Processing state information estimation device and processing state diagnosis device

Abstract

A machining state diagnosis device (10) includes a machining state information estimation device (1), a learning information storage unit (11) that stores relationship information between estimated machining state information and actual machining state information, and a machining state diagnosis unit (12) that diagnoses a machining state in actual machining based on machining state information obtained in actual machining using control information, machining state information estimated by the machining state information estimation device (1) based on corresponding control information, and the relationship information stored in the learning information storage unit (11). The machining state information estimation device (1) includes an information storage unit (2) that stores information related to a workpiece, a tool, and control information, a machining state information estimation unit (3) that estimates machining state information when the workpiece and the tool are relatively moved based on the information related to the workpiece, the tool, and the control information, and an image data generation unit (4) that generates image data based on the estimated machining state information.

Description

TECHNICAL FIELD

[0001] The present application relates to a machining state information estimating device which estimates machining state information at the time of machining a virtual workpiece based on control information used in machining of an NC machine tool, and an imaging machining state diagnosis device provided with the machining state information estimating device. BACKGROUND

[0002] In the above-described NC machine tool field, the desired machining is performed by controlling the movement mechanism section of the NC machine tool based on the NC program. Also, in the past, in order to achieve the most appropriate machining, various attempts to estimate the actual machining state have been made, and as one of them, a cutting load prediction system disclosed in the following Patent Literature 1 has been proposed.

[0003] The cutting load prediction system as described in the above-described Patent Literature 1 is a cutting load prediction system which predicts a cutting load applied to a cutting tool in a machine tool that moves the cutting tool to cut a workpiece based on NC data, and has: an NC data determination mechanism for determining the NC data that is a prediction target of the cutting load; a run-out execution mechanism that causes the machine tool to perform a cutting process based on the determined NC data in a run-out form in which cutting does not occur; a movement data acquisition mechanism that acquires predetermined movement data of the cutting tool including each tool position through which the cutting tool passes at the time of the cutting process; and a cutting load prediction mechanism that calculates a cutting load applied to the cutting tool when the machine tool performs the cutting process on the workpiece based on the NC data, based on the acquired movement data.

[0004] According to the cutting load prediction system, the machine tool is caused to perform run-out of moving the cutting tool based on the NC data, actual movement data (movement trajectory or tool path speed) of the cutting tool is acquired, and the cutting load of the cutting tool is predicted based on the acquired accurate movement data, so the prediction accuracy of the cutting load can be greatly improved compared to the cutting load prediction of the past in which the cutting load is predicted based on simulation of the NC data, that is, the cutting load prediction in which the actual movement trajectory (tool trajectory) of the machine tool is not considered at all.

[0005] PRIOR ART DOCUMENTS

[0006] PATENT LITERATURE

[0007] Patent Literature 1: Japanese Patent Application Publication No. 2017-72880 SUMMARY

[0008] PROBLEMS TO BE SOLVED BY THE INVENTION

[0009] However, according to the cutting load prediction system disclosed in the above-described Patent Document 1, there is a problem that, although it can be possible to predict the cutting load acting on the cutting tool with high precision, the state of the cutting load acting on the tool only captures one situation of the cutting process performed, and it is not possible to objectively grasp the process state captured as other situations.

[0010] The inventors of the present application have conducted intensive research on a presentation method by which the process state can be visually and objectively grasped in an NC machine tool in which a process is performed using an NC program including information on a movement path of a tool and the like, and have found that, by estimating information on the process state that changes with the passage of time and imaging the estimated information on the process state, it is possible to present the process state to which more elements are related.

[0011] The present application has been achieved in the above-described background, and aims to provide a process state information estimation device by which information on a process state can be imaged in a process using an NC machine tool, and a process state diagnosis device provided with the process state information estimation device.

[0012] Solution to the problem

[0013] The present application for solving the above-described technical problem relates to a process state information estimation device provided with:

[0014] an information storage section that stores control information used in a process by an NC machine tool, information on a workpiece processed by the NC machine tool, and information on a tool used in the NC machine tool;

[0015] a process state information estimation section that, after configuring a virtual workpiece and a virtual tool set in accordance with the information on the workpiece and the tool stored in the information storage section in a virtual space in the same positional relationship as a positional relationship based on the control information, relatively moves the configured virtual workpiece and virtual tool in accordance with the control information stored in the information storage section, and estimates a process state at the time of virtually processing the workpiece along with the relative movement of the workpiece and the tool; and

[0016] an image data generation section that generates image data for presenting a relationship between time and the process state information as an image in accordance with the process state information along the time axis estimated by the process state information estimation section.

[0017] According to the process state information estimation device, first, the control information used in a process by an NC machine tool, the information on a workpiece processed by the NC machine tool, and the information on a tool used in the process are stored in advance in the information storage section.

[0018] Then, based on the control information, the workpiece, and the tool-related information stored in the information storage section, the machining state information is estimated by the machining state information estimation section.

[0019] That is, the machining state information estimation section, after arranging the imaginary workpiece and the imaginary tool set based on the workpiece and the tool-related information stored in the information storage section in the imaginary space in the same positional relationship as the positional relationship based on the control information, relatively moves the arranged imaginary workpiece and the imaginary tool in accordance with the control information stored in the information storage section, and estimates the machining state when the imaginary workpiece is machined along with the relative movement of the workpiece and the tool.

[0020] Then, based on the machining state information along the time axis estimated by the machining state information estimation section, the image data for expressing the relationship between the time and the machining state information as an image is generated by the image data generation section.

[0021] Further, the control information includes at least information required for machining such as the movement path, the movement speed, and the rotational speed of the tool, and for example, includes at least one of an NC program having these pieces of information, or so-called tool path data before conversion into an NC program, or servo command data, or servo feedback data, and the like.

[0022] In this way, according to the machining state information estimation device of the present application, since the image data for expressing the machining state information along the time axis as an image is generated, the machining state when the workpiece is machined by relatively moving the tool and the workpiece in accordance with the control information can be objectively recognized by observing the image along the time axis.

[0023] Further, in the present application, the machining state information estimation section can be configured in a manner that estimates machining state information including at least one of information related to the contact state of the tool and the workpiece in the contact depth direction of the tool and the workpiece, information related to the contact state of the tool and the workpiece in the rotational direction of the tool, information related to the cutting resistance acting on the tool, information related to the surface roughness of the workpiece, and information related to the cutting depth in the rotational direction of the tool.

[0024] In particular, if the information related to the state of contact between the tool and the workpiece in the depth direction of contact is imaged, the time of contact between the tool and the workpiece and the state of contact in the depth direction of contact can be objectively recognized. Furthermore, by recognizing such a state of contact, the state of load acting on the tool can be estimated. In addition, in the case of a cutting knife having a plurality of tools, the image data generating section can be configured in a manner that generates image data representing information on the state of contact in the depth direction of contact along the time axis for each cutting knife.

[0025] Furthermore, if the image represents the state of contact between the tool and the workpiece in the depth direction of contact along the time axis, conversely, from the image and in combination with information on the diameter of the tool or the rotational speed, etc., the shape of the workpiece machined by the tool or the movement path of the tool, etc. can be recognized, and by analyzing the path of movement of the tool, the NC program used in the machining can be reproduced. Therefore, even in the case where the NC program is lost due to some reason, the lost NC program can be reproduced by analyzing the image representing the relationship between the time and the state of contact information corresponding to the NC program.

[0026] In addition, in the present application, the image data generating section can be configured in a manner that generates image data including at least data (information) on a color representing the state of machining as the image data. In this way, by representing the state of machining information by an image including at least information on a color, various kinds of state of machining information can be represented by an image.

[0027] Furthermore, in the present application, the image data generating section is configured in a manner that generates color image data composed of a plurality of color elements as the image data,

[0028] The machining state information estimating section can be configured in a manner that estimates machining state information including information selected from among information on the state of contact between the tool and the workpiece in the depth direction of contact, information on the state of contact between the tool and the workpiece in the rotational direction of the tool, information on the cutting resistance acting on the tool, information on the surface roughness of the workpiece, and information on the depth of cut in the rotational direction of the tool, in a number corresponding to the number of color elements, in which case one of the information estimated by the machining state information estimating section is respectively assigned to each color element. In this way, an image in which each of the above machining state information can be clearly distinguished (recognized) can be represented.

[0029] In addition, it is preferable that the processing state information estimating device according to the present application include a display section that displays an image according to the image data generated by the image data generating section. By displaying the image on the display section, the processing state can be easily confirmed.

[0030] In addition, the present application relates to a processing state diagnosing device that includes any one of the above-described processing state information estimating devices, and includes:

[0031] a learning information storage section that stores relationship information learned through experience, the relationship information associating estimated processing state information estimated when a workpiece is hypothetically processed using the control information for estimation with actual processing state information obtained when the workpiece is actually processed using the control information for estimation; and

[0032] a processing state diagnosing section that diagnoses the appropriateness of the processing state in actual processing using a prescribed control information, based on the processing state information obtained in the actual processing using the prescribed control information, the estimated processing state information estimated by the processing state estimating section based on the control information, and the relationship information stored in the learning information storage section.

[0033] According to the processing state diagnosing device, first, the relationship between the actual processing state information obtained when a workpiece is actually processed using each of a plurality of control information and the estimated processing state information estimated by the processing state estimating section based on the corresponding control information is learned, and the relationship information obtained based on the learning result is stored in the learning information storage section.

[0034] Further, the processing state diagnosing device diagnoses the appropriateness of the processing state in actual processing using a prescribed control information, that is, whether the processing state is normal or not, based on the processing state information obtained in the actual processing, the estimated processing state information estimated by the processing state estimating section based on the corresponding control information, and the relationship information stored in the learning information storage section.

[0035] For example, in a case where the processing state information obtained when a workpiece is processed using a prescribed control information exceeds a permissible range set with respect to the estimated processing state information estimated based on the corresponding control information and the actual processing state information that should be standard derived from the relationship information, the processing state diagnosing device diagnoses that the processing state in the processing is abnormal, and in a case where it is within the permissible range, diagnoses that the processing state is normal.

[0036] The machining state of the NC machine tool differs, for example, depending on the contact state of the tool with the workpiece, and also differs depending on the wear state of the tool, and the like. According to the machining state diagnosis device, for the machining state that varies depending on the contact state of the tool with the workpiece, and the like, it is determined whether the machining state is normal based on the learning result learned in advance, and thus the diagnosis of the machining state can be accurately performed.

[0037] Effects of the Invention

[0038] As explained above, according to the machining state information estimation device according to the present invention, machining information along a time axis is imaged, and thus by observing the image along the time axis, the machining state can be objectively recognized, the machining information being machining state information when the tool is relatively moved with respect to the workpiece in accordance with the control information to machine the workpiece.

[0039] In particular, if the machining state information related to the contact state of the tool with the workpiece in the contact depth direction of the tool with the workpiece is imaged, the time of contact of the tool with the workpiece and the contact state in the contact depth direction can be objectively recognized, and by recognizing the contact state, the load state acting on the tool can be estimated.

[0040] Further, if the above image represents the contact state of the tool with the workpiece in the contact depth direction along the time axis, conversely, the image, that is, the image representing the contact depth along the time axis is analyzed together with information such as the diameter of the tool or the rotational speed of the tool, and the shape of the workpiece machined by the tool or the movement path of the tool, and the like can be recognized, and thus by analyzing the path of movement of the tool, the NC program used in the machining can be reproduced.

[0041] Further, according to the machining state diagnosis device according to the present invention, the machining state that varies depending on the contact state of the tool with the workpiece is learned in advance, and based on the learning result, it is determined whether the machining state is normal, and thus the diagnosis of the machining state can be accurately performed. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a block diagram showing the schematic structure of a contact state estimation device according to a first embodiment of the present invention.

[0043] Figure 2 shows a tool used in the first embodiment, Figure 2 (a) is a front view of the tool, Figure 2 (b) is a bottom view of the tool.

[0044] Figure 3 is a plan view showing a workpiece machined in the first embodiment.

[0045] Figure 4is an explanatory view showing a processing method in the first embodiment.

[0046] Figure 5 is an explanatory view showing a processing method in the first embodiment.

[0047] Figure 6 is an explanatory view showing a processing method in the first embodiment.

[0048] Figure 7 is an explanatory view showing a processing method in the first embodiment.

[0049] Figure 8 is an explanatory view showing a processing method in the first embodiment.

[0050] Figure 9 is an explanatory view showing a processing method in the first embodiment.

[0051] Figure 10 is an explanatory view for explaining a contact state in a depth direction in the first embodiment.

[0052] Figure 11 is an explanatory view for explaining imaging of contact state information in the first embodiment.

[0053] Figure 12 is an explanatory view showing an image of contact state information in the first embodiment.

[0054] Figure 13 (a) is an enlarged view in which a portion a in Figure 12 is enlarged, Figure 13 (b) is an enlarged view in which a portion b in Figure 12 is enlarged.

[0055] Figure 14 is an explanatory view for explaining processing state information relating to Modification 1 of the first embodiment.

[0056] Figure 15 is an explanatory view showing an image of processing state information relating to Modification 1 of the first embodiment.

[0057] Figure 16 is an explanatory view for explaining imaging of processing state information relating to Modification 1 of the first embodiment.

[0058] Figure 17 is an explanatory view for explaining processing state information relating to Modification 2 of the first embodiment.

[0059] Figure 18 is an explanatory view showing an image of processing state information relating to Modification 2 of the first embodiment.

[0060] Figure 19 This is an explanatory diagram illustrating the imaging of processing status information involved in Modification 2 of the first embodiment.

[0061] Figure 20 This is an explanatory diagram illustrating the imaging of processing status information involved in Modification 3 of the first embodiment.

[0062] Figure 21 This is a block diagram illustrating the general structure of the processing status diagnostic device according to the second embodiment of the present invention. Detailed Implementation

[0063] Hereinafter, specific embodiments of the present invention will be described with reference to the accompanying drawings.

[0064] (First Implementation)

[0065] First, according to Figures 1-3 The processing status information estimation apparatus according to the first embodiment of the present invention will be described. For example... Figure 1 As shown, the processing status information estimation device 1 in this example consists of an information storage unit 2, a processing status information estimation unit 3, an image data generation unit 4, and a display unit 5. Except for the display unit 5, the processing status information estimation device 1 is composed of a computer including a CPU, RAM, ROM, etc. The processing status information estimation unit 3 and the image data generation unit 4 implement their functions through computer programs and perform the processing described later. Furthermore, the information storage unit 2 is composed of a suitable storage medium such as RAM. Additionally, the display unit 5 is composed of a display such as a touch panel.

[0066] The information storage unit 2 pre-stores control information used in the machining of the NC machine tool, information related to the workpiece being machined using the NC machine tool (workpiece information), and information related to the tools used in the NC machine tool (tool information). Furthermore, the control information includes at least the tool's movement path, movement speed, and rotation speed, and may include, for example, at least one of the following: an NC program containing this information, so-called tool path data before conversion into an NC program, servo command data, or servo feedback data. Additionally, the workpiece information includes at least information related to the workpiece's size and shape, and the tool information includes at least various tool-related information such as the type of tool, the number of cutting tools, the cutting tool's torsion angle, and the tool's nominal diameter (effective machining diameter).

[0067] The machining status information estimation unit 3 first sets up imaginary workpiece and tool models based on the workpiece information and tool information stored in the information storage unit 2. The set imaginary workpiece and tool models are then positioned in an imaginary space with the same positional relationship as those configured on the NC machine tool—in other words, based on the positional relationship of the control information. Then, according to the control information stored in the information storage unit 2, the machining status information estimation unit 3 moves the configured imaginary workpiece and tool models relative to each other. As the tool and workpiece move relative to each other, the contact state information between the tool and workpiece, which serves as machining status information, is estimated.

[0068] The following is for use Figure 2 The tool T shown is used for machining. Figure 3 The specific processing of the machining state information estimation unit 3 will be explained using the case of workpiece W as an example. Furthermore, Figure 2 The tool T shown is a tool with a nominal diameter of The end mill has four cutting tools, each with a torsion angle of γ. Additionally, Figure 3 The workpiece W shown has a hook-shaped planar shape. The tool T is moved from position P1 to position P2 as indicated by the arrow, and the cutting amount Wa shown by the dashed line along the inner side of the hook is machined.

[0069] First, as mentioned above, the machining state information estimation unit 3 estimates the machining state information in a manner that the workpiece W and the tool T are in a predetermined positional relationship. Specifically, in Figure 3 The tool T is positioned at P1, and the workpiece W is arranged in the imaginary space.

[0070] Next, the machining status information estimation unit 3 moves the model of tool T (hereinafter referred to as "tool T") relative to the model of workpiece W (hereinafter referred to as "workpiece W") according to the rotation speed, feed speed and movement path (direction shown by the arrow) included in the control information stored in the information storage unit 2, and estimates the contact status information between tool T and workpiece W.

[0071] The estimated contact state information between the tool T and the workpiece W includes the contact state information between each cutting blade of the tool T and the workpiece W in the depth direction, which changes over time, and the contact state information between each cutting blade of the tool T and the tool T in the rotation direction, which changes over the same period of time.

[0072] Furthermore, in the contact state information in the depth direction, such as Figure 10 As shown, the height dimension D of the lowest point where the cutting blade contacts the workpiece W is... a And the height dimension D of the highest position b This indicates each cutting blade. Additionally, the contact status information in the rotation direction, such as...Figure 4 As shown, with reference phase θ0 (=0) as the reference, the phase (angle) farthest from reference phase θ0 in the state where the cutting tool is in contact with the workpiece W is (θ). b +θ a ), and the phase (angle) θ closest to the reference phase θ0. a This represents each cutting blade. Furthermore, the contact state information in both the depth and rotation directions must be estimated taking into account the twist angle γ of the tool T.

[0073] Moreover, the processing status information estimation unit 3, such as Figure 3 As shown, the tool T is moved relative to the workpiece W from position P1 to position P2 in the direction indicated by the arrow (refer to...). Figures 4-9 Contact state information (D) in the depth direction is processed at specified time intervals. a D b ) and contact state information in the rotation direction (θ) a 、(θ a +θ b To make an estimate.

[0074] also, Figure 4 This indicates the state where tool T is in contact with workpiece W and has begun machining workpiece W. Figure 5 This indicates that tool T has reached the state where it is machining the workpiece W with a total cutting amount Wa set thereon. Additionally, Figure 6 This indicates the state in which tool T reaches the inner corner of workpiece W. Figure 7 This indicates the state at which tool T begins machining the inner corner of workpiece W. Figure 8 This indicates the state in which tool T reaches the center of the inner corner of workpiece W. Figure 9 This indicates the state where tool T cuts off the inner corner of workpiece W.

[0075] according to Figure 4 as well as Figure 10 It can be seen that if tool T begins to contact workpiece W, then the contact state information D in the depth direction... a The minimum value of D is 0. b The maximum value is the cutting load D p D a The time period with a value of 0 and D b D p The time period lasts for a specified duration. This time period continues until it becomes... Figure 5 The tool T shown gradually increases in length until it reaches the inner corner of the workpiece W, where a total cutting amount Wa is set. On the other hand, the contact state information θ in the rotational direction... a and θ b θ aGradually decrease, on the contrary θ b Gradually growing bigger, becoming Figure 5 After the state shown, until tool T reaches the inner corner of workpiece W, θ a and θ b Take a constant value, θ a It is 0.

[0076] Then, as Figures 6-8 As shown, the contact state information D in the depth direction is as follows: during the machining of the inner corner of the workpiece W to its center position by tool T. a The time period with a value of 0 and D b D p The time period gradually lengthened, and then, until Figure 9 The time interval gradually shortens as the tool T punches away the corner. On the other hand, the contact state information θ in the rotational direction... a and θ b From Figure 6 The state shown Figure 8 During the state shown, θ a and θ b Gradually grow bigger, and then... Figure 9 Up to the state shown, θ a Gradually increases, conversely θ b It gradually gets smaller.

[0077] Next, in tool T from Figure 9 As the state shown progresses in the direction indicated by the arrow, when the total machining cutting amount Wa is reached, the contact state information D in the depth direction is... a The time period with a value of 0 and D b D p The time period is constant, and then gradually shortens as tool T leaves workpiece W. At the moment tool T leaves workpiece W, D... a and D b They are both 0. On the other hand, the contact state information θ in the rotation direction is 0. a and θ b When tool T is in the state of machining cutting amount Wa, θ a and θ b It is constant, and thereafter, as the tool T leaves the workpiece W, θ b Gradually decreasing, but θ a Keep constant, θ a and θ b The values ​​are 0 respectively.

[0078] The image data generation unit 4 generates contact state information (D) in the depth direction estimated by the processing state information estimation unit 3. a Db ) and contact state information in the rotation direction (θ) a 、(θ a +θ b This performs image data generation processing to represent these contact state information as images (graphics in this example).

[0079] Specifically, the image data generation unit 4 generates image data that represents contact state information in the depth direction and contact state information in the rotation direction. Figure 11 The image shown. Figure 11 (a) is the contact state information in the depth direction (D) a D b For a given cutter, the height dimension of the lowest dimension in time t1 is set to D in the image being imaged. a1 Set the height of the highest digit to D. b1 If time t elapses, plot the lowest dimension height D. a And the height dimension D of the highest position b Then obtain Figure 11 (a) The figure (image) of the parallelogram shown.

[0080] Similarly, Figure 11 (b) is the contact state information (θ) in the rotation direction. a 、(θ a +θ b For a given cutting tool, the contact phase closest to the reference phase θ0 in time t1 is set as θ. a1 The contact phase furthest from the reference phase θ0 is set as (θ a1 +θ b1 If accompanied by an elapsed time t, plot the contact phase θ. a and contact phase (θ) a +θ b ), then obtain Figure 11 (b) shows the figure (image) of the parallelogram.

[0081] Moreover, the image data generation unit 4, such as Figure 12 As shown, for each of the first to fourth cutting blades, image data is generated that combines the images of the contact state information in the depth direction, which changes with time, and the images of the contact state information in the rotation direction.

[0082] In addition, Figure 12 In the middle, part a is located with tool T. Figure 5 The image corresponding to the contact state information near the position shown, part b is the position of tool T. Figure 6The contact state information around the position shown in the drawing corresponds to an image. Also, Figure 13 (a) is a graph showing an enlarged view of the time axis of the a part of Figure 12 Figure 13 (b) is a graph showing an enlarged view of the time axis of the b part of Figure 12 According to Figure 13 it is known that, in the case where the tool T is located in the vicinity shown in the drawing, the contact state information D Figure 6 in the depth direction becomes longer than in the case where the tool T is located in the vicinity shown in the drawing, and on the other hand, the values of the contact state information θ Figure 5 in the rotational direction become larger. a b p a b b

[0083] Then, the image shown in the drawing is displayed on the display section 5, which is generated by the image data generating section 4. Figure 12

[0084] According to the machining state information estimation device 1 of the present example having the above structure, first, the machining state information estimation section 3 estimates the contact state information in the depth direction (D a , D b ) and the contact state information in the rotational direction (θ a , (θ a + θ b )) of the imaginary workpiece W and the tool T according to the workpiece information, the tool information, and the control information stored in the information storage section 2, and moves the workpiece W and the tool T relative to each other in accordance with the control information.

[0085] Then, according to the contact state information in the depth direction (D a , D b ) and the contact state information in the rotational direction (θ a , (θ a + θ b )) estimated by the machining state information estimation section 3, the image data generating section 4 generates an image along the time axis showing the contact state information in the depth direction (D a , D b ) and the contact state information in the rotational direction (θ a , (θ a + θ b )), and displays the generated image on the display section 5.

[0086] ​​​​​​​​Thus, according to the processing state information estimation device 1, the contact state information (D a , D b ) in the depth direction and the contact state information (θ a , (θ a + θ b )) in the rotational direction of each cutting blade of the tool T in contact with the workpiece W are imaged along the time axis and displayed on the display section 5 when the tool T is relatively moved with respect to the workpiece W in accordance with the control information, so that the time when each cutting blade of the tool T is in contact with the workpiece W, the contact state in the depth direction, and the contact state in the rotational direction can be objectively recognized by observing the image along the time axis. Furthermore, by recognizing the contact state, the load state acting on the tool T can be estimated.

[0087] In addition, the image represents the contact state in the depth direction and the contact state in the rotational direction of the tool T with respect to the workpiece W along the time axis, so that conversely, according to the image, by combining and analyzing information such as the nominal diameter and the rotational speed of the tool T, the shape of the workpiece W processed by the tool T or the movement path of the tool T, and the like can be recognized, and by analyzing the path in which the tool T moves, the NC program used in the processing can be reproduced. Thus, even in the case where the NC program is lost due to some reason, by analyzing the image (contact state image) representing the relationship between the time and the contact state information corresponding to the NC program, the lost NC program can be reproduced.

[0088] Further, in the first embodiment described above, the contact state information of the tool with respect to the workpiece is imaged as the processing state information, but the imaged processing state information is not limited to the contact state information, and can be information related to the cutting resistance acting on the tool, information related to the surface roughness of the workpiece, information related to the depth of cut in the rotational direction of the tool, and the like. In addition, the image is not limited to the image in which the characteristics of the processing state information of the above example are represented as a graph, and can be an image including information related to color, and can be an image including a plurality of color elements. Hereinafter, variations with respect to these modes will be described.

[0089] (Variation 1 of the First Embodiment)

[0090] This variation 1 is a mode in which information related to the depth of cut in the rotational direction of the tool as the processing state information is imaged by color information.

[0091] As shown in Figs. 21(a) and 21(b), the image in which the depth of cut in the rotational direction of the tool is imaged by color information is displayed on the display section 5. Figure 14 (a), Figure 14(b) shows that, in a case where the end mill, i.e., the tool T having four twist cutters (cutting knives) is moved in a tool feed direction indicated by an arrow and the workpiece W is processed, the depth of cut (cutting amount) gradually increases after each cutting knife (first to fourth knives) contacts the workpiece W, and then becomes a non-contact state as it leaves the workpiece W. The state in which each cutting knife contacts the workpiece W is one of the processing state information, but as explained in the first embodiment described above, this contact state information can be expressed as a graph formed as a parallelogram. Furthermore, the change in the cutting amount along the time axis can be expressed as a change in the tint or color of a single color within the graph.

[0092] Figure 15 An example in which the change in the cutting amount along the time axis is expressed as a change in the tint of a single color is shown. As shown in Figure 16 (a), the brightness of a color (red in this example) is divided into 256 levels, and the maximum cutting amount is assigned a brightness of 255, and the cutting amount of 0 is assigned a brightness of 0. In Figure 16 (b), the cutting amount of the portion having a brightness of 100 at time tl is 39% of the maximum value. In this way, by expressing the cutting amount as a change in the tint of a color, the change in the cutting amount can be easily and objectively recognized. In addition, the state of variation in the cutting amount that varies due to the eccentricity of the unequal-pitch end mill, the eccentricity of the tool T, the variation in the rotational speed of the tool, and vibration and the like can be expressed.

[0093] Here, of course, the gray scale is included in the tint of a color. In addition, in a case where the change in the cutting amount along the time axis is expressed as a change in the color, it can also be expressed as a change in the hue. For example, in a case where a color is expressed by the RGB system, a case where the cutting amount is 0 can be set to blue (B), a case where the cutting amount is the maximum can be set to red (R), and an intermediate value can be set to green (G), and expressed in a manner that changes in 256 levels or more. In addition, of course, a color can also be expressed by other color spaces such as the CMYK system or the YUV system. Furthermore, in Figure 15 and Figure 16 the color change is expressed by the gray scale for convenience.

[0094] (Variation 2 of the First Embodiment)

[0095] This variation 2 is a manner in which information related to the cutting resistance acting on the tool as the processing state information is imaged by color information.

[0096] As shown in Figure 17 (a), Figure 17(b) shown, in the case where the tool T described above is moved in the tool feed direction indicated by the arrow and the workpiece W is machined, a cutting resistance is applied to each cutting blade (first blade to fourth blade), the cutting resistance gradually increases as the amount of cutting increases after the tool T comes into contact with the workpiece W, and then becomes 0 when the cutting blade comes out of contact with the workpiece W. Moreover, the cutting resistance can be expressed as components Fx, Fy, Fz in the X-axis direction, Y-axis direction, and Z-axis direction of the moving axis of the tool T.

[0097] As described above, the contact state information of each cutting blade can be expressed as a figure formed as a parallelogram. Moreover, the changes in the components Fx, Fy, Fz of the cutting resistance along the time axis can be expressed as changes in color within the figure. Furthermore, it is not limited to a parallelogram as long as it is a figure that can be distinguished from other regions.

[0098] Figure 18 The changes in the components Fx, Fy, Fz of the cutting resistance along the time axis are expressed as changes in color in the RGB series. As shown in Figure 19 (a), for example, the components Fx, Fy, Fz are expressed as changes in color in 256 levels, Fx = 255 is defined when Fx is the maximum value, Fy = 0, and Fz = 0, G = 255 is defined when Fy is the maximum value, Fx = 0, and Fz = 0, and B = 255 is defined when Fz is the maximum value, Fx = 0, and Fy = 0. Moreover, the values of R, G, B are assigned according to the values of the components Fx, Fy, Fz. For example, as shown in Figure 19 (b), at time t1, the portion where R = 100, G = 70, and B = 75 indicates that Fx is 39% of its maximum value, Fy is 27% of its maximum value, and Fz is 29% of its maximum value. Furthermore, even in this case, as described above, the components Fx, Fy, Fz can be expressed as changes in color in levels of 256 or more, and the color can be expressed by other color spaces such as the CMYK series or the YUV series. In addition, in Figure 18 and Figure 19 gray scale is used for convenience.

[0099] In this way, by expressing the cutting resistance as a change in color, the changes in the cutting resistance can be easily and objectively recognized. In addition, the state of changes in the cutting resistance due to the unequal-pitch end mill, eccentricity of the tool T, variation in the rotational speed of the tool, and vibration, etc. can be expressed, and this can be expressed by different coordinate systems such as the tool coordinate system or the workpiece coordinate system. Furthermore, in Figure 17 (a), Figure 17 (b), the case of upward cutting (up-cut) is shown, but the same can of course be expressed in the case of downward cutting (down-cut).

[0100] (Modification 3 of the first embodiment)

[0101] This variation 3 describes a method for compressing image data generated to represent processing state information. In the first embodiment described above, which images information related to the contact state between tool T and workpiece W (contact state information), the generated image data can be compressed by using color information to represent the parallelogram shape of the image.

[0102] use Figure 20 Explain the method used to compress this data. For example... Figure 20 As shown, if the area displaying the image is D max With D min Between these, if 40 pixels are allocated to line 1 along the D-axis, then the image data has 40 pixels of data along line 1 along the D-axis, and this 40-pixel data along line 1 is the data configured along the time axis.

[0103] Furthermore, assuming the color within the parallelogram region is white (image data "1") and its background is black (image data "0"), if Figure 20 If the top 10 pixels of the image in line 1 at time t1 are black, the next 21 pixels are white, and the next 9 pixels are black, then the image data for line 1 is "00000000, 00111111, 11111111, 11111110, 00000000". If we represent each 8-pixel data as 8 bits per byte, the values ​​are "0, 63, 255, 254, 0". Furthermore, if we represent this as 8-bit / 24-bit color information, we can represent 15 image data points sequentially: RGB = (0, 0, 0), RGB = (63, 63, 63), RGB = (255, 255, 255), RGB = (254, 254, 254), and RGB = (0, 0, 0). Furthermore, even in this case, as described above, color variations of 256 levels or more can be represented. Additionally, this can also be represented using other color spaces such as the CMYK or YUV series.

[0104] As mentioned above, compared to image data where each pixel is represented as "0" or "1", representing each pixel as 8-bit / 24-bit color information can compress the image data. Moreover, by representing image data as color information in this way, it can be converted into image processing information suitable for machine learning. In addition, by compressing the data, changes in processing state information can be easily represented.

[0105] In addition, Figure 20In the example shown, data compression is performed along the depth direction of the cutting blade, i.e., along the D-axis. However, data compression along the time axis is also possible, meaning two-dimensional data compression is feasible. Furthermore, in... Figure 20 In the example shown, for instance, multiple machining state information can also be represented by color information in such a way that R in the color information is assigned to the contact state information, G is assigned to the cutting amount in the rotational direction of the tool T, and B is assigned to the cutting resistance.

[0106] (Second Implementation)

[0107] Next, according to Figure 21 The processing status diagnostic device according to the second embodiment of the present invention will be described. Figure 21 As shown, the processing status diagnostic device 10 in this example, in addition to the processing status information estimation device 1 in the first embodiment described above, also includes a processing status diagnostic unit 12 and a learning information storage unit 11. The processing status diagnostic unit 12, besides the display unit 5, is also composed of a computer including a CPU, RAM, ROM, etc. The processing status information estimation unit 3, the image data generation unit 4, and the processing status diagnostic unit 12 can implement their functions through a computer program. The processing status information estimation unit 3 and the image data generation unit 4 perform the aforementioned processing, and the processing status diagnostic unit 12 performs the processing described later. Furthermore, the information storage unit 2 and the learning information storage unit 11 are composed of suitable storage media such as RAM. Additionally, the display unit 5 is composed of a display such as a touch panel.

[0108] The learning information storage unit 11 is a functional unit that stores the relationship information between the actual processing status information obtained when each of the multiple control information is actually processed by the NC machine tool and the estimated processing status information estimated by the processing status information estimation unit 3 based on the corresponding control information. This relationship information is stored externally in advance.

[0109] This relational information is obtained through experience from actual machining state information obtained during actual machining using multiple control information on the NC machine tool, and estimated machining state information estimated by the machining state information estimation unit 3 based on the corresponding control information, for example, through machine learning. Furthermore, the actual machining state information may include, for example, cutting loads detected by a measuring instrument (measuring head) installed on the NC machine tool or vibrations generated during cutting, but is not limited to these.

[0110] The processing state diagnosing section 12 receives the processing state information detected by the measuring device (measuring head) provided to the NC machine tool from the measuring device when processing is performed using the prescribed control information by the NC machine tool, and diagnoses whether the processing performed by the NC machine tool is in a normal state based on the received processing state information, the estimated processing state information estimated by the processing state information estimating section 3 based on the corresponding control information, and the relationship information stored in the learning information storage section 11, and displays the diagnosis result on the display section 5.

[0111] For example, in a case where the processing state information obtained when processing is performed using the prescribed control information exceeds the allowable range set with respect to the estimated processing state information estimated based on the corresponding control information and the actual processing state information that should be standard derived from the relationship information, the processing state diagnosing section 12 diagnoses that the processing state in the processing is abnormal, and in a case where it is within the allowable range, diagnoses that the processing state is normal.

[0112] According to the processing state diagnosing apparatus 10 of the present example having the above structure, in a case where processing is performed using the prescribed control information by the NC machine tool, first, the processing state information estimating section 3 estimates the estimated processing state information using the same control information as the control information used for the processing. Further, the processing state diagnosing section 12 diagnoses whether the processing performed by the NC machine tool is in a normal state based on the processing state information received by the measuring device (measuring head) provided to the NC machine tool, the estimated processing state information estimated by the processing state information estimating section 3, and the relationship information stored in the learning information storage section 11, and displays the diagnosis result on the display section 5.

[0113] The processing state of the NC machine tool differs, for example, depending on the contact state of the tool T with the workpiece W, and also differs depending on the wear state of the tool T, and the like. According to the processing state diagnosing apparatus 10, the processing state that varies depending on the contact state of the tool T with the workpiece W, and the like is learned in advance, and based on the learning result, it is determined whether the processing state is normal, and thus the diagnosis of the processing state can be performed accurately.

[0114] The above describes the embodiments of the present application, but the specific embodiments that the present application can take are not limited to the above-described first embodiment or the modifications thereof and the second embodiment.

[0115] For example, in the above example, as the machining state information, the contact state information of the tool T and the work W in the contact depth direction of the tool T and the work W, the contact state information of the tool T and the work W in the rotation direction of the tool, information relating to the cutting resistance acting on the tool T, information relating to the surface roughness of the work W, and information relating to the cutting depth in the rotation direction of the tool are exemplified as the machining state information, but are not limited thereto, and in addition thereto, information relating to the chatter of the tool can be imaged. In this case, the longitudinal axis can be taken as the contact depth, and the lateral axis can be taken as the time / chatter period (=t / Tc), so that the lateral axis corresponds to the stable pocket No. of the stable pocket theory in the reproduction of the chatter, and thus the image data can be easily corresponded to the stable pocket theory. Alternatively, the longitudinal axis can be taken as the contact depth, and the lateral axis can be taken as the time, and the pixel can be taken as the rotation period / chatter period, so that since the pixel corresponds to the stable pocket No. of the stable pocket theory in the chatter, the image data can be easily corresponded to the stable pocket theory as well.

[0116] In addition, in the machining state diagnosis device 10 described above, the relationship information learned externally is stored in the learning information storage section 11, but a learning processing section that performs the learning processing can be provided in the device, and the relationship information obtained based on the processing result of the learning processing section can be stored in the learning information storage section 11.

[0117] Although the above-described aspects of the embodiments are described repeatedly, all of the aspects of the above-described embodiments are exemplary and are not restrictive. The skilled person can appropriately modify and change them. The scope of the present application is not shown by the above-described embodiments, but is shown by the claims. Furthermore, the modification of the embodiments within the scope equivalent to the scope of the claims is included in the scope of the present application.

[0118] [Explanation of Symbols]

[0119] 1 Machining state information estimation device

[0120] 2 Information storage section

[0121] 3 Machining state information estimation section

[0122] 4 Image data generation section

[0123] 5 Display section

[0124] 10 Machining state diagnosis device

[0125] 11 Learning information storage section

[0126] 12 Machining state diagnosis section

[0127] T Tool

[0128] W Workpiece

Claims

1. A processing status information estimation device, characterized in that, have: The information storage unit stores control information used in the machining of the NC machine tool, information related to the workpiece machined by the NC machine tool, and information related to the tools used in the NC machine tool. The machining state information estimation unit, after configuring a hypothetical workpiece and tool, set according to workpiece and tool-related information stored in the information storage unit, in a hypothetical space in the same positional relationship as the positional relationship based on the control information, moves the configured hypothetical workpiece and tool relative to each other according to the control information stored in the information storage unit. As the workpiece and tool move relative to each other, the unit estimates the machining state of the hypothetical workpiece as it changes over time. The machining state information estimation unit estimates machining state information, which includes at least one piece of information selected from: information related to the contact state of each cutting blade of the tool with the workpiece in the contact depth direction between the tool and the workpiece; information related to the contact state of each cutting blade of the tool with the workpiece in the rotation direction of the tool; information related to the cutting resistance acting on each cutting blade of the tool; information related to the surface roughness of the workpiece; and information related to the cutting depth of each cutting blade in the rotation direction of the tool. as well as The image data generation unit generates image data that represents the magnitude of the processing state information along the time axis, based on the processing state information estimated along the time axis by the processing state information estimation unit. When the processing state information includes information concerning the contact state of each cutting blade of the tool with the workpiece in the contact depth direction between the tool and the workpiece, the image data generation unit generates image data, based on the lowest and highest height dimensions, graphically and / or color-representing the information concerning the contact state of each cutting blade of the tool with the workpiece for at least one of the cutting blades; or When the processing state information includes information related to the contact state between the cutting blade of the tool and the workpiece in the rotation direction of the tool, the image data generation unit generates, for at least one of the cutting blades, information related to the contact state between each cutting blade of the tool and the workpiece in the rotation direction of the tool, based on the contact phase closest to the reference phase and the contact phase farthest from the reference phase, and graphically and / or in color representing this contact state.

2. The processing status information estimation device according to claim 1, characterized in that, The image data generation unit is configured to generate color image data composed of multiple color elements as the image data. The machining state information estimation unit is configured to estimate machining state information, which includes information related to the contact state of each cutting blade of the tool with the workpiece in the contact depth direction between the tool and the workpiece, information related to the contact state of each cutting blade of the tool with the workpiece in the rotation direction of the tool, information related to the cutting resistance acting on each cutting blade of the tool, information related to the surface roughness of the workpiece, and information related to the cutting depth of each cutting blade in the rotation direction of the tool, and information corresponding to the number of color elements. One of the information estimated by the processing status information estimation unit is assigned to each of the color elements.

3. The processing status information estimation device according to claim 1, characterized in that, The processing status information estimation device includes a display unit, which displays an image based on the image data generated by the image data generation unit.

4. The processing status information estimation device according to claim 2, characterized in that, The processing status information estimation device includes a display unit, which displays an image based on the image data generated by the image data generation unit.

5. A processing status diagnostic device, characterized in that, The device for estimating processing status information according to any one of claims 1 to 4, and further comprises: The learning information storage unit stores relational information learned through experience. This relational information associates estimated processing state information (obtained when hypothetically machining a workpiece using the estimation control information) with actual processing state information (obtained when actually machining using the estimation control information). The processing status diagnosis unit diagnoses the appropriateness of the processing status in actual processing using the control information based on the processing status information obtained in actual processing using the prescribed control information, the estimated processing status information estimated by the processing status estimation unit of the processing status information estimation device based on the control information, and the relational information stored in the learning information storage unit.

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