Forming support information generating method, forming support information generating device, forming support method, forming support device, and program

Abstract

To provide a shaping support information generation method and a shaping support method, a shaping support information generation device and a shaping support device, and a program which are capable of conveniently evaluating whether an occurring defect is harmful or harmless even on the spot where lamination shaping is performed.SOLUTION: A shaping support information generation method for supporting generation of shaping support information in which information of a defect occurring on a shaped object of lamination shaping is contained includes a step of acquiring a shape model of the shaped object and stress analysis conditions of a stress acting on the shaped object, a step of performing stress analysis based on the shape model and the stress analysis conditions, a step of determining the permissible limit dimensions of the defect at every place of the shaped model in accordance with results of the stress analysis, and a step of generating the shaping support information containing a distribution image 55 which expresses a distribution of the permissible limit dimensions in a specified region of the shape model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a modeling support information generating method, a modeling support information generating device, a modeling support method, a modeling support device, and a program. [Background technology]

[0002] Metal additive manufacturing (AM) is a type of metal processing technology known as additive manufacturing. This technology involves slicing a geometric model based on 3D data, such as 3D-CAD, to a specified thickness to form separate layers using weld beads, and then stacking the resulting bead layers to create a 3D additively manufactured object. Furthermore, during the manufacturing process of an additively manufactured object, the manufactured shape is measured using a sensor, and the manufacturing conditions for the next layer, etc., are adjusted based on the measurement results to improve the quality of the additively manufactured object.

[0003] In quality control of such additively manufactured objects, Patent Document 1 discloses a technology in which data measured by a sensor during the manufacturing process is matched with load information and force flow information, and the sensor measurement values ​​are classified and judged in terms of their impact on quality. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-194031 Summary of the Invention [Problem to be solved by the invention]

[0005] While additive manufacturing can realize complex shapes, the complexity of these shapes makes it difficult to determine whether defects that occur in the object are harmful or harmless. The method disclosed in Patent Document 1 identifies high-stress areas based on load information such as von Mises stress and evaluates the impact of defects by comparing them with the locations of sensor values ​​that are detrimental to quality. However, this method does not directly evaluate quality, such as the size of the actual defects, but merely extracts events that are expected to be detrimental to quality from the sensor values. Furthermore, converting the above-mentioned load information and sensor value information into the quality of the object is a heavy burden and is not practical, particularly in additive manufacturing sites.

[0006] Therefore, the present invention aims to provide a modeling support information generation method, a modeling support method, a modeling support information generation device, a modeling support device, and a program that can easily evaluate whether defects that occur are harmful or harmless, even at sites where additive manufacturing is performed. [Means for solving the problem]

[0007] The present invention comprises the following configurations. (1) A method for generating modeling support information that supports generation of modeling support information including information on defects that may occur in an object to be manufactured by additive manufacturing, the method comprising: acquiring a shape model of the object and stress analysis conditions for stress acting on the object; performing a stress analysis based on the shape model and the stress analysis conditions; determining a tolerance limit size of the defect for each location of the shape model according to the results of the stress analysis; generating the modeling support information including a distribution image representing a distribution of the allowable limit dimensions in a specific region of the shape model; Forming support information generation method. (2) Displaying the modeling support information generated by the modeling support information generating method according to (1) on a display device. Modeling support method. (3) A modeling support information generation device that supports generation of modeling support information including information on defects that may occur in a modeled object to be manufactured by additive manufacturing, comprising: an information acquisition unit that acquires a shape model of the object and stress analysis conditions for stress acting on the object; an analysis unit that executes stress analysis based on the shape model and the stress analysis conditions; a tolerance calculation unit that calculates a tolerance dimension of the defect for each location of the shape model according to the result of the stress analysis; an image information generating unit that generates the modeling support information including a distribution image that represents a distribution of the allowable limit dimensions in a specific region of the shape model; A modeling support information generating device comprising: (4) A program for realizing a modeling support information generation function that supports the generation of modeling support information including information on defects that may occur in an object to be modeled by additive manufacturing, On the computer, a function of acquiring a shape model of the object and stress analysis conditions for stress acting on the object; a function of executing a stress analysis based on the shape model and the stress analysis conditions; a function of determining an allowable limit size of the defect for each location of the shape model according to the result of the stress analysis; a function of generating the modeling support information including a distribution image representing a distribution of the allowable limit dimensions in a specific region of the shape model; A program to achieve this. [Effects of the Invention]

[0008] According to the present invention, even at an additive manufacturing site, it is possible to easily evaluate whether defects that occur are harmful or harmless. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a functional block diagram of the modeling support information generating device. [Figure 2] FIG. 2 is a diagram showing the overall configuration of the layered manufacturing device. [Figure 3] FIG. 3 is a flowchart showing the procedure for generating modeling support information and correcting a modeling plan. [Figure 4] FIG. 4 is an explanatory diagram showing a shape model of a shaped object. [Figure 5] FIG. 5 is an explanatory diagram showing a shape model divided into meshes. [Figure 6] FIG. 6 is a contour diagram of stress distribution showing an example of the results of stress analysis. [Figure 7] FIG. 7 is an explanatory diagram showing an analytical model of a buried crack. [Figure 8] FIG. 8 is an explanatory diagram showing a distribution image representing the distribution of the allowable limit dimensions. [Figure 9] FIG. 9 is an explanatory diagram showing a procedure for generating a composite image by combining a distribution image and defect information. [Figure 10] FIG. 10 is an explanatory diagram showing an example of a composite image including defect information. [Figure 11] FIG. 11 is an explanatory diagram showing the depth positions of defects detected by the defect detection unit. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The modeling support information generation method described here is a method for supporting the generation of modeling support information that includes information on defects that may occur in an object to be manufactured by additive manufacturing, and uses stress analysis results to convert the defect information of the object into a form that is easier for the user to understand. This makes it easier for the user to review and correct the modeling plan, enabling the desired shape to be manufactured more accurately.

[0011] 1 is a functional block diagram of a modeling support information generation device 100. The modeling support information generation device 100 includes an information acquisition unit 11, an analysis unit 13, an allowable limit calculation unit 15, an image information generation unit 17, and a defect detection unit 19. Details of each unit will be described later, but the functions thereof are outlined as follows.

[0012] The information acquisition unit 11 acquires a shape model of the object and stress analysis conditions for stress acting on the object. The analysis unit 13 performs stress analysis based on the shape model and the stress analysis conditions. The tolerance limit calculation unit 15 calculates the tolerance limit size of defects for each location on the shape model based on the results of the stress analysis by the analysis unit 13. The image information generation unit 17 generates modeling support information including a distribution image that shows the distribution of tolerance limit sizes in a specific region of the shape model. The defect detection unit 19 detects defects contained in the object actually produced by additive manufacturing.

[0013] The modeling support information generated by the modeling support information generating device 100 is output as, for example, display image information. By displaying this display image information on a display device 21 such as a monitor or a display board, the user is notified of defect information occurring in the model. This makes it easier to visually identify areas in the model to be modeled that are weak or strong in terms of structural strength due to defects. Furthermore, a composite image may be generated by combining the above-mentioned distribution image with the defect detection result from the defect detection unit 19 or the defect prediction result from the analysis unit (defect prediction unit) 13, and this composite image may be displayed on the display device 21. In this case, the user can visually easily determine whether the detected or predicted defect exceeds the allowable limit.

[0014] Next, we will explain the basic steps of additive manufacturing supported by the above-mentioned modeling support information generation device 100 and modeling support device 150. The modeling support information generation device 100 and modeling support device 150 support the work of creating or correcting a modeling plan for an object when additive manufacturing an object using an additive manufacturing device.

[0015] (Configuration of additive manufacturing device) 2 is an overall configuration diagram of the additive manufacturing apparatus 200. The additive manufacturing apparatus 200 includes a manufacturing unit 31 and a manufacturing control unit 33 that controls the manufacturing unit 31. The manufacturing support information generation device 100 may be connected to the manufacturing control unit 33 to configure a part of the additive manufacturing apparatus 200, or may be provided separately from the additive manufacturing apparatus 200 and connected to the manufacturing control unit 33 via communication such as a network or a storage medium.

[0016] The molding unit 31 includes a manipulator 37 , a filler metal supply unit 39 , a manipulator control unit 41 , and a heat source control unit 43 .

[0017] The manipulator control unit 41 controls the manipulator 37 and the heat source control unit 43. A controller (not shown) is connected to the manipulator control unit 41, and any operation from the manipulator control unit 41 can be instructed by an operator via the controller.

[0018] The manipulator 37 is, for example, an articulated robot, and a torch 35 attached to the tip shaft supports the filler material M so that it can be continuously supplied. The torch 35 holds the filler material M protruding from the tip. The position and posture of the torch 35 can be set arbitrarily in three dimensions within the range of the degrees of freedom of the robot arm constituting the manipulator 37. The manipulator 37 preferably has six or more degrees of freedom, and is preferably one that can arbitrarily change the axial direction of the heat source at the tip. The manipulator 37 may be in various forms, such as a four- or more-axis articulated robot as shown in FIG. 2, or a robot equipped with angle adjustment mechanisms on two or more orthogonal axes.

[0019] The torch 35 has a shield nozzle (not shown), through which shielding gas is supplied. The shielding gas blocks the atmosphere and prevents oxidation and nitridation of the molten metal during welding, thereby suppressing welding defects. The arc welding method used in this configuration may be either a consumable electrode type such as shielded metal arc welding or carbon dioxide gas arc welding, or a non-consumable electrode type such as TIG (Tungsten Inert Gas) welding or plasma arc welding, and is selected appropriately depending on the object to be formed. Here, gas metal arc welding will be used as an example. In the case of a consumable electrode type, a contact tip is disposed inside the shield nozzle, and a filler material M to which current is supplied is held by the contact tip. The torch 35 holds the filler material M and generates an arc from the tip of the filler material M in a shielding gas atmosphere.

[0020] The filler material supply unit 39 supplies the filler material M toward the torch 35. The filler material supply unit 39 includes a reel 39a around which the filler material M is wound, and a payout mechanism 39b that pays out the filler material M from the reel 39a. The filler material M is fed to the torch 35 by the payout mechanism 39b while being sent in the forward or reverse direction as needed. The payout mechanism 39b is not limited to a push type that is arranged on the filler material supply unit 39 side and pushes out the filler material M, but may also be a pull type or a push-pull type that is arranged on a robot arm or the like.

[0021] The heat source control unit 43 is a welding power source that supplies the power required for welding by the manipulator 37. The heat source control unit 43 adjusts the welding current and welding voltage supplied when forming a bead by melting and solidifying the filler material M. In addition, the filler material supply speed of the filler material supply unit 39 is adjusted in conjunction with the welding conditions set by the heat source control unit 43, such as the welding current and welding voltage.

[0022] The heat source for melting the filler material M is not limited to the arc described above. Other heat sources may also be used, such as a heating method that combines an arc and a laser, a heating method that uses plasma, or a heating method that uses an electron beam or laser. Heating with an electron beam or laser allows for more precise control of the amount of heat, which can more appropriately maintain the state of the weld bead to be formed and contribute to further improving the quality of the additively manufactured product. The material of the filler material M is also not particularly limited. The type of filler material M used may vary depending on the characteristics of the manufactured product Wk, such as mild steel, high-tensile steel, aluminum, aluminum alloy, nickel, or nickel-based alloy.

[0023] The forming control unit 33 controls the above-mentioned units in an integrated manner.

[0024] The additive manufacturing apparatus 200 configured as described above operates in accordance with a manufacturing program created based on a manufacturing plan for the object Wk. The manufacturing program is composed of a large number of command codes and is created based on an appropriate algorithm depending on various conditions, such as the shape, material, and heat input of the object Wk. According to this manufacturing program, the torch 35 is moved to melt and solidify the supplied filler material M, thereby forming a linear weld bead B, which is a molten solid of the filler material M, on the base 45. That is, the manipulator control unit 41 drives the manipulator 37 and the heat source control unit 43 based on a predetermined program provided by the manufacturing control unit 33. In response to commands from the manipulator control unit 41, the manipulator 37 moves the torch 35 while melting the filler material M with an arc to form the weld bead B.

[0025] In this way, by moving the processing position along a pre-set shaping path and using beads formed by adding molten processing material, filler metal M, to the surface to be shaped, layered bead layers are repeatedly stacked to obtain a shaped object Wk of the desired shape.

[0026] The modeling control unit 33 receives modeling support information output from the modeling support information generating device 100 and the modeling support device 150. This modeling support information includes the above-mentioned modeling plan and modeling program information. The modeling control unit 33 replaces or modifies the prepared modeling program in accordance with the input modeling support information, and drives each component of the modeling unit 31 to perform additive manufacturing.

[0027] The above-described shaping support information generation device 100 and shaping support device 150 are configured by hardware using an information processing device such as a PC (Personal Computer). Each function of the shaping support information generation device 100 and the shaping support device 150 is realized by a control unit (not shown) reading and executing a program having a specific function stored in a storage device (not shown). Examples of the control unit include a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processor Unit), or a dedicated circuit. Examples of the storage device include memories such as RAM (Random Access Memory), which is a volatile storage area connected to the processor, and ROM (Read Only Memory), which is a non-volatile storage area, as well as storage devices such as HDD (Hard Disk Drive) and SSD (Solid State Drive).

[0028] In addition to the above-mentioned forms, the modeling support information generating device 100 and the modeling support device 150 may be configured by another computer connected to the modeling control unit 33 from a remote location via a network, etc., as described above, or may be configured by a computer that is not connected to the modeling control unit 33.

[0029] (Generation of modeling support information) Next, a procedure for generating the shaping support information by the shaping support information generating device 100 will be described in detail together with the configuration of each part shown in FIG. 3 is a flowchart showing the procedure for generating modeling support information and correcting a modeling plan. First, the information acquisition unit 11 acquires a shape model representing a predicted shape or target shape of the model Wk to be formed by a weld bead, a modeling plan for additive manufacturing of the model Wk, and information on stress analysis conditions (described later) (S1). As the shape model, for example, shape data from a 3D CAD system can be used. If a modeling plan has not yet been created, a modeling plan for additive manufacturing of the model Wk is created using a known algorithm in accordance with the shape model and the welding conditions to be used by the modeling unit 31.

[0030] The analysis unit 13 performs stress analysis based on the stress analysis conditions using the shape model acquired by the information acquisition unit 11 (S2). This stress analysis may include various types of stress analysis, such as analysis of residual stress caused mainly by thermal distortion introduced when additively manufacturing the object Wk, and analysis of stress caused by loads expected to be applied when the object Wk is commercialized.

[0031] Furthermore, the shape model may have a smooth surface after cutting after additive manufacturing, or may have irregularities due to the weld beads that are layered. If the shape model includes irregularities due to the weld beads, the irregularities act as defects such as surface cracks, allowing for a safer evaluation. In particular, it is possible to more appropriately evaluate components whose surfaces cannot be smoothed by cutting, such as the internal space of the object (e.g., internal flow channels).

[0032] The above-mentioned stress analysis conditions include various conditions required for stress analysis, such as three-dimensional mesh division conditions for FEM analysis of the shape model, constraint conditions, load conditions, and physical property conditions.

[0033] Fig. 4 is an explanatory diagram showing a shape model MD of an object, and Fig. 5 is an explanatory diagram showing the shape model MD divided into meshes. The analysis unit 13 divides the shape model MD of the object shown in Fig. 4 into meshes as shown in Fig. 5, and performs stress analysis based on the set stress analysis conditions.

[0034] Figure 6 is a contour diagram of stress distribution showing an example of the results of stress analysis. Here, the results of analyzing only half of the horizontal area are shown, taking advantage of the symmetry of the shape of the object, but the stress analysis is performed in three dimensions using a shape model to determine the stress distribution in any cross section.

[0035] Next, based on the information of the above stress analysis results, the tolerance limit calculation unit 15 calculates the tolerance limit defect size value (S3). Figure 7 is an explanatory diagram showing an analytical model for a buried crack. In this model, for simplicity, the load stress σa is assumed to be in only one axis direction. When a partially through crack Ck is defined as an embedded elliptical crack (major axis length: 2Lc, minor axis length: 2La) as shown in Figure 7, the stress intensity factor K acting on the crack Cr is related to the load stress σa and the defect size a (a: equivalent circular diameter of the crack Ck) as shown in equation (1). Here, Fa is a dimensionless constant that depends on the crack size ratio and the loading type.

[0036]

number

[0037] Based on formula (1), the lower limit of the stress intensity factor range ΔK where the crack propagates is defined as ΔK th When this definition is given, the allowable limit dimension a of the defect (crack Ck) is th can be expressed by equation (2).

[0038]

number

[0039] Lower limit of stress intensity factor variation range Δk th may be determined in advance for each material used and each structure to be manufactured. For example, a value obtained by subjecting a test specimen of the manufactured object to a fatigue crack growth test may be used, or a value predicted from past manufacturing results may be used. The test specimen may be prepared by manufacturing a block test specimen using the same material and welding conditions as the filler metal M used in the object to be manufactured, and then cutting out a portion containing a defect from the block test specimen.

[0040] Furthermore, the above-mentioned non-dimensional constant Fa is a value that is set according to the shape of the defect, and can be set by referring to known standards (for example, in the case of welded structures, the Japan Welding Engineering Society standard WES2805 (2011)). The depth h from the surface of the test specimen to the crack position is taken into account in the non-dimensional constant Fa. From the above, if the defect type (for example, buried crack, surface crack) is identified and the acting stress is determined, the allowable limit size of the defect can be determined for each location using equation (2).

[0041] Next, the image information generating unit 17 generates a distribution image by visualizing the distribution of the calculated allowable limit dimensions, and displays the distribution image on the display device 21 (S4). Here, the distribution of the allowable limit dimensions calculated using the stress values ​​calculated by the stress analysis is displayed superimposed on the shape model.

[0042] FIG. 8 is an explanatory diagram showing a distribution image representing the distribution of allowable limit dimensions. The allowable limit dimension at each location on the object can be calculated from the stress value obtained by stress analysis and Equation (2). As shown in FIG. 8, the image information generation unit 17 classifies the allowable limit dimension values ​​into multiple levels and generates a distribution image that distinguishes each level. For example, a certain standard, such as 2 mm, 3 mm, or 4 mm, is specified as the allowable limit dimension, and a boundary line of an area corresponding to each standard is generated. In addition, to distinguish each area, a contour image may be generated in which the same area is displayed in a color or shade using a pre-specified color or density. By generating a contour image in which the allowable limit dimension is divided into multiple levels by color, visibility for the user can be improved.

[0043] The distribution image may be an emphasized image that emphasizes areas where the value of the allowable limit dimension is smaller than a predetermined threshold. For example, only areas where the allowable limit dimension is equal to or smaller than a predetermined value are extracted, and the areas are displayed on the display device 21 in a prominent form, such as by being displayed with a different color or density so that they can be easily distinguished from other areas, or by blinking. This makes it easier for the user to identify areas where defects are particularly likely to occur.

[0044] The generated distribution image may be displayed superimposed on a shape model of the object or a mesh-divided model, or may be displayed superimposed on a predicted shape model that represents the predicted shape of the object divided into individual weld beads to be layered. When the distribution image is displayed superimposed on the predicted shape model, the allowable defect dimensions can be visually and easily grasped while referring to the sizes and arrangements of the weld beads to be layered, which allows for smooth review and correction of the building conditions.

[0045] The display format of the distribution image of the tolerance limit dimensions is not particularly limited. When performing defect detection such as ultrasonic testing on a molded object, a contour image may be displayed for a cross section at a specified depth h [mm] from the inspection surface for the defect detection, or for a cross section having an axis in the depth direction. The distribution image may also be a plurality of images generated by classifying each defect type. By generating a distribution image for each defect type, different evaluation criteria can be set for each defect, resulting in more accurate determination of whether a defect is harmful or harmless. Defect types include surface cracks and internal cracks, and it is preferable to distinguish between these and evaluate them.

[0046] Specifically, the defect detection unit 19 forms a block specimen under the same conditions as those for the object to be manufactured by additive manufacturing, and detects defects that occur in the block specimen. The defect detection procedure involves, for example, bringing a phased array probe into contact with the inspection surface and detecting the size and position (depth) of defects in the block specimen using a normal inspection method or an angle beam inspection method. Types of defects that can be detected include blowholes, penetration defects, internal cracks, surface cracks, undercuts, and fusion defects. Note that while the above example illustrates defect detection using ultrasonic inspection, other methods such as magnetic inspection and eddy current inspection may also be used.

[0047] The defect detection unit 19 outputs defect information of the defect detection results described above to the image information generation unit 17. Meanwhile, the tolerance calculation unit 15 outputs information on the determined defect tolerance dimension to the image information generation unit 17. The image information generation unit 17 generates a composite image 59 by incorporating the defect information 57 from the defect detection unit 19 into a distribution image 55 obtained by visualizing the distribution of the tolerance dimension from the tolerance calculation unit 15.

[0048] FIG. 9 is an explanatory diagram showing the procedure for generating a composite image 59 by combining a distribution image 55 and defect information 57. The distribution image 55 of the allowable limit dimensions is a two-dimensional image showing the distribution of the allowable limit dimensions in a specific region (any surface or cross section) in the shape model of the object to be analyzed, and its coordinate axes are i, j. The output from the defect detection unit 19 includes information on the coordinates and sizes of the detected defects. Because the coordinate system of the defect information output from the defect detection unit 19 differs from the coordinate system of the distribution image 55, the image information generation unit 17 converts the defect information output from the defect detection unit 19 into the coordinate system (i, j) of the distribution image 55. In this way, a composite image 59 is generated by combining defect information 57 of the detected defects with the defect position Pd(i, j) of the distribution image 55.

[0049] FIG. 10 is an explanatory diagram showing an example of a composite image 59 including defect information. In this composite image 59, the detected defect position and a contour image of the distribution of the tolerance limit dimensions are superimposed on the same coordinate system. It is preferable that the detected defect is represented in the composite image 59 using a color or density corresponding to the tolerance limit dimension of the defect. In this case, it is possible to clearly determine from the composite image 59 whether the detected defect exceeds the tolerance limit dimension at the defect position Pd(i,j) (S5). That is, if the size of the detected defect exceeds the tolerance limit dimension around the defect, it is determined to be a harmful defect, and the modeling conditions are changed (S6), and a new modeling plan is created. If the size is equal to or smaller than the tolerance limit dimension around the defect, it is determined to be a harmless defect, and the set modeling plan is determined as the modeling plan to be used for additive manufacturing (S7).

[0050] FIG. 11 is an explanatory diagram showing the depth position of a defect detected by the defect detection unit 19. The defect detection unit 19 contacts a probe with the test surface 61a of the block specimen 61 and measures the time and intensity of ultrasonic waves emitted from the probe and reflected back from a defect Pd inside the block. The size and location of the defect Pd are determined from the measurement results. As shown in FIG. 11, the image information generation unit 17 generates a composite image by combining a distribution image of the allowable limit dimensions at the cross section of the block specimen 61 with defect information, and displays the composite image on the display device 21. Alternatively, the image information generation unit 17 outputs the composite image to another device as display image information. Furthermore, the image information generation unit 17 may generate a two-dimensional composite image (not shown) of the plane at depth H where the defect Pd was detected (the cross-sectional area indicated by the dashed line area Ac). By comparing the distribution of the allowable limit dimensions at depth H with the size of the defect, it can be determined whether the defect is harmful or harmless. In this way, the position of the composite image set on the block specimen 61 can be set on any surface as needed. This allows for the free and selective extraction of information necessary for defect evaluation. Furthermore, the results of flaw detection inspections performed on the surface being inspected during additive manufacturing can be easily reflected on the geometric model, making it easier to determine whether the measured flaws are harmful or harmless.

[0051] Furthermore, defect information is not limited to detection by the defect detection unit 19 as described above. For example, in the stress analysis performed by the analysis unit 13, the occurrence of defects may be predicted depending on the stress value. In such a case, the analysis unit 13 is made to function as a defect prediction unit and outputs the defect prediction result to the image information generation unit 17. The image information generation unit 17 generates a composite image by combining this predicted defect information (defect size and position) with a distribution image of the allowable limit dimensions, thereby making it possible to determine whether the defect is harmful or harmless and to confirm the validity of the molding plan. Furthermore, both the defect prediction result by the defect prediction unit and the defect detection result by the defect detection unit 19 may be included in the composite image. In this case, defects can be evaluated from the perspectives of both actual measurement and prediction, and a molding plan that reduces the occurrence of defects can be more reliably realized.

[0052] In this way, according to the modeling support information generating device having this configuration, it is possible to easily evaluate whether the defects that occur are harmful or harmless even at the site where additive manufacturing is carried out.

[0053] The present invention is not limited to the above-described embodiments, and it is also intended that the various components of the embodiments be combined with one another, and that modifications and applications be made by those skilled in the art based on the description in the specification and well-known techniques, and these modifications and applications are included in the scope of protection sought.

[0054] As described above, the present specification discloses the following: (1) A method for generating modeling support information that supports generation of modeling support information including information on defects that may occur in an object to be manufactured by additive manufacturing, the method comprising: acquiring a shape model of the object and stress analysis conditions for stress acting on the object; performing a stress analysis based on the shape model and the stress analysis conditions; determining a tolerance limit size of the defect for each location of the shape model according to the results of the stress analysis; generating the modeling support information including a distribution image representing a distribution of the allowable limit dimensions in a specific region of the shape model; Forming support information generation method. According to this method for generating modeling support information, the allowable limit dimensions of defects are determined in accordance with the results of stress analysis based on a shape model of the object, and the distribution of these allowable limit dimensions is generated as a distribution image. From this distribution image, it becomes easier to identify areas that are weak and strong against defects in terms of structural strength.

[0055] (2) The method for generating modeling support information according to (1), wherein the distribution image is an enhanced image in which a portion where the value of the allowable limit dimension is smaller than a predetermined threshold value is enhanced. According to this method for generating modeling support information, by converting the distribution image into an emphasized image, it becomes easier for the user to grasp areas where defects are particularly likely to occur.

[0056] (3) The method for generating modeling support information according to (1), wherein the distribution image is a contour image in which the values ​​of the allowable limit dimensions are classified into a plurality of stages and each stage is color-coded. According to this method for generating modeling support information, the distribution image can be made into an image with excellent visibility for the user.

[0057] (4) A method for generating modeling support information according to (1), which generates a composite image by superimposing on the distribution image at an image position corresponding to the defect position at least one of the detection results of the defect position and defect size of the defect contained in the additively manufactured object and the prediction results of the defect position and defect size of the defect predicted to occur in the object by the stress analysis. According to this method for generating modeling support information, a composite image can be generated in which at least one of the defect detection result and the defect prediction result is superimposed on the distribution image, thereby facilitating comparison between the defect information and the allowable limit dimension.

[0058] (5) The distribution image includes a plurality of images generated by classifying the defects by type, The method for generating modeling support information according to (4), wherein the types of defects include surface cracks and internal cracks. According to this method for generating modeling support information, different determination criteria can be set for each defect, and whether a defect is harmful or harmless can be determined more accurately.

[0059] (6) The specific region of the shape model is within a plane including a depth position of the shape model corresponding to the depth of the defect detection position of the object, The method for generating modeling support information described in (4), wherein the composite image is an image in which information on the defect dimensions of the defect is superimposed at an image position corresponding to the defect position of the defect in a distribution image of the allowable limit dimensions within the surface. According to this method for generating modeling support information, a composite image is generated by overlaying information on the defect dimensions of the defect onto a distribution image of the allowable limit dimensions within the surface at the depth position corresponding to the defect detection position of the shape model, and the composite image makes it clear whether the defect exceeds the allowable limit dimensions.

[0060] (7) The object is formed by repeatedly stacking layer-shaped bead layers using a weld bead formed by adding molten processing material to a surface to be formed while moving a processing position along a preset modeling path, The method for generating modeling support information according to any one of (1) to (6), wherein the shape model is a predicted shape of the object to be formed by the weld beads. According to this method for generating modeling support information, information is generated that supports modeling when a model is formed by stacking weld beads.

[0061] (8) Displaying the modeling support information generated by the modeling support information generating method according to any one of (1) to (7) on a display device. Modeling support method. According to this modeling support method, the generated modeling support information is displayed on a display device, which makes it easier for the user to visually understand the modeling support information.

[0062] (9) A modeling support information generation device that supports generation of modeling support information including information on defects that may occur in a modeled object to be manufactured by additive manufacturing, comprising: an information acquisition unit that acquires a shape model of the object and stress analysis conditions for stress acting on the object; an analysis unit that executes stress analysis based on the shape model and the stress analysis conditions; a tolerance calculation unit that calculates a tolerance dimension of the defect for each location of the shape model according to the result of the stress analysis; an image information generating unit that generates the modeling support information including a distribution image that represents a distribution of the allowable limit dimensions in a specific region of the shape model; A modeling support information generating device comprising: According to this modeling support information generating device, the allowable limit dimensions of defects are calculated in accordance with the results of stress analysis based on a shape model of the object, and the distribution of these allowable limit dimensions is generated as a distribution image. From this distribution image, it becomes easier to identify areas that are weak and strong against defects in terms of structural strength.

[0063] (10) a defect detection unit that detects defects in the object produced by the additive manufacturing process; a defect prediction unit that predicts defects that will occur in the object by stress analysis; Equipped with The modeling support information generating device according to (9), wherein the image information generating unit generates a composite image by overlaying defect information, including defect types and defect positions obtained from at least one of the defect detection unit and the defect prediction unit, on the distribution image in correspondence with the defect positions. According to this modeling support information generating device, a composite image can be generated in which at least one of the defect detection result and the defect prediction result is superimposed on the distribution image, thereby facilitating comparison between the defect information and the allowable limit dimension.

[0064] (11) A modeling support information generating device according to (9) or (10), a display device that displays the generated modeling support information; A modeling support device comprising: According to this shaping support device, the generated shaping support information is displayed on the display device, which makes it easier for the user to visually understand the shaping support information.

[0065] (12) A program for realizing a modeling support information generation function that supports generation of modeling support information including information on defects that may occur in an object to be modeled by additive manufacturing, the program comprising: On the computer, a function of acquiring a shape model of the object and stress analysis conditions for stress acting on the object; a function of executing a stress analysis based on the shape model and the stress analysis conditions; a function of determining an allowable limit size of the defect for each location of the shape model according to the result of the stress analysis; a function of generating the modeling support information including a distribution image representing a distribution of the allowable limit dimensions in a specific region of the shape model; A program to achieve this. This program determines the allowable limit dimensions of defects according to the results of stress analysis based on a shape model of the object, and generates a distribution image of the distribution of these allowable limit dimensions. This distribution image makes it easier to identify areas that are weak and strong against defects in terms of structural strength.

[0066] (13) The program according to (12), The computer, a function of detecting defects contained in the object produced by the additive manufacturing process; a function of predicting defects that may occur in the object by stress analysis; a function of generating a composite image by superimposing defect information, including defect types and defect positions obtained from at least one of the defect detection results and the defect prediction results, on the distribution image in correspondence with the defect positions; A program to further realize this. This program can generate a composite image in which at least one of the defect detection results and the defect prediction results is superimposed on a distribution image, making it easier to compare defect information with the tolerance limit dimensions.

[0067] (14) The program according to (12) or (13), The computer, Further, a program for realizing a function of displaying the generated modeling support information on a display device. According to this program, the generated modeling support information is displayed on a display device, making it easier for the user to visually understand the modeling support information. [Explanation of symbols]

[0068] 11 Information acquisition department 13 Analysis Department 15 Allowable limit calculation section 17 Image information generation unit 19 Defect detection section 21 Display Devices 25 Torch 31 Modeling Department 33 Modeling control section 35 Torch 37 Manipulator 39 Filler metal supply section 39a Reel 39b Feeding mechanism 41 Manipulator control unit 43 Heat source control unit 45 base 55 Distribution image 57 Defect Information 59 Composite Images 61 Block specimen 61a Flaw detection surface 100 Modeling support information generation device 150 Modeling support equipment 200 Additive Manufacturing Equipment B Weld bead M filler metal MD Shape Model Pd defects Wk sculpture

Claims

1. 1. A modeling support information generation method for supporting generation of modeling support information including information on defects that may occur in a modeled object to be manufactured by additive manufacturing, comprising: acquiring a shape model of the object and stress analysis conditions for stress acting on the object; performing a stress analysis based on the shape model and the stress analysis conditions; determining a tolerance limit size of the defect for each location of the shape model according to the results of the stress analysis; generating the modeling support information including a distribution image representing a distribution of the allowable limit dimensions in a specific region of the shape model; Forming support information generation method.

2. the distribution image is an enhanced image in which a portion where the value of the allowable limit dimension is smaller than a predetermined threshold value is enhanced; The method for generating modeling support information according to claim 1 .

3. The distribution image is a contour image in which the values ​​of the allowable limit dimensions are classified into a plurality of stages and each stage is distinguished by a different color. The method for generating modeling support information according to claim 1 .

4. generating a composite image by superimposing, on the distribution image, at least one of information on the detection results of defect positions and defect sizes of defects contained in the additively manufactured object and information on the prediction results of defect positions and defect sizes of defects predicted to occur in the object by the stress analysis, at image positions corresponding to the defect positions; The method for generating modeling support information according to claim 1 .

5. the distribution image includes a plurality of images generated by classifying the defects by type, The types of defects include surface cracks and internal cracks. The method for generating modeling support information according to claim 4 .

6. the specific region of the shape model is within a plane that includes a depth position of the shape model that corresponds to the depth of the defect detection position of the object, the composite image is an image in which information on the defect dimensions of the defects is superimposed at an image position corresponding to the defect position of the defect in the distribution image of the tolerance limit dimensions within the surface. The method for generating modeling support information according to claim 4 .

7. The object is formed by repeatedly stacking layer-shaped bead layers using weld beads formed by adding molten processing material to a surface to be modeled while moving a processing position along a preset modeling path, the shape model is a predicted shape of the object to be formed by the weld bead; The method for generating modeling support information according to claim 1 .

8. The modeling support information generated by the modeling support information generating method according to claim 1 is displayed on a display device. Modeling support method.

9. displaying the modeling support information generated by the modeling support information generating method according to claim 7 on a display device; Modeling support method.

10. 1. A modeling support information generation device that supports generation of modeling support information including information on defects that may occur in a modeled object to be manufactured by additive manufacturing, an information acquisition unit that acquires a shape model of the object and stress analysis conditions for stress acting on the object; an analysis unit that executes stress analysis based on the shape model and the stress analysis conditions; a tolerance calculation unit that calculates a tolerance dimension of the defect for each location of the shape model according to the result of the stress analysis; an image information generating unit that generates the modeling support information including a distribution image that represents a distribution of the allowable limit dimensions in a specific region of the shape model; A modeling support information generating device comprising:

11. a defect detection unit that detects defects included in the object produced by the additive manufacturing process; and a defect prediction unit that predicts defects that will occur in the object by stress analysis; Equipped with the image information generating unit generates a composite image by superimposing defect information, including defect types and defect positions obtained from at least one of the defect detection unit and the defect prediction unit, on the distribution image in correspondence with the defect positions. The modeling support information generating apparatus according to claim 10.

12. The modeling support information generating device according to claim 10 or 11, a display device that displays the generated modeling support information; A modeling support device comprising:

13. A program for realizing a modeling support information generation function that supports generation of modeling support information including information on defects that may occur in an object to be manufactured by additive manufacturing, On the computer, a function of acquiring a shape model of the object and stress analysis conditions for stress acting on the object; a function of executing a stress analysis based on the shape model and the stress analysis conditions; a function of determining the allowable limit size of the defect for each location of the shape model according to the result of the stress analysis; a function of generating the modeling support information including a distribution image representing a distribution of the allowable limit dimensions in a specific region of the shape model; A program to achieve this.

14. 14. The program according to claim 13, The computer, a function of detecting defects contained in the object produced by the additive manufacturing process; a function of predicting defects that may occur in the object by stress analysis; a function of generating a composite image by superimposing defect information, including defect types and defect positions obtained from at least one of the defect detection results and the defect prediction results, on the distribution image in correspondence with the defect positions; A program to further realize this.

15. 15. The program according to claim 13 or 14, The computer, Further, a program for realizing a function of displaying the generated modeling support information on a display device.

Images