGC is inappropriate, the user can not only adjust the clustering threshold TH_C to solve the problem of the inappropriate clustering threshold TH_C, but also adjust the inter-model distance threshold TH_M again to solve the problem of the inappropriate multiple extraction points Pext. Furthermore, when the inter-model distance threshold TH_M is adjusted, the user can adjust the clustering threshold TH_C again according to the adjustment of the inter-model distance threshold TH_M. Therefore, compared with the case where the user cannot adjust the inter-model distance threshold TH_M again after adjusting the clustering threshold TH_C, the model generation unit 2211 can generate a more appropriate point group clustering PGC. As a result, the model generation unit 2211 can generate a more appropriate differential model DM based on the appropriately generated point group clustering PGC.

[0226] (4) Variant example

[0227] Next, a variant example of the processing system SYS will be described.

[0228] (4-1) First variant example

[0229] As described above, each time the user adjusts the inter-model distance threshold TH_M ( Figure 14 step S412: Yes), the model generation unit 2211 extracts multiple extraction points Pext from the target model TM in real time according to the inter-model distance threshold TH_M adjusted by the user ( Figure 14 step S413), and displays the extraction points Pext in real time ( Figure 14 step S414). However, the larger the number of points Ptm included in the target model TM and the number of points Pom included in the object model OM (i.e., the data size of the point group), the higher the processing load required to extract and display the multiple extraction points Pext. As a result, depending on the data size of the point group, the model generation unit 2211 may not be able to extract and display the multiple extraction points Pext in real time.

[0230] Similarly, as described above, each time the user adjusts the clustering threshold TH_C ( Figure 14 Step S422: Yes), the model generation unit 2211 generates at least one point group clustering PGC in real time according to the adjusted clustering threshold TH_C by the user ( Figure 14 Step S423) and displays the point group clustering PGC in real time ( Figure 14 Step S424). However, the larger the number of points Ptm included in the target model TM and the number of points Pom included in the object model OM (i.e., the data size of the point group), the higher the processing load required to generate and display at least one point group clustering PGC. As a result, depending on the data size of the point group, the model generation unit 2211 may not be able to generate and display at least one point group clustering PGC in real time.

[0231] Therefore, in the first modification example, in addition to the original target model TM_original obtained in Figure 8 Step S3, the model generation unit 2211 can also use a reduced target model TM_reduced with a smaller number of points Ptm than the original target model TM_original (i.e., a smaller data size of the point group) as the target model TM. Similarly, in addition to the original object model OM_original generated by the shape measurement device 21 in Figure 8 Step S2, the model generation unit 2211 can also use a reduced object model OM_reduced with a smaller number of points Pom than the original object model OM_original (i.e., a smaller data size of the point group) as the object model OM.

[0232] In addition, in the following description, if necessary, the original target model TM_original and the original object model OM_original are collectively referred to as "original model M_original". In addition, in the following description, if necessary, the reduced target model TM_reduced and the reduced object model OM_reduced are collectively referred to as "reduced model M_reduced". In addition, the reduced model M_reduced can also be regarded as equivalent to a reduced three-dimensional image. Therefore, the reduced model M_reduced can also be called a "thumbnail" representing a reduced (in other words, compressed) image.

[0233] When using the reduced model M_reduced, the model generation unit 2211 can also start Figure 14Before the differential model generation operation shown, obtain the reduced model M_reduced. For example, the model generation unit 2211 may also perform a reduction process on the target model TM_original to generate the target model TM_reduced, thereby obtaining the target model TM_reduced. For example, the model generation unit 2211 may also obtain the target model TM_reduced from a model generation device that generates the target model TM_reduced. For example, the model generation unit 2211 may perform a reduction process on the object model OM_original to generate the object model OM_reduced, thereby obtaining the object model OM_reduced. For example, the model generation unit 2211 may also obtain the object model OM_reduced from a model generation device that generates the object model OM_reduced. Then, in addition to the original model M_original, the model generation unit 2211 may also use the reduced model M_reduced to perform Figure 14 the differential model generation operation shown.

[0234] As Figure 18 shown in (a) of, the original model M_original and the reduced model M_reduced may also be used in a file format in which the original model M_original and the reduced model M_reduced are stored in one data file. Or, as Figure 18 shown in (b) of, the original model M_original and the reduced model M_reduced may also be used in a file format in which the original model M_original and the reduced model M_reduced are stored in different data files respectively. In the case where the original model M_original and the reduced model M_reduced are stored in different data files respectively, as Figure 18 shown in (c) of, the model generation unit 2211 may also use the file association information to obtain one original model M_original and one reduced model M_reduced corresponding to one original model M_original, and the file association information associates the data file storing one original model M_original with the data file storing one reduced model M_reduced corresponding to one original model M_original.

[0235] Hereinafter, a specific example of the differential model generation operation using the reduced model M_reduced will be described.

[0236] (4-1-1) First specific example of the differential model generation operation using the reduced model M_reduced

[0237] In the first specific example, in order to Figure 14In step S410 or step S413, when extracting a plurality of extraction points Pext, the model generation unit 2211 may also first use the reduced model M_reduced instead of the original model M_original.

[0238] For example, Figure 19 FIG. shows a plurality of extraction points Pext extracted by using the reduced model M_reduced instead of the original model M_original. As Figure 19 shown, at time t11, when the user adjusts the inter-model distance threshold TH_M such that it becomes the first inter-model distance threshold TH_M#11, the model generation unit 2211 may also use the target model TM_reduce, the object model OM_reduced, and the first inter-model distance threshold TH_M#11 to extract a plurality of extraction points Pext. Further, the display device 25 may also display an extraction point display image 51 for displaying the plurality of extraction points Pext. Then, at time t12, when the user adjusts the inter-model distance threshold TH_M such that it changes from the first inter-model distance threshold TH_M#11 to the second inter-model distance threshold TH_M#12, the model generation unit 2211 may also use the target model TM_reduce, the object model OM_reduced, and the second inter-model distance threshold TH_M#12 to extract a plurality of extraction points Pext. Further, the display device 25 may also display an extraction point display image 51 for displaying the plurality of extraction points Pext. As Figure 19 shown, it may also be that due to the change in the inter-model distance threshold TH_M, at least one of the position and the number of the plurality of extraction points Pext changes. Then, at time t13, when the user adjusts the inter-model distance threshold TH_M such that it changes from the second inter-model distance threshold TH_M#12 to the third inter-model distance threshold TH_M#13, the model generation unit 2211 may also use the target model TM_reduce, the object model OM_reduced, and the third inter-model distance threshold TH_M#13 to extract a plurality of extraction points Pext. Further, the display device 25 may also display an extraction point display image 51 for displaying the plurality of extraction points Pext. As Figure 19 shown, it may also be that due to the change in the inter-model distance threshold TH_M, at least one of the position and the number of the plurality of extraction points Pext changes.

[0239] As a result of the adjustment of the inter-model distance threshold TH_M, when the user determines that the third inter-model distance threshold TH_M#13 is suitable as the final inter-model distance threshold TH_M, the model generation unit 2211 can also use the original model M_original instead of the reduced model M_reduced to extract a plurality of extraction points Pext. That is, the model generation unit 2211 can also extract a plurality of points Ptm that satisfy the condition that the inter-model distance D1 between the point Ptm of the target model TM_original and the closest point Pom_closest of the object model OM_original is equal to or greater than the third inter-model distance threshold TH_M#13 as the plurality of extraction points Pext. Further, if necessary, the display device 25 can also display an extraction point display image 51 for displaying the plurality of extraction points Pext.

[0240] As a result, during the period when the user adjusts the inter-model distance threshold TH_M, the model generation unit 2211 uses the reduced model M_reduced to extract and display a plurality of extraction points Pext. Therefore, compared with the case of using the original model M_original, the processing load required to extract and display the plurality of extraction points Pext is reduced. As a result, the possibility that the model generation unit 2211 can extract and display the plurality of extraction points Pext in real time becomes higher.

[0241] On the other hand, after the user completes the adjustment of the inter-model distance threshold TH_M, the model generation unit 2211 re-extracts a plurality of extraction points Pext using the original model M_original. Therefore, the model generation unit 2211 can use the plurality of extraction points Pext extracted using the original model M_original instead of the plurality of extraction points Pext extracted using the reduced model M_reduced to generate the point group clustering PGC. Therefore, the action of extracting the plurality of extraction points Pext using the reduced model M_reduced does not affect the subsequent action of generating the point group clustering PGC.

[0242] (4-1-2) Second specific example of the differential model generation action using the reduced model M_reduced

[0243] In the second specific example, in order to Figure 14In step S410 or step S413, multiple extraction points Pext are extracted. In addition to the original model M_original, the model generation unit 2211 can also use the reduced model M_reduced. That is, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the original model M_original and the process of extracting multiple extraction points Pext using the reduced model M_reduced in parallel. However, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the original model M_original as a background process. On the other hand, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the reduced model M_reduced as a foreground process.

[0244] For example, Figure 20 shows multiple extraction points Pext extracted using the reduced model M_reduced in addition to the original model M_original. As Figure 20 shown, at time t14, when the user adjusts the inter-model distance threshold TH_M to the fourth inter-model distance threshold TH_M#14, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the target model TM_reduce, the object model OM_reduced, and the fourth inter-model distance threshold TH_M#14 as a foreground process. Furthermore, the display device 25 can also display an extraction point display image 51 for displaying the multiple extraction points Pext extracted by the foreground process. On the other hand, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the target model TM_original, the object model OM_original, and the fourth inter-model distance threshold TH_M#14 as a background process. In this case, the display device 25 may not display an extraction point display image 51 for displaying the multiple extraction points Pext extracted by the background process. Then, at time t15, when the user changes the inter-model distance threshold TH_M from the fourth inter-model distance threshold TH_M#14 to the fifth inter-model distance threshold TH_M#15, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the target model TM_reduce, the object model OM_reduced, and the fifth inter-model distance threshold TH_M#15 as a foreground process. Furthermore, the display device 25 can also display an extraction point display image 51 for displaying the multiple extraction points Pext extracted by the foreground process. As Figure 20As shown, it can also be that due to the change in the model - to - model distance threshold TH_M, at least one of the positions and the number of multiple extraction points Pext changes. On the other hand, the model generation unit 2211 can also perform the process of extracting multiple extraction points Pext using the target model TM_original, the object model OM_original, and the fifth model - to - model distance threshold TH_M#15 as a background process. In this case, the display device 25 may not display the extraction point display image 51 for displaying the multiple extraction points Pext extracted by the background process.

[0245] As a result of such an adjustment of the model - to - model distance threshold TH_M, when the user determines that the fifth model - to - model distance threshold TH_M#15 is suitable as the final model - to - model distance threshold TH_M, the model generation unit 2211 can also generate the point - group clustering PGC using the multiple extraction points Pext that have been generated by the background process using the fifth model - to - model distance threshold TH_M#15. As a result, compared with the case where no background process is performed, the time required from when the user completes the adjustment of the model - to - model distance threshold TH_M to the generation of the point - group clustering PGC is shortened. In addition, since the process of extracting multiple extraction points Pext using the original model M_original is performed as a background process, compared with the case where the process of extracting multiple extraction points Pext using the original model M_original is performed as a foreground process, the model generation unit 2211 can allocate relatively more resources to the process of extracting and displaying multiple extraction points Pext using the reduced model M_reduced. As a result, the possibility that the model generation unit 2211 can extract and display multiple extraction points Pext in real time becomes higher.

[0246] (4 - 1 - 3) The third specific example of the differential model generation operation using the reduced model M_reduced

[0247] In the third specific example, in order to generate at least one point - group clustering PGC in Figure 14 step S420 or step S423, the model generation unit 2211 can first use the reduced model M_reduced instead of the original model M_original.

[0248] For example, Figure 21 shows at least one point - group clustering PGC generated by using the reduced model M_reduced instead of the original model M_original. As Figure 21As shown, at time t21, when the user adjusts the clustering threshold TH_C such that the clustering threshold TH_C becomes the first clustering threshold TH_C#11, the model generation unit 2211 can also cluster the multiple extraction points Pext extracted from the target model TM_reduce according to the first clustering threshold TH_C#11, thereby generating at least one point group clustering PGC. Furthermore, the display device 25 can also display a point group clustering display image 52 for displaying at least one point group clustering PGC. Then, at time t22, when the user adjusts the clustering threshold TH_C such that the clustering threshold TH_C changes from the first clustering threshold TH_C#11 to the second clustering threshold TH_C#12, the model generation unit 2211 can also cluster the multiple extraction points Pext extracted from the target model TM_reduce according to the second clustering threshold TH_C#12, thereby generating at least one point group clustering PGC. Furthermore, the display device 25 can also display a point group clustering display image 52 for displaying at least one point group clustering PGC. As Figure 21 shown, it can also be that due to the change of the clustering threshold TH_C, at least one of the position, shape, and number of the point group clustering PGC changes. Then, at time t23, when the user adjusts the clustering threshold TH_C such that the clustering threshold TH_C changes from the second clustering threshold TH_C#12 to the third clustering threshold TH_C#13, the model generation unit 2211 can also cluster the multiple extraction points Pext extracted from the target model TM_reduce according to the third clustering threshold TH_C#13, thereby generating at least one point group clustering PGC. Furthermore, the display device 25 can also display a point group clustering display image 52 for displaying at least one point group clustering PGC. As Figure 21 shown, it can also be that due to the change of the clustering threshold TH_C, at least one of the position, shape, and number of the point group clustering PGC changes.

[0249] As a result of such an adjustment of the clustering threshold TH_C, when the user determines that the third clustering threshold TH_C#13 is suitable as the final clustering threshold TH_C, the model generation unit 2211 can also cluster the multiple extraction points Pext extracted from the target model TM_original instead of clustering the multiple extraction points Pext extracted from the target model TM_reduced, thereby generating at least one point group clustering PGC. Furthermore, as needed, the display device 25 can also display a point group clustering display image 52 for displaying at least one point group clustering PGC.

[0250] As a result, during the period when the user adjusts the clustering threshold TH_C, the model generation unit 2211 uses the reduced model M_reduced to generate and display at least one point group clustering PGC. Therefore, compared with the case of using the original model M_original, the processing load required to generate and display at least one point group clustering PGC is reduced. As a result, the possibility that the model generation unit 2211 can generate and display at least one point group clustering PGC in real time becomes higher.

[0251] On the other hand, after the user has completed the adjustment of the clustering threshold TH_C, the model generation unit 2211 regenerates at least one point group clustering PGC using the original model M_original. Therefore, the model generation unit 2211 can use at least one point group clustering PGC generated using the original model M_original instead of at least one point group clustering PGC generated using the reduced model M_reduced to generate the differential model DM. Therefore, the action of using the reduced model M_reduced to generate at least one point group clustering PGC does not affect the subsequent action of generating the differential model DM.

[0252] (4-1-4) The 4th specific example of the differential model generation action using the reduced model M_reduced

[0253] In the 4th specific example, in order to generate at least one point group clustering PGC in step S420 or step S423 of Figure 14 , in addition to the original model M_original, the model generation unit 2211 can also use the reduced model M_reduced. That is, the model generation unit 2211 can also perform the process of generating at least one point group clustering PGC using the original model M_original and the process of generating at least one point group clustering PGC using the reduced model M_reduced in parallel. However, the model generation unit 2211 can also perform the process of generating at least one point group clustering PGC using the original model M_original as a background process. On the other hand, the model generation unit 2211 can also perform the process of generating at least one point group clustering PGC using the reduced model M_reduced as a foreground process.

[0254] For example, Figure 22 shows at least one point group clustering PGC generated by using the reduced model M_reduced in addition to the original model M_original. As Figure 22As shown, at time t24, when the user adjusts the clustering threshold TH_C such that it becomes the 4th clustering threshold TH_C#14, the model generation unit 2211 may also perform, as a foreground process, the process of clustering a plurality of extraction points Pext extracted from the target model TM_reduce according to the 4th clustering threshold TH_C#14 to thereby generate at least one point group cluster PGC. Further, the display device 25 may also display a point group cluster display image 52 for displaying the at least one point group cluster PGC generated by the foreground process. On the other hand, the model generation unit 2211 may also perform, as a background process, the process of clustering a plurality of extraction points Pext extracted from the target model TM_original according to the 4th clustering threshold TH_C#14 to thereby generate at least one point group cluster PGC. In this case, the display device 25 may also not display the point group cluster display image 52 for displaying the at least one point group cluster PGC generated by the background process. Then, at time t25, when the user adjusts the clustering threshold TH_C such that it changes from the 4th clustering threshold TH_C#14 to the 5th clustering threshold TH_C#15, the model generation unit 2211 may also perform, as a foreground process, the process of clustering a plurality of extraction points Pext extracted from the target model TM_reduce according to the 5th clustering threshold TH_C#15 to thereby generate at least one point group cluster PGC. Further, the display device 25 may also display a point group cluster display image 52 for displaying the at least one point group cluster PGC generated by the foreground process. As Figure 22 shown, it may also be that due to the change in the clustering threshold TH_C, at least one of the position, shape, and number of the point group clusters PGC changes. On the other hand, the model generation unit 2211 may also perform, as a background process, the process of clustering a plurality of extraction points Pext extracted from the target model TM_original according to the 5th clustering threshold TH_C#15 to thereby generate at least one point group cluster PGC. In this case, the display device 25 may also not display the point group cluster display image 52 for displaying the at least one point group cluster PGC generated by the background process.

[0255] As a result of the adjustment of such a clustering threshold TH_C, in the case where the user determines that the 5th clustering threshold TH_C#15 is suitable as the final clustering threshold TH_C, the model generation unit 2211 can also use at least one point group clustering PGC generated by the background process using the 5th clustering threshold TH_C#15 to generate the differential model DM. As a result, compared with the case where no background process is performed, the time required from the user's completion of the adjustment of the clustering threshold TH_C to the generation of the point group clustering PGC is shortened. In addition, since the process of generating at least one point group clustering PGC using the original model M_original is performed as a background process, the model generation unit 2211 can allocate relatively more resources to the process of generating and displaying at least one point group clustering PGC using the reduced model M_reduced, compared with the case where the process of generating at least one point group clustering PGC using the original model M_original is performed as a foreground process. As a result, the possibility that the model generation unit 2211 can generate and display at least one point group clustering PGC in real time becomes higher.

[0256] (4-2) Second Modification

[0257] In the second modification, as Figure 23 shown, the model generation unit 2211 can also use at least one of the model distance threshold TH_M and the clustering threshold TH_C adjusted to generate the first differential model DM#21, which represents the three-dimensional shape of the first three-dimensional structure ST#21 that should be modeled on the first workpiece W#21, to generate the second differential model DM#22, which represents the three-dimensional shape of the second three-dimensional structure ST#22 that should be modeled on the second workpiece W#22 different from the first workpiece W#21.

[0258] In particular, as Figure 23As shown, when the processing device 1 processes a plurality of workpieces W having the same shape so that the shapes of the plurality of workpieces W become the same target shape, the three-dimensional shape of the first three-dimensional structure ST#21 of the second workpiece W#21 to be modeled should be the same as the three-dimensional shape of the second three-dimensional structure ST#22 of the second workpiece W#22 to be modeled. Therefore, the first difference model DM#21 and the second difference model DM#22 should also be the same. Therefore, even if at least one of the model distance threshold TH_M and the clustering threshold TH_C used to generate the first difference model DM#21 is used to generate the second difference model DM#22, the model generation unit 2211 can generate the second difference model DM#22 that accurately represents the three-dimensional shape of the second three-dimensional structure ST#22 of the second workpiece W#22 accordingly. Therefore, when the processing device 1 processes a plurality of workpieces W having the same shape so that the shapes of the plurality of workpieces W become the same target shape, the technical effect brought by using at least one of the model distance threshold TH_M and the clustering threshold TH_C is enhanced.

[0259] Specifically, to generate the second difference model DM#22, in Figure 14 step S410, the model generation unit 2211 can also use the model distance threshold TH_M adjusted to generate the first difference model DM#21 to extract a plurality of extraction points Pext. For example, when the model distance threshold TH_M#21 is used as the determined value of the model distance threshold TH_M to generate the first difference model DM#21, in Figure 14 step S410, the model generation unit 2211 can also use the model distance threshold TH_M#21 to extract a plurality of extraction points Pext.

[0260] In this case, in Figure 14 step S412, the user does not necessarily need to adjust the model distance threshold TH_M. Therefore, the effort of the user to adjust the model distance threshold TH_M is reduced. Furthermore, the time required for the user to adjust the model distance threshold TH_M, the time required for the extraction of a plurality of extraction points Pext accompanying the adjustment of the model distance threshold TH_M by the user, and the time required for the update of the extraction point display image 51 accompanying the extraction of the plurality of extraction points Pext are not needed. Therefore, the throughput related to the generation of the difference model DM is increased. Furthermore, the model generation unit 2211 can also not perform the extraction of a plurality of extraction points Pext accompanying the adjustment of the model distance threshold TH_M by the user and the update of the extraction point display image 51 accompanying the extraction of the plurality of extraction points Pext. Therefore, the processing load of the model generation unit 2211 required for the generation of the difference model DM is reduced.

[0261] In addition, in order to generate the second difference model DM#22, in Figure 14 step S420, the model generation unit 2211 may also use the clustering threshold TH_C adjusted for generating the first difference model DM#21 to generate at least one point group clustering PGC. For example, in the case where the clustering threshold TH_C#21 is used as the determined value of the clustering threshold TH_C for generating the first difference model DM#21, in Figure 14 step S420, the model generation unit 2211 may also use the clustering threshold TH_C#21 to generate at least one point group clustering PGC.

[0262] In this case, in Figure 14 step S422, the user may not necessarily adjust the clustering threshold TH_C. Therefore, the effort of the user for adjusting the clustering threshold TH_C is reduced. Furthermore, the time required by the user for adjusting the clustering threshold TH_C, the time required for generating at least one point group clustering PGC accompanying the adjustment of the clustering threshold TH_C by the user, and the time required for updating the point group clustering display image 52 accompanying the generation of at least one point group clustering PGC are not required. Therefore, the throughput related to the generation of the difference model DM is improved. Furthermore, the model generation unit 2211 may also not perform the generation of at least one point group clustering PGC accompanying the adjustment of the clustering threshold TH_C by the user and the update of the point group clustering display image 52 accompanying the generation of at least one point group clustering PGC. Therefore, the processing load of the model generation unit 2211 required for generating the difference model DM is reduced.

[0263] The model generation unit 2211 may also use the single inter-model distance threshold TH_M adjusted by the user with a single workpiece W as the object as the inter-model distance threshold TH_M with other workpieces W as the object. Alternatively, the model generation unit 2211 may also use two or more inter-model distance thresholds TH_M adjusted by the user with two or more workpieces W as the object as the inter-model distance threshold TH_M with other workpieces W as the object. In this case, the model generation unit 2211 may also use the value calculated based on two or more inter-model distance thresholds TH_M as the inter-model distance threshold TH_M with other workpieces W as the object. As an example of the value calculated based on two or more inter-model distance thresholds TH_M, at least one of the average value, median value, minimum value, and maximum value of two or more inter-model distance thresholds TH_M is listed.

[0264] Furthermore, in order to generate the second difference model DM#22, the model generation unit 2211 may also use, in addition to or instead of at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C adjusted for generating the first difference model DM#21, the position of the first difference model DM#21. Specifically, the model generation unit 2211 may also use the point group clustering PGC at the position closest to the first difference model DM#21 among the at least one point group clustering PGC generated for generating the second difference model DM#22. That is, the model generation unit 2211 may also use the point group clustering PGC at the position closest to the first difference model DM#21 to generate the second difference model DM#22.

[0265] However, the model generation unit 2211 may also not use at least one of the inter-model distance threshold TH_M, the clustering threshold TH_C, and the position of the difference model DM. That is, the user may also adjust the inter-model distance threshold TH_M and the clustering threshold TH_C for generating the first difference model DM#21, and adjust the inter-model distance threshold TH_M and the clustering threshold TH_C for generating the second difference model DM#22. In this case, compared with the case of using at least one of the inter-model distance threshold TH_M, the clustering threshold TH_C, and the position of the difference model DM, the model generation unit 2211 can generate a second difference model DM#22 that more accurately represents the three-dimensional shape of the second three-dimensional structure ST#22 to be modeled on the second workpiece W#22.

[0266] In addition, in the case where the three-dimensional shape of the second difference model DM#22 generated by using at least one of the inter-model distance threshold TH_M, the clustering threshold TH_C, and the position of the difference model DM is very different from the assumed three-dimensional shape, the model generation unit 2211 may also regenerate the second difference model DM#22 without using at least one of the inter-model distance threshold TH_M, the clustering threshold TH_C, and the position of the difference model DM. This is because it is assumed that in this case, due to using at least one of the inter-model distance threshold TH_M, the clustering threshold TH_C, and the position of the difference model DM, the accuracy of the second difference model DM#22 will deteriorate. In this case, the model generation unit 2211 may also use the inter-model distance threshold TH_M and the clustering threshold TH_C adjusted by the user to generate the second difference model DM#22 for generating the second difference model DM#22.

[0267] In addition, as described above, when the processing device 1 processes a plurality of workpieces W so that the shapes of the plurality of workpieces W having the same shape become the same target shape, the technical effect brought by using at least one of the model distance threshold TH_M and the clustering threshold TH_C becomes higher. In this way, when at least one workpiece W having a greatly different shape is included among the plurality of workpieces W, if at least one of the model distance threshold TH_M and the clustering threshold TH_C is used for the at least one workpiece W having a greatly different shape, the accuracy of the differential model DM may deteriorate. Therefore, the model generation unit 2211 may also determine whether the actual shape of the workpiece W is greatly different from the assumed shape based on the measurement result of the three-dimensional shape of the workpiece W by the shape measurement device 21 (that is, the object model OM). For the workpiece W having an actual shape greatly different from the assumed shape, the model generation unit 2211 may generate the differential model DM without using at least one of the model distance threshold TH_M, the clustering threshold TH_C, and the position of the differential model DM. As a result, even when at least one workpiece W having a greatly different shape is included among the plurality of workpieces W, the possibility of deterioration of the accuracy of the differential model DM is reduced.

[0268] The second modification may be combined with the first modification. That is, in the first modification, the model generation unit 2211 may use at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C as described in the second modification.

[0269] (4-3) Third Modification

[0270] In the third modification, Figure 8 After acquiring the target model TM in step S3, the model generation unit 2211 deforms the target model TM according to the object model OM, and generates a difference model DM according to the object model OM and the deformed target model TM. The following describes the technical reasons for deforming the target model TM and then the operation of deforming the target model TM.

[0271] As described above, a part of the workpiece W may be damaged as the workpiece W is used. On the other hand, in addition to or instead of the damage to a part of the workpiece W, at least a part of the workpiece W may be physically deformed as the workpiece W is used. For example, the workpiece W may be physically deformed due to a load applied to the workpiece W used in a high temperature environment. In other words, creep may occur. For example, the workpiece W may be physically deformed due to aging or the like.

[0272] In the case where the workpiece W is deformed, "the use of the workpiece W" may include the use of the workpiece W to the extent that the workpiece W is deformed. For example, "the use of the workpiece W" may include the long-term use of the workpiece W to the extent that the workpiece W is deformed. Therefore, the workpiece W that has been actually used may include the workpiece W that has been used to the extent that the workpiece W is deformed. On the other hand, the workpiece W before actual use may include the workpiece W that has been used but has not been used to the extent that the workpiece W is deformed. For example, the workpiece W before actual use may include the short-term use of the workpiece W to the extent that the workpiece W is not deformed (for example, the test run of the workpiece W or a product including the workpiece W, etc.).

[0273] In addition, when a part of the workpiece W is damaged (for example, worn) as the workpiece W is used, the three-dimensional shape of the workpiece W is changed, so it can also be said that the workpiece W is deformed. However, in the present embodiment, "deformation of the workpiece W" refers to deformation of the workpiece W that occurs as the workpiece W is used, and refers to deformation of the workpiece W caused by a cause different from the damage of the workpiece W.

[0274] Here, as described above, the object model OM represents the three-dimensional shape of the workpiece W that is actually used. Therefore, when the workpiece W is deformed as the workpiece W is used, the object model OM generated when the workpiece W is deformed is schematically shown. Figure 24 As shown on the right side of , the object model OM represents the actual three-dimensional shape of the workpiece W that has been deformed as the workpiece W is used. That is, the deformation of the workpiece W that occurs as the workpiece W is used is reflected in the object model OM. On the other hand, as described above, the three-dimensional shape of the workpiece W before actual use is shown in the target model TM. Therefore, any deformation of the workpiece W that occurs as the workpiece W is used is not reflected in the target model TM. Therefore, Figure 24 As shown on the left side of , the target model TM represents the target shape of the undeformed workpiece W.

[0275] In this case, although the three-dimensional shape of the original object model OM should be the same as the three-dimensional shape of the corresponding model part CMP corresponding to the object model OM in the target model TM, due to the deformation of the workpiece W, such as Figure 24 As shown in FIG. 1 , the three-dimensional shape of the object model OM is different from the three-dimensional shape of the corresponding model part CMP of the target model TM. As a result, as shown schematically in FIG. 1 , the differential model DM generated when the workpiece W is deformed is Figure 25As shown, the differential model DM equivalent to the difference between the target model TM and the object model OM represents a three-dimensional shape different from the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should shape by performing additional processing. For example, the differential model DM represents a three-dimensional shape different from the three-dimensional shape of the defective part of the workpiece W. As a result, when generating machining control information based on such a differential model DM, the machining device 1 will shape a three-dimensional structure ST with a shape different from the desired shape. That is, the machining device 1 cannot shape a three-dimensional structure ST with the desired shape. For example, the machining device 1 cannot shape a three-dimensional structure ST that can appropriately fill the defective part. Thus, when the workpiece W is deformed as it is used, there is a technical problem that the measurement system 2 (in particular, the control information generation device 22) may not be able to generate machining control information for controlling the machining device 1 in such a way as to shape a three-dimensional structure ST with the desired shape.

[0276] Therefore, in the third modification, to solve the above technical problem, the model generation unit 2211 deforms the target model TM that does not reflect the deformation of the workpiece W according to the object model OM that reflects the deformation of the workpiece W. For example, as shown in the figure showing the undeformed target model TM and the deformed target model TM Figure 26 As shown, the model generation unit 2211 may also deform the target model TM according to the deformation of the workpiece W. That is, the model generation unit 2211 may deform the target model TM that does not reflect the deformation of the workpiece W according to the actual shape of the workpiece W represented by the object model OM (i.e., the shape of the deformed workpiece W). In addition, the undeformed target model TM may be referred to as a reference model to distinguish it from the deformed target model TM.

[0277] After the target model TM is deformed, the model generation unit 2211 generates a differential model DM based on the object model OM and the deformed target model TM. Specifically, as shown in Figure 27 As shown, the model generation unit 2211 may also generate a three-dimensional model equivalent to the difference between the deformed target model TM and the object model OM as the differential model DM. As a result, as shown in Figure 27 As shown, the three-dimensional shape represented by the differential model DM equivalent to the difference between the undeformed target model TM and the object model OM (refer to Figure 25)Compared with [description missing in the original], the differential model DM, which is equivalent to the difference between the deformed target model TM and the object model OM, is closer to the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should model. In this way, in the third modification example, compared with the case where the differential model DM is generated using the undeformed target model TM, the possibility of a decrease in the accuracy of the differential model DM is reduced in the situation where the workpiece W is deformed. That is, the possibility that the differential model DM appropriately shows the three-dimensional shape of the three-dimensional structure ST that the processing device 1 should model so that the three-dimensional shape of the workpiece W becomes the target shape is increased. Therefore, even when the workpiece W is deformed as it is used, the control information generation device 22 can appropriately generate the processing control information for controlling the processing device 1 so as to model the three-dimensional structure ST having the desired shape. Therefore, even when the workpiece W is deformed as it is used, the processing device 1 can model the three-dimensional structure ST having the desired shape.

[0278] In addition, the third modification example can also be combined with at least one of the first to second modification examples described above. That is, in at least one of the first to second modification examples described above, as described in the third modification example, the model generation unit 2211 can deform the target model TM according to the object model OM and generate the differential model DM based on the object model OM and the deformed target model TM.

[0279] (4-4) Fourth modification example

[0280] In the fourth modification example, the model generation unit 2211 can also deform at least a part of the object model OM to generate the deformed object model OM as the target model TM. In this case, even when the target model TM cannot be obtained in Figure 8 step S3, the model generation unit 2211 can generate the target model TM.

[0281] As shown in Figure 28 showing the deformed object model OM, the model generation unit 2211 can also deform the object model OM by stretching at least a part of the object model OM. That is, the model generation unit 2211 can also deform the object model OM by enlarging at least a part of the object model OM. For example, the model generation unit 2211 can also deform the object model OM by uniformly moving a desired distance the first surface of the object model OM in a specified direction. As an example, Figure 28 shows an example in which the model generation unit 2211 deforms the object model OM by uniformly moving a desired distance the upper surface of the object model OM (for example, the surface on the opposite side of the bottom surface as the reference part). In particular, Figure 28An example is shown in which the model generation unit 2211 deforms the object model OM in such a way that the upper surface of the object model OM uniformly moves a desired distance along the normal direction of the upper surface. In this case, it can also be regarded that the model generation unit 2211 deforms the object model OM in such a way that the object model OM is stretched along the length direction. As another example, the model generation unit 2211 can also deform the object model OM in such a way that a surface other than the upper and lower surfaces of the object model OM (for example, a side surface) uniformly moves a desired distance. The model generation unit 2211 can also deform the object model OM in such a way that a surface other than the upper and lower surfaces of the object model OM (for example, a side surface) uniformly moves a desired distance along the normal direction of the surface. In this case, it can also be regarded that the model generation unit 2211 deforms the object model OM in such a way that the object model OM is stretched along the width direction.

[0282] The model generation unit 2211 can also deform the object model OM in such a way that at least a part of the object model OM shrinks. That is, the model generation unit 2211 can also deform the object model OM in such a way that at least a part of the object model OM is reduced. For example, the model generation unit 2211 can also deform the object model OM in such a way that the object model OM shrinks along the length direction. For example, the model generation unit 2211 can also deform the object model OM in such a way that the object model OM shrinks along the width direction.

[0283] Furthermore, in the case where the object model OM is deformed in such a way that the first surface of the object model OM uniformly moves a desired distance, the model generation unit 2211 can also deform the object model OM in such a way that the second surface of the object model OM, which is different from and connected to the first surface, follows the movement of the first surface. In Figure 28 the example shown, the model generation unit 2211 can also deform the object model OM in such a way that the side surface of the object model OM (in particular, the upper end portion of the side surface connected to the upper surface) follows the upward movement of the upper surface of the object model OM and moves upward (typically extends).

[0284] The model generation unit 2211 can also generate the target model TM by removing a part of the object model OM after deforming the object model OM in such a way that the first surface of the object model OM moves a distance longer than the desired distance. For example, the model generation unit 2211 can also remove a part of the object model OM by performing a Boolean operation (for example, a Boolean operation to obtain a difference) between the deformed object model OM and a model having the shape of the surface of the target model TM assumed.

[0285] In the case of generating the target model TM by deforming the object model OM in this way, as Figure 29As shown, the model generation unit 2211 can also generate a three-dimensional model equivalent to the difference between the target model TM and the object model OM as the difference model DM. That is, the model generation unit 2211 can also generate a three-dimensional model equivalent to the difference between the target model TM generated by deforming the object model OM and the undeformed object model OM as the difference model DM.

[0286] In such a fourth modification example, when the upper surface of the workpiece W (for example, the tip of the turbine blade) is evenly worn, the model generation unit 2211 can easily generate the target model TM by deforming the object model OM as Figure 28 shown. Therefore, the model generation unit 2211 can generate the difference model DM without obtaining the target model TM.

[0287] On the other hand, when a crack occurs on the upper surface of the workpiece W or the upper surface of the workpiece W is locally worn, even if the object model OM is simply deformed as Figure 28 shown, the deformed object model OM may not be usable as the target model TM that accurately represents the target shape of the workpiece W. In such a case, the model generation unit 2211 can also specify a part of the upper surface of the object model OM as the first surface that is deformed as Figure 28 shown, and specify another part of the upper surface of the object model OM as the second surface that is not deformed as Figure 28 shown. As a result, even when a crack occurs on the upper surface of the workpiece W or the upper surface of the workpiece W is locally worn, the model generation unit 2211 can generate the target model TM that accurately represents the target shape of the workpiece W by deforming a part of the upper surface of the object model OM as Figure 28 shown. In addition, when it is not easy to generate the target model TM that accurately represents the target shape of the workpiece W by deforming the object model OM, the model generation unit 2211 can also use the method shown in the third modification example to generate the target model TM.

[0288] In addition, the fourth modification example can also be combined with at least one of the above first to third modification examples. That is, in at least one of the above first to third modification examples, as described in the fourth modification example, the model generation unit 2211 can generate the deformed object model OM as the target model TM by deforming at least a part of the object model OM.

[0289] (4-5) Fifth modification example

[0290] In the fifth modification example, as shown in the extraction point display image 51 showing the fifth modification example Figure 30As shown, the extraction point display image 51 may also include a display image 512, which includes a plurality of operation objects 5121. The plurality of operation objects 5121 are display objects that can be operated by the user to adjust the inter-model distance threshold TH_M, which is used to extract a plurality of extraction points Pext from a corresponding one of a plurality of different regions of the target model TM among the plurality of points Ptm included in the region.

[0291] In Figure 30 In the example shown, the display image 512 includes two operation objects 5121 (specifically, operation object 5121#1 and operation object 5121#2). The operation object 5121#1 is a display object that can be operated by the user to adjust the inter-model distance threshold TH_M, which is used to extract a plurality of extraction points Pext from the plurality of points Ptm included in the first region 514#1 of the target model TM. The operation object 5121#2 is a display object that can be operated by the user to adjust the inter-model distance threshold TH_M, which is used to extract a plurality of extraction points Pext from the plurality of points Ptm included in the second region 514#2 of the target model TM different from the first region 514#1.

[0292] In this case, the model generation unit 2211 may also use the inter-model distance threshold TH_M adjusted by the user using the operation object 5121#1 to extract a plurality of extraction points Pext from the plurality of points Ptm included in the first region 514#1 of the target model TM. On the other hand, the model generation unit 2211 may also not use the inter-model distance threshold TH_M adjusted by the user using the operation object 5121#2 in order to extract a plurality of extraction points Pext from the plurality of points Ptm included in the first region 514#1 of the target model TM. Similarly, the model generation unit 2211 may also use the inter-model distance threshold TH_M adjusted by the user using the operation object 5121#2 to extract a plurality of extraction points Pext from the plurality of points Ptm included in the second region 514#2 of the target model TM. On the other hand, the model generation unit 2211 may also not use the inter-model distance threshold TH_M adjusted by the user using the operation object 5121#1 in order to extract a plurality of extraction points Pext from the plurality of points Ptm included in the second region 514#2 of the target model TM.

[0293] As an example, when the user adjusts the inter-model distance threshold TH_M in such a way that the inter-model distance threshold TH_M becomes the first inter-model distance threshold TH_M#51 by using the operation object 5121#1, the model generation unit 2211 can also use the first inter-model distance threshold TH_M#51 to extract a plurality of extraction points Pext from a plurality of points Ptm included in the first region 514#1 of the target model TM. On the other hand, the model generation unit 2211 may not use the first inter-model distance threshold TH_M#51 in order to extract a plurality of extraction points Pext from a plurality of points Ptm included in the second region 514#2 of the target model TM.

[0294] As another example, when the user adjusts the inter-model distance threshold TH_M in such a way that the inter-model distance threshold TH_M becomes the second inter-model distance threshold TH_M#52 by using the operation object 5121#2, the model generation unit 2211 can also use the second inter-model distance threshold TH_M#52 to extract a plurality of extraction points Pext from a plurality of points Ptm included in the second region 514#2 of the target model TM. On the other hand, the model generation unit 2211 may not use the second inter-model distance threshold TH_M#52 in order to extract a plurality of extraction points Pext from a plurality of points Ptm included in the first region 514#1 of the target model TM.

[0295] In the fifth modification example, as shown in the point group clustering display image 52 showing the fifth modification example Figure 31 shown, the point group clustering display image 52 may further include a display image 522, and the display image 522 includes a plurality of operation objects 5221. The plurality of operation objects 5221 are respectively display objects that can be operated by the user to adjust the clustering threshold TH_C, and the clustering threshold TH_C is used for clustering a plurality of extraction points Pext included in a corresponding one of different regions of the target model TM.

[0296] In Figure 31 the example shown, the display image 522 includes two operation objects 5221 (specifically, the operation object 5221#1 and the operation object 5221#2). The operation object 5221#1 is a display object that can be operated by the user to adjust the clustering threshold TH_C, and the clustering threshold TH_C is used for clustering a plurality of extraction points Pext included in the first region 524#1 of the target model TM. The operation object 5221#2 is a display object that can be operated by the user to adjust the clustering threshold TH_C, and the clustering threshold TH_C is used for clustering a plurality of extraction points Pext included in the second region 524#2 of the target model TM different from the first region 524#1.

[0297] In this case, the model generation unit 2211 can also use the clustering threshold TH_C adjusted by the user using the operation object 5221#1 to perform clustering on the multiple extraction points Pext included in the first region 524#1 of the target model TM. On the other hand, the model generation unit 2211 may not use the clustering threshold TH_C adjusted by the user using the operation object 5221#2 in order to perform clustering on the multiple extraction points Pext included in the first region 524#1 of the target model TM. Similarly, the model generation unit 2211 can also use the clustering threshold TH_C adjusted by the user using the operation object 5221#2 to perform clustering on the multiple extraction points Pext included in the second region 524#2 of the target model TM. On the other hand, the model generation unit 2211 may not use the clustering threshold TH_C adjusted by the user using the operation object 5221#1 in order to perform clustering on the multiple extraction points Pext included in the second region 524#2 of the target model TM.

[0298] As an example, when the user uses the operation object 5221#1 to adjust the clustering threshold TH_C so that the clustering threshold TH_C becomes the first clustering threshold TH_C#51, the model generation unit 2211 can also use the first clustering threshold TH_C#51 to perform clustering on the multiple extraction points Pext included in the first region 524#1 of the target model TM. On the other hand, the model generation unit 2211 may not use the first clustering threshold TH_C#51 in order to perform clustering on the multiple extraction points Pext included in the second region 524#2 of the target model TM.

[0299] As another example, when the user uses the operation object 5221#2 to adjust the clustering threshold TH_C so that the clustering threshold TH_C becomes the second clustering threshold TH_C#52, the model generation unit 2211 can also use the second clustering threshold TH_C#52 to perform clustering on the multiple extraction points Pext included in the second region 524#2 of the target model TM. On the other hand, the model generation unit 2211 may not use the second clustering threshold TH_C#52 in order to perform clustering on the multiple extraction points Pext included in the first region 524#1 of the target model TM.

[0300] In addition, the fifth modification example can also be combined with at least one of the first to fourth modification examples described above. That is, in at least one of the first to fourth modification examples described above, as described in the fifth modification example, the extraction point display image 51 may include the display image 512, the display image 512 includes a plurality of operation objects 5121, and the point group clustering display image 52 includes the display image 522, the display image 522 includes a plurality of operation objects 5221.

[0301] (4-6) Sixth Modification Example

[0302] In the sixth modification example, the model generation unit 2211 may, in addition to or instead of controlling the display device 25 to display the extraction point display image 51 and the point group clustering display image 52, control the display device 25 to display a combined display image 53 formed by combining the extraction point display image 51 and the point group clustering display image 52.

[0303] Figure 32 An example of the combined display image 53 is shown. As Figure 32 shown, the combined display image 53 may also include a display image 531. The display image 531 is an image for displaying a plurality of extraction points Pext extracted by the model generation unit 2211 and at least one point group clustering PGC generated by the model generation unit 2211. The display mode of the plurality of extraction points Pext in the display image 531 of the combined display image 53 may be the same as or different from the display mode of the plurality of extraction points Pext in the display image 511 of the above-mentioned extraction point display image 51. The display mode of at least one point group clustering PGC in the display image 531 of the combined display image 53 may be the same as or different from the display mode of at least one point group clustering PGC in the display image 521 of the above-mentioned point group clustering display image 52.

[0304] As Figure 32 shown, the combined display image 53 may further include a display image 532. The display image 532 is an image for displaying an operation object 5121 that can be operated by the user to adjust the inter-model distance threshold TH_M for extracting the extraction points Pext and an operation object 5221 that can be operated by the user to adjust the clustering threshold TH_C for generating at least one point group clustering PGC. The display mode of the operation object 5121 in the display image 532 of the combined display image 53 may be the same as or different from the display mode of the operation object 5121 in the display image 512 of the above-mentioned extraction point display image 51. The display mode of the operation object 5221 in the display image 532 of the combined display image 53 may be the same as or different from the display mode of the operation object 5221 in the display image 522 of the above-mentioned point group clustering display image 52.

[0305] In such a sixth modification example, since the combined display image 53 is displayed, the user can repeat the adjustment of the inter-model distance threshold TH_M and the adjustment of the clustering threshold TH_C the required number of times without switching the image displayed on the display device 25. In this case, it can also be substantially regarded that the model generation unit 2211 controls the display device 25 to simultaneously display the extraction point display image 51 and the point group clustering display image 52.

[0306] In addition, the sixth modification example can also be combined with at least one of the first to fifth modification examples described above. That is, in at least one of the first to fifth modification examples described above, the model generation unit 2211 can also control the display device 25 to display the combined display image 53 as described in the sixth modification example.

[0307] (4-7) Seventh Modification Example

[0308] In the seventh modification example, as Figure 33 shown, the extraction point display image 51 may further include at least one display image 513 in addition to the above-described display images 511 and 512. The display image 513 is an image for displaying a plurality of extraction points Pext extracted when the inter-model distance threshold TH_M is set to a predetermined preset value (in other words, a candidate value or a tentative value) corresponding to the display image 513.

[0309] In Figure 33 the example shown, the extraction point display image 51 includes two display images 513 (specifically, display image 513#71 and display image 513#72). The display image 513#71 displays a plurality of extraction points Pext extracted when the inter-model distance threshold TH_M is set to the first preset value TH_D#71. The display image 513#72 displays a plurality of extraction points Pext extracted when the inter-model distance threshold TH_M is set to a second preset value TH_D#72 different from the first preset value TH_D#71. However, the extraction point display image 51 may include a single display image 513 or three or more display images 513.

[0310] The user can also use the input device 24 to select any one of the plurality of display images 513, thereby adjusting the inter-model distance threshold TH_M. In this case, the input device 24 can also accept an input in which the user selects the display image 513 as an input for adjusting the inter-model distance threshold TH_M. Specifically, when the user uses the input device 24 to select any one of the plurality of display images 513, the model generation unit 2211 can also set the inter-model distance threshold TH_M to a preset value corresponding to the display image 513 selected by the user. For example, when the user selects Figure 33 the shown display image 513#71, the model generation unit 2211 can also set the inter-model distance threshold TH_M to the first preset value TH_D#71 corresponding to the display image 513#71 selected by the user. For example, when the user selects Figure 33In the case of the displayed image 513#72 shown, the model generation unit 2211 may also set the inter-model distance threshold TH_M to the first preset value TH_D#72 corresponding to the displayed image 513#72 selected by the user. In this case, the user can adjust the inter-model distance threshold TH_M while confirming the displayed image 513 showing the extraction results of multiple extraction points Pext. Therefore, the user can intuitively adjust the inter-model distance threshold TH_M.

[0311] The user may also adjust the inter-model distance threshold TH_M by operating the operation object 5121 after adjusting the inter-model distance threshold TH_M by selecting the displayed image 513. That is, the user may also adjust the inter-model distance threshold TH_M by selecting the displayed image 513 before adjusting the inter-model distance threshold TH_M by operating the operation object 5121. Therefore, the displayed image 513 may also be displayed before the user operates the operation object 5121. In this case, the user may also finely adjust the inter-model distance threshold TH_M by operating the operation object 5121 after roughly adjusting the inter-model distance threshold TH_M by selecting the displayed image 513. As a result, compared with the case of adjusting the inter-model distance threshold TH_M without selecting the displayed image 513, the user can relatively easily adjust the inter-model distance threshold TH_M to extract appropriate extraction points Pext.

[0312] In the case where the inter-model distance threshold TH_M is adjusted by the user selecting the displayed image 513, similarly to the case where the inter-model distance threshold TH_M is adjusted by the user operating the operation object 5121, the model generation unit 2211 may also use the inter-model distance threshold TH_M adjusted by the user to extract multiple extraction points Pext ( Figure 14 step S413). For example, in the case where the user selects Figure 33 the displayed image 513#71 shown, the model generation unit 2211 may also, in Figure 14 step S413, extract multiple extraction points Pext by using the first preset value TH_D#71 as the inter-model distance threshold TH_M. For example, in the case where the user selects Figure 33 the displayed image 513#72 shown, the model generation unit 2211 may also, in Figure 14 step S413, extract multiple extraction points Pext by using the second preset value TH_D#72 as the inter-model distance threshold TH_M. Then, the model generation unit 2211 may also update the extraction point display image 51( Figure 14Step S414). In particular, the model generation unit 211 may also update the display image 511, which displays a plurality of extraction points Pext extracted using the inter-model distance threshold TH_M adjusted by the user. Figure 14 Step S414).

[0313] The plurality of extraction points Pext displayed in the display image 513 may also be extracted using the reduced model M_reduced described in the first modification example. In this case, the processing load required to display the display image 513 (specifically, the processing load required to extract a plurality of extraction points Pext using the preset value of the inter-model distance threshold TH_M) does not become too high. On the other hand, the plurality of extraction points Pext displayed in the display image 511 may also be extracted using the original model M_original described in the first modification example.

[0314] In addition, the data size of the display image 513 displayed using the reduced model M_reduced may also be smaller than the data size of the display image 511 displayed using the original model M_original. Within the extraction point display image 51, the size of the display image 513 (e.g., the vertical and horizontal sizes) may also be smaller than the size of the display image 511. Therefore, the display image 513 displayed using the reduced model M_reduced may also be referred to as a reduced image.

[0315] In addition, the seventh modification example may also be combined with at least one of the above first to sixth modification examples. That is, in at least one of the above first to sixth modification examples, as described in the seventh modification example, the extraction point display image 51 may include at least one display image 513 in addition to the above display images 511 and 512.

[0316] (4-8) Eighth Modification Example

[0317] In the eighth modification example, as Figure 34 shown, the point group clustering display image 52 may also include at least one display image 523 in addition to the above display images 521 and 522. The display image 523 is an image for displaying at least one point group clustering PGC generated when the clustering threshold TH_C is set to a specified preset value (in other words, a candidate value or a tentative value) corresponding to the display image 523.

[0318] In Figure 34In the example shown, the dot cluster display image 52 includes two display images 523 (specifically, display image 523#81 and display image 523#82). The display image 523#71 shows at least one dot cluster PGC generated when the clustering threshold TH_C is set to the first preset value TH_C#81. The display image 523#72 shows at least one dot cluster PGC generated when the clustering threshold TH_C is set to a second preset value TH_C#82 different from the first preset value TH_C#81. However, the dot cluster display image 52 may include a single display image 523 or may include three or more display images 523.

[0319] The user can also adjust the clustering threshold TH_C by selecting any one of the plurality of display images 523 using the input device 24. In this case, the input device 24 can also accept an input in which the user selects the display image 523 as an input for adjusting the clustering threshold TH_C. Specifically, when the user selects any one of the plurality of display images 523 using the input device 24, the model generation unit 2211 can also set the clustering threshold TH_C to a preset value corresponding to the display image 523 selected by the user. For example, when the user selects Figure 34 the shown display image 523#81, the model generation unit 2211 can also set the clustering threshold TH_C to the first preset value TH_C#81 corresponding to the display image 523#81 selected by the user. For example, when the user selects Figure 34 the shown display image 523#82, the model generation unit 2211 can also set the clustering threshold TH_C to the first preset value TH_C#82 corresponding to the display image 523#82 selected by the user. In this case, the user can adjust the clustering threshold TH_C while confirming the display image 523 showing the generation result of at least one dot cluster PGC. Therefore, the user can intuitively adjust the clustering threshold TH_C.

[0320] The user can also adjust the clustering threshold TH_C by operating on the operation object 5221 after adjusting the clustering threshold TH_C by selecting the display image 523. That is, the user can also adjust the clustering threshold TH_C by selecting the display image 523 before adjusting the clustering threshold TH_C by operating on the operation object 5221. Therefore, the display image 523 can also be displayed before the user operates on the operation object 5221. In this case, the user can also finely adjust the clustering threshold TH_C by operating on the operation object 5221 after roughly adjusting the clustering threshold TH_C by selecting the display image 523. As a result, compared with the case of adjusting the clustering threshold TH_C without selecting the display image 523, the user can relatively easily adjust the clustering threshold TH_C to generate an appropriate point group clustering PGC.

[0321] In the case where the clustering threshold TH_C is adjusted by the user selecting the display image 523, similarly to the case where the clustering threshold TH_C is adjusted by the user operating on the operation object 5221, the model generation unit 2211 can also use the clustering threshold TH_C adjusted by the user to generate at least one point group clustering PGC ( Figure 14 step S423). For example, when the user selects Figure 34 the display image 523#81 shown, the model generation unit 2211 can also Figure 14 in step S423, generate at least one point group clustering PGC by using the first preset value TH_C#81 as the clustering threshold TH_C. For example, when the user selects Figure 34 the display image 523#82 shown, the model generation unit 2211 can also Figure 14 in step S423, generate at least one point group clustering PGC by using the second preset value TH_C#82 as the clustering threshold TH_C. Then, the model generation unit 2211 can also update the point group clustering display image 52 ( Figure 14 step S424). In particular, the model generation unit 211 can also update the display image 521 that displays at least one point group clustering PGC generated by using the clustering threshold TH_C adjusted by the user ( Figure 14 step S424).

[0322] At least one point group cluster PGC displayed in the display image 523 can also be generated using the reduced model M_reduced described in the first modification example. In this case, the processing load required to display the display image 523 (specifically, the processing load required to generate at least one point group cluster PGC using the preset value of the clustering threshold TH_C) does not become excessive. On the other hand, at least one point group cluster PGC displayed in the display image 521 can also be extracted using the original model M_original described in the first modification example.

[0323] In addition, the data size of the display image 523 displayed using the reduced model M_reduced can also be smaller than the data size of the display image 521 displayed using the original model M_original. Within the point group cluster display image 52, the size of the display image 523 (e.g., the vertical and horizontal sizes) can also be smaller than the size of the display image 521. Therefore, the display image 523 displayed using the reduced model M_reduced can also be referred to as a reduced image.

[0324] In addition, the eighth modification example can also be combined with at least one of the first to seventh modification examples described above. That is, in at least one of the first to seventh modification examples described above, like in the eighth modification example, the point group cluster display image 52 can include at least one display image 523 in addition to the above-mentioned display image 521 and display image 522.

[0325] (4-9) Ninth modification example

[0326] In the ninth modification example, similar to the sixth modification example, a combined display image 53 can also be displayed. However, in the ninth modification example, as Figure 35 shown, the combined display image 53 can also include at least one display image 533 in addition to the above-mentioned display image 531 and display image 532. The display image 533 is an image for displaying a plurality of extracted points Pext extracted when the inter-model distance threshold TH_M is set to a specified preset value corresponding to the display image 533 and the clustering threshold TH_C is set to a specified preset value corresponding to the display image 533. In addition, the display image 533 is an image for displaying at least one point group cluster PGC generated when the inter-model distance threshold TH_M is set to a specified preset value corresponding to the display image 533 and the clustering threshold TH_C is set to a specified preset value corresponding to the display image 533.

[0327] In Figure 35In the example shown, the combined display image 53 includes four display images 533 (specifically, display image 533#91, display image 533#92, display image 533#93, display image 533#94). The display image 533#91 shows a plurality of extracted points Pext extracted when the inter-model distance threshold TH_M is set to the first preset value TH_D#91 and the clustering threshold TH_C is set to the first preset value TH_C#91. In addition, the display image 533#91 shows at least one point group clustering PGC generated when the inter-model distance threshold TH_M is set to the first preset value TH_D#91 and the clustering threshold TH_C is set to the first preset value TH_C#91. The display image 533#92 shows a plurality of extracted points Pext extracted when the inter-model distance threshold TH_M is set to the first preset value TH_D#91 and the clustering threshold TH_C is set to a second preset value TH_C#92 different from the first preset value TH_C#91. In addition, the display image 533#92 shows at least one point group clustering PGC generated when the inter-model distance threshold TH_M is set to the first preset value TH_D#91 and the clustering threshold TH_C is set to the second preset value TH_C#92. The display image 533#93 shows a plurality of extracted points Pext extracted when the inter-model distance threshold TH_M is set to a second preset value TH_D#92 different from the first preset value TH_D#91 and the clustering threshold TH_C is set to the first preset value TH_C#91. In addition, the display image 533#93 shows at least one point group clustering PGC generated when the inter-model distance threshold TH_M is set to the second preset value TH_D#92 and the clustering threshold TH_C is set to the first preset value TH_C#91. The display image 533#94 shows a plurality of extracted points Pext extracted when the inter-model distance threshold TH_M is set to the second preset value TH_D#92 and the clustering threshold TH_C is set to the second preset value TH_C#92. In addition, the display image 533#94 shows at least one point group clustering PGC generated when the inter-model distance threshold TH_M is set to the second preset value TH_D#92 and the clustering threshold TH_C is set to the second preset value TH_C#92.

[0328] The user can also use the input device 24 to select any one of the multiple display images 533, thereby adjusting the inter-model distance threshold TH_M and the clustering threshold TH_C. In this case, the input device 24 can also accept the input of the user's selection of the display image 533 as the input for adjusting the inter-model distance threshold TH_M and the clustering threshold TH_C. Specifically, when the user uses the input device 24 to select any one of the multiple display images 533, the model generation unit 2211 can also set the inter-model distance threshold TH_M to a preset value corresponding to the display image 533 selected by the user, and set the clustering threshold TH_C to a preset value corresponding to the display image 533 selected by the user. For example, when the user selects Figure 35 the display image 533#91 shown, the model generation unit 2211 can also set the inter-model distance threshold TH_M to the first preset value TH_D#91 corresponding to the display image 533#91 selected by the user, and set the clustering threshold TH_C to the first preset value TH_C#91 corresponding to the display image 533#91 selected by the user. For example, when the user selects Figure 35 the display image 533#92 shown, the model generation unit 2211 can also set the inter-model distance threshold TH_M to the first preset value TH_D#91 corresponding to the display image 533#92 selected by the user, and set the clustering threshold TH_C to the second preset value TH_C#92 corresponding to the display image 533#92 selected by the user. For example, when the user selects Figure 35 the display image 533#93 shown, the model generation unit 2211 can also set the inter-model distance threshold TH_M to the second preset value TH_D#92 corresponding to the display image 533#93 selected by the user, and set the clustering threshold TH_C to the first preset value TH_C#91 corresponding to the display image 533#93 selected by the user. For example, when the user selects Figure 35 the display image 533#94 shown, the model generation unit 2211 can also set the inter-model distance threshold TH_M to the second preset value TH_D#92 corresponding to the display image 533#94 selected by the user, and set the clustering threshold TH_C to the second preset value TH_C#92 corresponding to the display image 533#94 selected by the user.

[0329] The user can also adjust the inter-model distance threshold TH_M and the clustering threshold TH_C by operating on the operation object 5121 after adjusting the inter-model distance threshold TH_M and the clustering threshold TH_C by selecting the display image 533. That is, the user can also adjust the inter-model distance threshold TH_M by operating on the operation object 5121 and adjust the clustering threshold TH_C by operating on the operation object 5221 before adjusting the inter-model distance threshold TH_M and the clustering threshold TH_C by selecting the display image 533. Therefore, the display image 533 can also be displayed before the user operates on the operation object 5221. In this case, the user can also finely adjust the inter-model distance threshold TH_M by operating on the operation object 5121 and finely adjust the clustering threshold TH_C by operating on the operation object 5221 after roughly adjusting the inter-model distance threshold TH_M and the clustering threshold TH_C by selecting the display image 533. As a result, compared with the case of adjusting the inter-model distance threshold TH_M and the clustering threshold TH_C without selecting the display image 533, the user can relatively easily adjust the inter-model distance threshold TH_M and the clustering threshold TH_C to extract an appropriate extraction point Pext and generate an appropriate point group clustering PGC.

[0330] In the ninth modification example, similar to the seventh modification example, when the inter-model distance threshold TH_M is adjusted by the user selecting the display image 533, the model generation unit 2211 uses the inter-model distance threshold TH_M adjusted by the user to extract a plurality of extraction points Pext. Similarly, in the ninth modification example, similar to the seventh modification example, when the clustering threshold TH_C is adjusted by the user selecting the display image 533, the model generation unit 2211 uses the clustering threshold TH_C adjusted by the user to generate at least one point group clustering PGC. Then, the model generation unit 2211 can also update the merged display image 53.

[0331] The plurality of extraction points Pext displayed on the display image 533 can also be extracted using the reduced model M_reduced described in the first modification example. In this case, the processing load required to display the display image 533 (specifically, the processing load required to extract a plurality of extraction points Pext using the preset value of the inter-model distance threshold TH_M) does not become too high. On the other hand, the plurality of extraction points Pext displayed on the display image 531 can also be extracted using the original model M_original described in the first modification example.

[0332] Similarly, at least one point group cluster PGC displayed in the display image 533 can also be generated using the reduced model M_reduced described in the first modification example. In this case, the processing load required to display the display image 533 (specifically, the processing load required to generate at least one point group cluster PGC using the preset value of the clustering threshold TH_C) does not become excessive. On the other hand, at least one point group cluster PGC displayed in the display image 531 can also be generated using the original model M_original described in the first modification example.

[0333] In addition, the data size of the display image 533 displayed using the reduced model M_reduced can also be smaller than the data size of the display image 531 displayed using the original model M_original. In the combined display image 53, the size of the display image 533 (for example, the vertical and horizontal sizes) can also be smaller than the size of the display image 531. Therefore, the display image 533 displayed using the reduced model M_reduced can also be referred to as a reduced image.

[0334] In addition, the ninth modification example can also be combined with at least one of the above first to eighth modification examples. That is, in at least one of the above first to eighth modification examples, the combined display image 53 (or at least one of the point display image 51 and the point group cluster display image 52) can include at least one display image 533 as described in the ninth modification example.

[0335] (4-10) Tenth modification example

[0336] In the tenth modification example, as Figure 36 shown, when the feature amount of the object model OM and the feature amount of the target model TM are input, the model generation unit 2211 can also use a machine learning model LM capable of outputting a recommended value of at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C to set at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C. As an example of the machine learning model, a machine learning model using a neural network is listed. In this case, the model generation unit 2211 can also use the recommended value of the inter-model distance threshold TH_M output by the machine learning model LM to extract a plurality of extraction points Pext in Figure 14 step S410 or step S413. The model generation unit 2211 can also use the recommended value of the clustering threshold TH_C output by the machine learning model LM to generate at least one point group cluster PGC in Figure 14 step S420 or step S423.

[0337] When the machine learning model LM outputs a recommended value for at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C, the user may also not adjust at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C. Therefore, the effort for the user to adjust at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C is reduced. However, when the machine learning model LM outputs a recommended value for at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C, the user may also adjust at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C. For example, the user may also adjust at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C according to the recommended value for at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C output by the machine learning model LM.

[0338] As Figure 37 shown, the machine learning model LM can also be generated by machine learning using teacher data, which includes sample data and correct labels. The sample data includes the feature quantities of the object model OM and the target model TM, and the correct labels represent the correct value of at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C that should be set for the object model OM and the target model TM. In particular, the machine learning model LM can also be generated by machine learning using a large teacher data set that includes teacher data. In this case, machine learning can also be performed to reduce the error between the output of the machine learning model LM when the sample data included in the teacher data (specifically, the feature quantities of the object model OM and the target model TM) is input and the correct labels included in the teacher data.

[0339] Alternatively, the model generation unit 2211 may also generate a recommended value for at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C without using the machine learning model LM. For example, the model generation unit 2211 may generate a recommended value for the clustering threshold TH_C according to the above distance conditions. For example, the model generation unit 2211 may generate a recommended value for the inter-model distance threshold TH_M according to the conditions of the point cloud clustering PGC (e.g., at least one of the size and the number of the point cloud clustering PGC). In this case, the user may also not adjust at least one of the inter-model distance threshold TH_M and the clustering threshold TH_C.

[0340] The model generation unit 2211 may also automatically output or generate both the inter-model distance threshold TH_M and the clustering threshold TH_C. It is also possible that the model generation unit 2211 automatically outputs or generates the inter-model distance threshold TH_M, while the user manually adjusts the clustering threshold TH_C. It is also possible that the model generation unit 2211 automatically outputs or generates the clustering threshold TH_C, while the user manually adjusts the inter-model distance threshold TH_M. The operation mode of the model generation unit 2211 may also be able to switch between an automatic mode and a manual mode. In the automatic mode, the extraction point Pext is extracted using the inter-model distance threshold TH_M output or generated by the model generation unit 2211. In the manual mode, the extraction point Pext is extracted using the inter-model distance threshold TH_M adjusted by the user. The operation mode of the model generation unit 2211 may also be able to switch between an automatic mode and a manual mode. In the automatic mode, the clustering of the extraction point Pext is performed using the clustering threshold TH_C output or generated by the model generation unit 2211. In the manual mode, the clustering of the extraction point Pext is performed using the clustering threshold TH_C adjusted by the user.

[0341] (4-11) The 11th modification example

[0342] Next, in the above description, the processing device 1 performs additional processing on the workpiece W. On the other hand, in the 11th modification example, the processing device 1 may perform removal processing on the workpiece W in addition to or instead of performing additional processing on the workpiece W. That is, the processing device 1 may be able to perform removal processing for removing a part of the workpiece W. In addition, the processing device 1 may perform removal processing on the modeled object on the workpiece W modeled by the processing device 1 in addition to or instead of performing removal processing on the workpiece W.

[0343] When the processing device 1 performs removal processing on the workpiece W, the control information generation device 22 may also generate processing control information by performing the above-described control information generation operation (refer to Figure 8 ). However, when the processing device 1 performs removal processing on the workpiece W, as Figure 38 shows, the object model OM represents the three-dimensional shape of the workpiece W including the removed part RP removed by the removal processing, and the target model TM represents the three-dimensional shape of the workpiece W without the removed part RP. Therefore, in the 11th modification example, the difference model DM corresponding to the difference between the target model TM and the object model OM represents the three-dimensional shape of the part removed from the workpiece W by the removal processing.

[0344] In addition, in the 11th modification example, in Figure 14In step S410 or step S413 of the differential model generation operation shown, the model generation unit 2211 may also extract a plurality of points Pom that satisfy a specified distance condition from among the plurality of points Pom included in the object model OM as the plurality of extraction points Pext. The specified distance condition may also include the following condition: the distance D3 between one point Pom of the object model OM and one point Ptm of the closest point Pom of the target model TM is equal to or greater than a specified inter-model distance threshold TH_M. In this case, even when the machining device 1 performs removal machining, the above-described effects that can be enjoyed when the machining device 1 performs additional machining can be enjoyed.

[0345] In addition, as described above, the machining device 1 that performs additional machining may also perform additional machining as at least a part of a repair process for the workpiece W having a defective part. In this case, the machining device 1 that can perform removal machining may also perform removal machining as at least another part of the repair process. For example, the machining device 1 may also perform removal machining to remove a part of the shaped object attached to the workpiece W. When the machining device 1 performs removal machining, the machining device 1 may also machine the workpiece W using the principle of non-thermal machining (e.g., ablation machining). Alternatively, the machining device 1 that can perform melting machining may also perform melting machining as at least another part of the repair process. The melting machining may also include machining for melting the surface of the workpiece W or the shaped object and solidifying the melted surface. In addition, the melting machining may also be referred to as remelting machining. The machining device 1 may also perform planar machining by performing melting machining, and this planar machining is used to make the surface of the workpiece W or the shaped object closer to a plane than before the melting machining.

[0346] In addition, when the machining device 1 performs machining different from the additional machining and the removal machining, the control information generation device 22 may also generate machining control information by performing the above-described control information generation operation (see Figure 8 ). However, in this case, in Figure 14In step S410 or step S413 of the differential model generation operation shown, the model generation unit 2211 may also extract a plurality of points that satisfy a specified distance condition from among the plurality of points included in either the object model OM or the target model TM as the plurality of extraction points Pext. The specified distance condition may also include the following condition: the distance D4 between a first point in either the object model OM or the target model TM and a second point in the other of the object model OM and the target model TM that is closest to the first point is equal to or greater than a specified inter-model distance threshold TH_M. In this case, even when the processing device 1 performs processing different from additive processing and subtractive processing, the above-described effects that can be achieved when the processing device 1 performs at least one of additive processing and subtractive processing can be obtained.

[0347] In addition, not limited to the case where the processing device 1 processes the workpiece W, when generating a differential model corresponding to the difference between the first three-dimensional model and the second three-dimensional model, the Figure 14 differential model generation operation shown may be performed. However, in this case, it may also be that, in Figure 14 step S410 or step S413, the model generation unit 2211 extracts a plurality of points that satisfy a specified distance condition from among the plurality of points included in either the first three-dimensional model or the second three-dimensional model as the plurality of extraction points Pext. The specified distance condition may also include the following condition: the distance D5 between a first point in either the first three-dimensional model or the second three-dimensional model and a second point in the other of the first three-dimensional model and the second three-dimensional model that is closest to the first point is equal to or greater than a specified inter-model distance threshold TH_M. In this case, even when generating a differential model corresponding to the difference between the first three-dimensional model and the second three-dimensional model, the above-described effects can be obtained.

[0348] (4-12) Other modification examples

[0349] In the above description, the processing device 1 melts the modeling material M by irradiating the modeling material M with the processing light EL. However, the processing device 1 may also melt the modeling material M by irradiating the modeling material M with an arbitrary energy beam. As an example of the arbitrary energy beam, at least one of a charged particle beam and an electromagnetic wave, etc. is cited. As an example of the charged particle beam, at least one of an electron beam and an ion beam, etc. is cited.

[0350] In the above description, the processing apparatus 1 performs additional processing using the laser surfacing method. However, the processing apparatus 1 may also perform additional processing using an additional processing method different from the laser surfacing method. For example, the processing apparatus 1 may also perform additional processing using the powder bed fusion method. When the processing apparatus 1 performs additional processing using the powder bed fusion method, after the workpiece W measured by the measurement system 2 or the holding fixture holding the workpiece W is placed on the worktable 131 of the processing apparatus 1, the processing apparatus 1 only needs to fill the modeling material M around the workpiece W so that the uppermost surface of the modeling material M is located on the upper surface of the workpiece W to perform additional processing on the upper surface of the workpiece W.

[0351] (5) Supplementary Note

[0352] Regarding the embodiments described above, the following supplementary notes are further disclosed.

[0353] [Supplementary Note 1]

[0354] An information processing method for generating a differential model representing a portion added to an object to process the object into a target shape, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object, wherein,

[0355] The information processing method includes the following steps:

[0356] Receiving a first input as an input from a user;

[0357] Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model and the distance between the plurality of unit regions and the object model being greater than or equal to a first threshold set by the first input;

[0358] Receiving a second input as an input from the user;

[0359] Regarding a set of unit regions in which the distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold set by the second input as a clustering region, and obtaining at least one of the clustering regions; and

[0360] Outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

[0361] [Supplementary Note 2]

[0362] According to the information processing method described in Supplementary Note 1, wherein,

[0363] Generate machining control information according to the differential model, and control a machining device capable of machining the object in such a way that the shape of the object becomes the target shape by machining the object.

[0364] [Supplementary Note 3]

[0365] According to the information processing method described in Supplementary Note 2, wherein

[0366] The machining device is an additional machining device,

[0367] The additional machining of the object by the machining device is performed as at least a part of a repair process of the object.

[0368] [Supplementary Note 4]

[0369] According to the information processing method described in any one of Supplementary Notes 1 to 3, wherein

[0370] The information processing method further includes the following step: generating the target model by deforming at least a part of the reference model of the object according to the object model.

[0371] [Supplementary Note 5]

[0372] According to the information processing method described in any one of Supplementary Notes 1 to 3, wherein

[0373] The target model is generated by deforming at least a part of the object model.

[0374] [Supplementary Note 6]

[0375] According to the information processing method described in Supplementary Note 5, wherein

[0376] The deformation of the object model includes magnification of a part of the object model.

[0377] [Supplementary Note 7]

[0378] According to the information processing method described in any one of Supplementary Notes 1 to 6, wherein

[0379] The information processing method further includes the following steps:

[0380] Display a first display image, which includes the plurality of unit regions and a first operation object that can be operated by a user to adjust the first threshold; and

[0381] Display a second display image, which includes the clustering region and a second operation object that can be operated by the user to adjust the second threshold.

[0382] [Appendix 8]

[0383] The information processing method according to Appendix 7, wherein,

[0384] The information processing method further includes the following steps:

[0385] Updating a plurality of unit regions included in the first display image according to the operation of the first operation object; and

[0386] Updating the clustering region included in the second display image according to the operation of the second operation object.

[0387] [Appendix 9]

[0388] The information processing method according to Appendix 7 or Appendix 8, wherein,

[0389] By the user's adjustment of the second threshold, the number of the clustering regions included in the second display image changes.

[0390] [Appendix 10]

[0391] The information processing method according to any one of Appendices 7 to 9, wherein,

[0392] When the first threshold is set to the first candidate value, the first display image includes a first reduced image, and the first reduced image includes a plurality of unit regions obtained when the first threshold is set to a second candidate value different from the first candidate value.

[0393] [Appendix 11]

[0394] The information processing method according to Appendix 10, wherein,

[0395] After the first display image including the first reduced image is displayed, when the user designates the first reduced image in the first display image, the designation is used as the first input, and the first display image when the first threshold is set to the second candidate value is displayed.

[0396] [Appendix 12]

[0397] The information processing method according to any one of Appendices 7 to 11, wherein,

[0398] The information processing method further includes a step of displaying a first tentative image, and the first tentative image includes: an image including a plurality of unit regions obtained when the first threshold is set to a first tentative value; and an image including a plurality of unit regions obtained when the first threshold is set to a second tentative value,

[0399] When the user designates the image based on the first tentative value in the first tentative image, the designation is taken as the first input, and the first display image is displayed when the first threshold value is set to the first tentative value.

[0400] [Appendix 13]

[0401] The information processing method according to Appendix 12, wherein

[0402] The first tentative image is displayed before the first input is made.

[0403] [Appendix 14]

[0404] The information processing method according to any one of Appendices 7 to 13, wherein

[0405] The second display image when the second threshold value is set to the third candidate value includes a second reduced image, and the second reduced image includes a plurality of unit regions obtained when the second threshold value is set to a fourth candidate value different from the third candidate value.

[0406] [Appendix 15]

[0407] The information processing method according to Appendix 14, wherein

[0408] After the second display image including the second reduced image is displayed, when the user designates the second reduced image in the second display image, the designation is taken as the second input, and the first display image is displayed when the second threshold value is set to the fourth candidate value.

[0409] [Appendix 16]

[0410] The information processing method according to any one of Appendices 7 to 15, wherein

[0411] The information processing method further includes a step of displaying a second tentative image, which includes: an image of a clustering region obtained when the second threshold value is set to a third tentative value; and an image of a clustering region obtained when the second threshold value is set to a fourth tentative value,

[0412] When the user designates the image based on the third tentative value in the second tentative image, the designation is taken as the second input, and the second display image is displayed when the second threshold value is set to the third tentative value.

[0413] [Appendix 17]

[0414] The information processing method according to Note 16, wherein,

[0415] The second provisional image is displayed before the first input is performed.

[0416] [Note 18]

[0417] The information processing method according to any one of Notes 7 to 17, wherein,

[0418] The first display image and the second display image are displayed simultaneously.

[0419] [Note 19]

[0420] The information processing method according to any one of Notes 1 to 18, wherein,

[0421] The information processing method further includes a step of receiving a third input, which is an input of the user for specifying at least one of the clustering regions,

[0422] The at least one clustering region specified by the third input is output as the difference model or as information for generating the difference model.

[0423] [Note 20]

[0424] The information processing method according to any one of Notes 1 to 19, wherein,

[0425] The information processing method further includes a step of obtaining a thumbnail associated with the plurality of unit regions before obtaining the plurality of unit regions.

[0426] [Note 21]

[0427] The information processing method according to any one of Notes 1 to 20, wherein,

[0428] The information processing method further includes a step of obtaining a thumbnail associated with the clustering region before obtaining the clustering region.

[0429] [Note 22]

[0430] The information processing method according to any one of Notes 1 to 21, wherein,

[0431] The object is a first object,

[0432] The object model is a first object model,

[0433] The unit region is a first unit region,

[0434] The clustering region is the first clustering region,

[0435] The difference model is the first difference model,

[0436] Determine the first difference model according to the first determination value of the first threshold and the second determination value of the second threshold,

[0437] The information processing method further includes the following steps:

[0438] Obtain a plurality of second unit regions, each of the plurality of second unit regions corresponds to a part of the target model of the second object, and the distance between the plurality of second unit regions and the second object model representing the three-dimensional shape of the second object is greater than or equal to the first determination value;

[0439] Regard a set of second unit regions with a distance between two of the plurality of obtained second unit regions less than or equal to the second determination value as a second clustering region, and obtain at least one of the second clustering regions; and

[0440] Output at least one of the at least one second clustering region as a second difference model or as information for generating the second difference model.

[0441] [Supplementary Note 23]

[0442] According to the information processing method described in any one of Supplementary Notes 1 to 22, wherein,

[0443] By accepting the first input, set the first threshold and a third threshold different from the first threshold,

[0444] The step of obtaining the plurality of unit regions includes the following steps: obtain a plurality of unit regions with a distance greater than or equal to the first threshold from the object model from the plurality of unit regions included in the first region of the target model,

[0445] The information processing method further includes the following steps: obtain a plurality of unit regions with a distance greater than or equal to the third threshold from the object model from the plurality of unit regions included in the second region of the target model different from the first region.

[0446] [Supplementary Note 24]

[0447] According to the information processing method described in any one of Supplementary Notes 1 to 23, wherein,

[0448] By accepting the second input, set the second threshold and a fourth threshold different from the second threshold,

[0449] The steps of obtaining the clustering region include the following steps: obtaining, as the clustering region, a set of unit regions with the distance between two unit regions among the obtained multiple unit regions being below the second threshold value from the third region included in the obtained multiple unit regions.

[0450] The information processing method further includes the following steps: obtaining, as the clustering region, a set of unit regions with the distance between two unit regions among the obtained multiple unit regions being below the fourth threshold value from the multiple unit regions included in the fourth region different from the third region.

[0451] [Supplementary Note 25]

[0452] An information processing method for generating a differential model, which is a three-dimensional model representing the difference between an object model and a target model, where the object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Among them,

[0453] The information processing method includes the following steps:

[0454] Displaying a first display image that displays multiple unit regions and a first operation object that can be operated by the user to adjust the first threshold value. The multiple unit regions are obtained by subdividing the target model, and the distance between the multiple unit regions and the object model is above the first threshold value;

[0455] Regarding a set of unit regions with the distance between two unit regions among the multiple unit regions being below the second threshold value as one clustering region, and displaying a second display image that displays at least one of the clustering regions and a second operation object that can be operated by the user to adjust the second threshold value; and

[0456] Outputting at least one of the clustering regions obtained after operating the first operation object and the second operation object as the differential model or as information for generating the differential model.

[0457] [Supplementary Note 26]

[0458] According to the information processing method described in Supplementary Note 25, among them,

[0459] Updating the display of the first display image according to the operation of the first operation object,

[0460] Updating the display of the second display image according to the operation of the second operation object.

[0461] [Supplementary Note 27]

[0462] An information processing method for generating a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Wherein,

[0463] The information processing method includes the following steps:

[0464] Receiving a first input as the input of the user;

[0465] Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being greater than or equal to a first threshold set by the first input;

[0466] Regarding a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold as a clustering region, and obtaining at least one of the clustering regions; and

[0467] Outputting at least one of the clustering regions obtained according to the second threshold as the difference model or as information for generating the difference model.

[0468] [Appendix 28]

[0469] An information processing method for generating a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Wherein,

[0470] The information processing method includes the following steps:

[0471] Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being greater than or equal to a first threshold;

[0472] Receiving an input as the input of the user;

[0473] Regarding a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold set by the input as a clustering region, and obtaining at least one of the clustering regions; and

[0474] Outputting at least one of the clustering regions obtained after the reception of the input as the difference model or as information for generating the difference model.

[0475] [Appendix 29]

[0476] An information processing method for generating a differential model representing a portion removed from an object to process the object into a target shape, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object, wherein,

[0477] The information processing method includes the following steps:

[0478] Receiving a first input as an input from a user;

[0479] Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the object model, and the distance between the plurality of unit regions and the target model being equal to or greater than a first threshold set by the first input;

[0480] Receiving a second input as an input from the user;

[0481] Regarding a set of unit regions whose distance between two of the obtained plurality of unit regions is equal to or less than a second threshold set by the second input as a clustering region, and obtaining at least one of the clustering regions; and

[0482] Outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

[0483] [Supplementary Note 30]

[0484] An information processing method for generating a differential model representing a processed portion for processing an object into a target shape, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object, wherein,

[0485] The information processing method includes the following steps:

[0486] Receiving a first input as an input from a user;

[0487] Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of one of the object model and the target model, and the distance between the plurality of unit regions and the other of the object model and the target model being equal to or greater than a first threshold set by the first input;

[0488] Accept a second input that is the input of the user;

[0489] Regard a set of unit regions, where the distance between two of the obtained multiple unit regions is below a second threshold set by the second input, as a clustering region, and obtain at least one such clustering region; and

[0490] Output at least one of the clustering regions obtained after accepting the first input and accepting the second input as the difference model or as information for generating the difference model.

[0491] [Appendix 31]

[0492] A processing method that generates processing control information for processing the shape of an object into a target shape using a processing device capable of processing the object based on a difference model generated according to the information processing method described in any one of Appendices 1 to 30.

[0493] [Appendix 32]

[0494] An information processing method for generating a difference model, which is a three-dimensional model representing the difference between a first model and a second model, where

[0495] The information processing method includes the following steps:

[0496] Accept a first input that is the input of the user;

[0497] Obtain multiple unit regions, each of which corresponds to a part of one of the first model and the second model, and the distance between the multiple unit regions and the other of the first model and the second model is above a first threshold set by the first input;

[0498] Accept a second input that is the input of the user;

[0499] Regard a set of unit regions, where the distance between two of the obtained multiple unit regions is below a second threshold set by the second input, as a clustering region, and obtain at least one such clustering region; and

[0500] Output at least one of the clustering regions obtained after accepting the first input and accepting the second input as the difference model or as information for generating the difference model.

[0501] [Appendix 33]

[0502] An information processing method for generating a difference model, which is a three-dimensional model representing the difference between a first model and a second model, wherein,

[0503] The information processing method includes the following steps:

[0504] Obtain a plurality of unit regions, each of which corresponds to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model is equal to or greater than a first threshold;

[0505] Take a set of unit regions with a distance between two unit regions in the obtained plurality of unit regions being equal to or less than a second threshold as a clustering region, and obtain at least one such clustering region; and

[0506] Output at least one of the clustering regions obtained according to the second threshold as the difference model or as information for generating the difference model.

[0507] [Supplementary Note 34]

[0508] A display method for displaying a difference model, which is a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein,

[0509] The display method includes the following steps:

[0510] Display a plurality of unit regions, each of which corresponds to a part of the target model, and the distance between the plurality of unit regions and the object model is equal to or greater than a first threshold set by a first input as the input of the user;

[0511] Take a set of unit regions with a distance between two unit regions in the obtained plurality of unit regions being equal to or less than a second threshold set by a second input as the input of the user as a clustering region, and display at least one such clustering region; and

[0512] Display at least one of the clustering regions obtained after accepting the first input and the second input as the difference model.

[0513] [Supplementary Note 35]

[0514] A display device for displaying a difference model, which is a three-dimensional model representing the difference between an object model and a target model. The object model is a three-dimensional model representing the three-dimensional shape of an object, and the target model is a three-dimensional model representing the target shape of the object. Among them,

[0515] The display device has an input device.

[0516] The display device displays a plurality of unit regions as a first display image. Each of the plurality of unit regions corresponds to a part of the target model, and the distance between the plurality of unit regions and the object model is greater than or equal to a first threshold set by the input device.

[0517] The display device takes a set of unit regions, where the distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold set by the input device, as a clustering region, and displays at least one of the clustering regions as a second display image.

[0518] The display device displays at least one of the clustering regions obtained according to the second threshold as the difference model.

[0519] [Appendix 36]

[0520] The display device according to Appendix 35, wherein,

[0521] The display device displays a first operation object that enables the user to set the first threshold together with the plurality of unit regions as the first display image.

[0522] The display device displays a second operation object that can be operated to set the second threshold together with at least one of the clustering regions as the second display image.

[0523] The display device displays at least one of the clustering regions obtained after the operations of the first operation object and the second operation object as the difference model.

[0524] [Appendix 37]

[0525] The display device according to Appendix 35 or 36, wherein,

[0526] The first display image and the second display image are switched and displayed.

[0527] [Appendix 38]

[0528] The display device according to Appendix 35 or 36, wherein,

[0529] The first display image and the second display image are displayed simultaneously.

[0530] [Supplementary Note 39]

[0531] An information processing apparatus that generates the difference model using the information processing method described in any one of Supplementary Notes 1 to 30 and Supplementary Notes 32 to 33.

[0532] [Supplementary Note 40]

[0533] A computer program that causes a computer to execute the information processing method described in any one of Supplementary Notes 1 to 30 and Supplementary Notes 32 to 33.

[0534] [Supplementary Note 41]

[0535] A computer program that causes a computer to execute the display method described in Supplementary Note 34.

[0536] [Supplementary Note 42]

[0537] An information processing method for generating a difference model, which is a three-dimensional model representing the difference between a first model and a second model, wherein

[0538] The information processing method includes the following steps:

[0539] Accepting an input from a user;

[0540] Obtaining a plurality of unit regions, each of which corresponds to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model is equal to or greater than a first threshold set by the input;

[0541] Regarding a set of unit regions in which the distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold as one clustering region, and obtaining at least one of the clustering regions; and

[0542] Outputting at least one of the clustering regions obtained after accepting the input as the difference model or as information for generating the difference model.

[0543] [Supplementary Note 43]

[0544] An information processing method for generating a difference model, which is a three-dimensional model representing the difference between a first model and a second model, wherein

[0545] The information processing method includes the following steps:

[0546] Accepting an input from a user;

[0547] Obtain a plurality of unit regions, each of the plurality of unit regions corresponding to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model being equal to or greater than a first threshold;

[0548] Take a set of unit regions where the distance between two of the obtained plurality of unit regions is equal to or less than a second threshold set through the input as a clustering region, and obtain at least one such clustering region; and

[0549] Output at least one of the clustering regions obtained after receiving the input as the differential model or as information for generating the differential model.

[0550] [Supplementary Note 44]

[0551] An information processing method for generating a differential model, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein,

[0552] The information processing method includes the following steps:

[0553] Obtain a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being equal to or greater than a first threshold;

[0554] Take a set of unit regions where the distance between two of the obtained plurality of unit regions is equal to or less than a second threshold as a clustering region, and obtain at least one such clustering region; and

[0555] Output at least one of the clustering regions obtained according to the second threshold as the differential model or as information for generating the differential model.

[0556] At least a part of the constituent elements of the above-described embodiments can be appropriately combined with at least another part of the constituent elements of the above-described embodiments. It is also possible not to use a part of the constituent elements of the above-described embodiments. And, as long as permitted by law, all the publicly disclosed gazettes and the publicly disclosed contents of U.S. patents cited in the above-described embodiments are cited as a part of the description herein.

[0557] The present invention is not limited to the above embodiments, and can be appropriately modified within the scope not violating the gist or idea of the invention read from the entire claims and the specification. The information processing method, processing method, display method, display device, information processing device, and computer program accompanied by such modifications are also included in the technical scope of the present invention.

[0558] Reference Signs Explanation

[0559] SYS: Processing system; 1: Processing device; 2: Measuring system; 22: Control information generation device; 2211: Model generation unit; 2212: Control information generation unit; 24: Input device; 25: Display device; 51: Extracted point display image; 52: Point cloud clustering display image; W: Workpiece; EL: Processing light; OM: Object model; TM: Target model; DM: Difference model; Ptm, Pom: Points; Pext: Extracted point; PGC: Point cloud clustering; TH_D: Model distance threshold; TH_C: Clustering threshold.

Claims

1. An information processing method for generating a differential model representing a part to be added to an object in order to process the object into a target shape, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object. Wherein, The information processing method includes the following steps: Receiving a first input as an input from a user; Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model and the distance between the plurality of unit regions and the object model being greater than or equal to a first threshold set by the first input; Receiving a second input as an input from the user; Regarding a set of unit regions where the distance between two unit regions among the obtained plurality of unit regions is less than or equal to a second threshold set by the second input as a clustering region, and obtaining at least one of the clustering regions; And Outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

2. The information processing method according to claim 1, Wherein, Processing control information is generated based on the differential model, and the processing control information controls a processing device capable of processing the object in such a way that the shape of the object becomes the target shape when the object is processed.

3. The information processing method according to claim 2, Wherein, The processing device is an additional processing device, The additional processing of the object by the processing device is performed as at least a part of a repair process of the object.

4. The information processing method according to any one of claims 1 to 3, Wherein, The information processing method further includes the following step: generating the target model by deforming at least a part of a reference model of the object according to the object model.

5. The information processing method according to any one of claims 1 to 3, Wherein, The target model is generated by deforming at least a part of the object model.

6. The information processing method according to claim 5, Wherein, The deformation of the object model includes magnification of a part of the object model.

7. The information processing method according to any one of claims 1 to 6, Wherein, The information processing method further includes the following steps: Displaying a first display image including the plurality of unit regions and a first operation object that can be operated by the user to adjust the first threshold; And Displaying a second display image including the clustering region and a second operation object that can be operated by the user to adjust the second threshold.

8. The information processing method according to claim 7, Wherein, The information processing method further includes the following steps: Updating the plurality of unit regions included in the first display image according to the operation of the first operation object; and Update the clustering region included in the second display image according to the operation of the second operation object.

9. The information processing method according to claim 7 or 8, wherein, By the adjustment of the second threshold by the user, the number of the clustering regions included in the second display image is changed.

10. The information processing method according to any one of claims 7 to 9, wherein, The first display image when the first threshold is set to the first candidate value includes a first reduced image, and the first reduced image includes a plurality of unit regions obtained when the first threshold is set to a second candidate value different from the first candidate value.

11. The information processing method according to claim 10, wherein, After the first display image including the first reduced image is displayed, when the user designates the first reduced image in the first display image, the designation is used as the first input, and the first display image when the first threshold is set to the second candidate value is displayed.

12. The information processing method according to any one of claims 7 to 11, wherein, The information processing method further includes a step of displaying a first tentative image, and the first tentative image includes: an image including a plurality of unit regions obtained when the first threshold is set to a first tentative value; and an image including a plurality of unit regions obtained when the first threshold is set to a second tentative value, When the user designates the image based on the first tentative value in the first tentative image, the designation is used as the first input, and the first display image when the first threshold is set to the first tentative value is displayed.

13. The information processing method according to claim 12, wherein, The first tentative image is displayed before the first input.

14. The information processing method according to any one of claims 7 to 13, wherein, The second display image when the second threshold is set to a third candidate value includes a second reduced image, and the second reduced image includes a plurality of unit regions obtained when the second threshold is set to a fourth candidate value different from the third candidate value.

15. The information processing method according to claim 14, wherein, After the second display image including the second reduced image is displayed, when the user designates the second reduced image in the second display image, the designation is used as the second input, and the first display image when the second threshold is set to the fourth candidate value is displayed.

16. The information processing method according to any one of claims 7 to 15, wherein, The information processing method further includes a step of displaying a second tentative image, and the second tentative image includes: an image including the clustering regions obtained when the second threshold is set to a third tentative value; and an image including the clustering regions obtained when the second threshold is set to a fourth tentative value, In the case where the user designates the image based on the third provisional value in the second provisional image, the designation is taken as the second input, and the second display image in the case where the second threshold value is set to the third provisional value is displayed.

17. The information processing method according to claim 16, wherein, the second provisional image is displayed before the first input is performed.

18. The information processing method according to any one of claims 7 to 17, wherein, the first display image and the second display image are displayed simultaneously.

19. The information processing method according to any one of claims 1 to 18, wherein, the information processing method further includes a step of receiving a third input, which is an input of the user for designating at least one of the clustering regions, and at least one clustering region designated by the third input is output as the difference model or as information for generating the difference model.

20. The information processing method according to any one of claims 1 to 19, wherein, the information processing method further includes a step of acquiring a thumbnail associated with the plurality of unit regions before acquiring the plurality of unit regions.

21. The information processing method according to any one of claims 1 to 20, wherein, the information processing method further includes a step of acquiring a thumbnail associated with the clustering region before acquiring the clustering region.

22. The information processing method according to any one of claims 1 to 21, wherein, the object is a first object, the object model is a first object model, the unit region is a first unit region, the clustering region is a first clustering region, the difference model is a first difference model, a first difference model is determined based on a first determination value of the first threshold value and a second determination value of the second threshold value, the information processing method further includes the following steps: acquiring a plurality of second unit regions, each of the plurality of second unit regions corresponding to a part of a target model of a second object and having a distance from a second object model, which is a three-dimensional model representing the three-dimensional shape of the second object, of not less than the first determination value; taking a set of second unit regions, the distance between two of the plurality of second unit regions obtained being not more than the second determination value, as one second clustering region, and acquiring at least one of the second clustering regions; and outputting at least one of the at least one second clustering region as a second difference model or as information for generating the second difference model.

23. The information processing method according to any one of claims 1 to 22, wherein, by receiving the first input, a first threshold value and a third threshold value different from the first threshold value are set, the step of acquiring the plurality of unit regions includes the following steps: acquiring a plurality of unit regions having a distance from the object model of not less than the first threshold value from among the plurality of unit regions included in a first region of the target model. The information processing method further includes the following steps: obtaining a plurality of unit regions with a distance from the object model greater than or equal to the third threshold from a second region of the target model that is different from the first region.

24. The information processing method according to any one of claims 1 to 23, wherein, by receiving the second input, setting the second threshold and a fourth threshold different from the second threshold, the step of obtaining the clustering region includes the following steps: obtaining, as a clustering region, a set of unit regions with a distance between two unit regions among the obtained plurality of unit regions less than or equal to the second threshold from a third region included in the obtained plurality of unit regions. The information processing method further includes the following steps: obtaining, as a clustering region, a set of unit regions with a distance between two unit regions among the obtained plurality of unit regions less than or equal to the fourth threshold from a fourth region different from the third region included in the plurality of unit regions.

25. An information processing method for generating a differential model, which is a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein, the information processing method includes the following steps: displaying a first display image that displays a plurality of unit regions and a first operation object that can be operated by the user to adjust a first threshold, the plurality of unit regions being obtained by subdividing the target model and having a distance from the object model greater than or equal to the first threshold; taking a set of unit regions with a distance between two unit regions among the plurality of unit regions less than or equal to a second threshold as a clustering region, and displaying a second display image that displays at least one of the clustering regions and a second operation object that can be operated by the user to adjust the second threshold; and outputting at least one of the clustering regions obtained after operating the first operation object and the second operation object as the differential model or as information for generating the differential model.

26. The information processing method according to claim 25, wherein, updating the display of the first display image according to the operation of the first operation object, updating the display of the second display image according to the operation of the second operation object.

27. An information processing method for generating a differential model, which is a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein, the information processing method includes the following steps: receiving a first input as an input from the user; Obtain a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being equal to or greater than a first threshold set by the first input; Take a set of unit regions where the distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold as a clustering region, and obtain at least one such clustering region; And Output at least one of the clustering regions obtained according to the second threshold as the difference model or as information for generating the difference model.

28. An information processing method for generating a difference model, which is a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein, the information processing method includes the following steps: Obtain a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being equal to or greater than a first threshold; Accept an input as the input of the user; Take a set of unit regions where the distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the input as a clustering region, and obtain at least one such clustering region; And Output at least one of the clustering regions obtained after accepting the input as the difference model or as information for generating the difference model.

29. An information processing method for generating a difference model representing the part removed from an object to process the object into a target shape, the difference model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object, wherein, the information processing method includes the following steps: Accept a first input as the input of the user; Obtain a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the object model, and the distance between the plurality of unit regions and the target model being equal to or greater than a first threshold set by the first input; Accept a second input as the input of the user; Take a set of unit regions where the distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the second input as a clustering region, and obtain at least one such clustering region; And Output at least one of the clustering regions obtained after accepting the first input and the second input as the difference model or as information for generating the difference model.

30. An information processing method for generating a differential model representing a portion to be processed to process an object into a target shape, the differential model being a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of the object, and the target model being a three-dimensional model representing the target shape of the object. Wherein, the information processing method includes the following steps: Receiving a first input as an input from a user; Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of one of the object model and the target model, and the distance between the plurality of unit regions and the other of the object model and the target model being equal to or greater than a first threshold set by the first input; Receiving a second input as an input from the user; Regarding a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the second input as one clustering region, and obtaining at least one of the clustering regions; And Outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

31. A processing method for generating processing control information for processing the shape of an object into the target shape by using a processing device capable of processing the object according to the differential model generated by using the information processing method according to any one of claims 1 to 30.

32. An information processing method for generating a differential model, the differential model being a three-dimensional model representing the difference between a first model and a second model. Wherein, the information processing method includes the following steps: Receiving a first input as an input from a user; Obtaining a plurality of unit regions, each of the plurality of unit regions corresponding to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model being equal to or greater than a first threshold set by the first input; Receiving a second input as an input from the user; Regarding a set of unit regions whose distance between two unit regions among the obtained plurality of unit regions is equal to or less than a second threshold set by the second input as one clustering region, and obtaining at least one of the clustering regions; And Outputting at least one of the clustering regions obtained after receiving the first input and receiving the second input as the differential model or as information for generating the differential model.

33. An information processing method for generating a differential model, the differential model being a three-dimensional model representing the difference between a first model and a second model. Wherein, the information processing method includes the following steps: Obtain a plurality of unit regions, each of the plurality of unit regions corresponding to a part of one of the first model and the second model, and the distance between the plurality of unit regions and the other of the first model and the second model being greater than or equal to a first threshold; Take a set of unit regions whose distance between two of the obtained plurality of unit regions is less than or equal to a second threshold as a clustering region, and obtain at least one of the clustering regions; And Output at least one of the clustering regions obtained according to the second threshold as the differential model or as information for generating the differential model.

34. A display method for displaying a differential model, which is a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein, the display method includes the following steps: Display a plurality of unit regions, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being greater than or equal to a first threshold set by a first input as the input of the user; Take a set of unit regions whose distance between two of the obtained plurality of unit regions is less than or equal to a second threshold set by a second input as the input of the user as a clustering region, and display at least one of the clustering regions; Display at least one of the clustering regions obtained after receiving the first input and the second input as the differential model.

35. A display device for displaying a differential model, which is a three-dimensional model representing the difference between an object model and a target model, the object model being a three-dimensional model representing the three-dimensional shape of an object, and the target model being a three-dimensional model representing the target shape of the object, wherein, the display device has an input device, the display device displays a plurality of unit regions as a first display image, each of the plurality of unit regions corresponding to a part of the target model, and the distance between the plurality of unit regions and the object model being greater than or equal to a first threshold set by the input device, the display device takes a set of unit regions whose distance between two of the obtained plurality of unit regions is less than or equal to a second threshold set by the input device as a clustering region, and displays at least one of the clustering regions as a second display image, the display device displays at least one of the clustering regions obtained according to the second threshold as the differential model.

36. The display device according to claim 35, wherein, the display device displays a first operation object capable of allowing the user to set the first threshold together with the plurality of unit regions as the first display image, the display device displays a second operation object capable of being operated to set the second threshold together with at least one of the clustering regions as the second display image, The display device displays at least one of the clustering regions obtained after the operations of the first operation object and the second operation object as the difference model.

37. The display device according to claim 35 or 36, wherein, the first display image and the second display image are switched for display.

38. The display device according to claim 35 or 36, wherein, the first display image and the second display image are displayed simultaneously.

39. An information processing device that generates the difference model by using the information processing method according to any one of claims 1 to 30 and claims 32 to 33.

40. A computer program that causes a computer to execute the information processing method according to any one of claims 1 to 30 and claims 32 to 33.

41. A computer program that causes a computer to execute the display method according to claim 34.

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