Processed surface determination device, determination program, and processed surface determination method

Abstract

To determine occurrence of degradation abnormality in a tool in a short time.SOLUTION: A processed surface determination device 30 has a storage part 56, an extraction image generation part 57, and a determination part 58. The storage part 56 stores a basic image 44B which is a photographic image 44 obtained by photographing an abrasive plane in a first period, a latest image 44N which is the photographic image 44 obtained by photographing the abrasive plane in a second period later than the first period, and a learned first learning device 61. The extraction image generation part 57 generates a determination image 65 which is an image obtained by extracting a tip part of the abrasive plane from a difference image indicating a difference between the basic image 44B and the latest image 44N, using the basic image 44B, the latest image 44N, and the learned first learning device 61. The determination part 58 determines occurrence of degradation abnormality in the abrasive grain, under the condition that deviation of distribution of the tip part of the abrasive plane in the determination image 65 is equal to or higher than a predetermined level.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a machined surface judging device that judges the state of a machined surface of a tool, a judging program used in the device, and a machined surface judging method that judges the state of a machined surface of a tool. [Background technology]

[0002] In grinding processing using a grinding device, the finish quality of the machined surface of a workpiece depends on the condition of the machining surface (so-called grinding surface) of a grinding wheel serving as a grinding tool. Conventionally, in order to grasp the condition of the grinding surface of a grinding wheel, a measuring device has been proposed for measuring the condition of the grinding surface (specifically, the amount of protrusion of abrasive grains) (for example, Patent Document 1). In the device described in Patent Document 1, the grinding surface is imaged using a metallurgical microscope equipped with a camera, and the condition of the grinding surface is measured based on the image data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2004-45078 A Summary of the Invention [Problem to be solved by the invention]

[0004] In a device using an electron microscope, the surface properties of the grinding surface can be observed in detail using the electron microscope. However, since the observation range of the electron microscope is very narrow, it takes a long time to measure the condition of the grinding surface over the entire surface using the electron microscope. Moreover, if the condition of the grinding surface is judged for each observation range of the electron microscope, i.e., each measurement range of the measuring device, the judgment also takes a long time.

[0005] This situation is also generally the same for devices that judge the condition of the machined surface of a wire tool used in wire saw machining or wire electric discharge machining. [Means for solving the problem]

[0006] A machined surface evaluation device for solving the above problem, in which the machined surface evaluation device evaluates the condition of the grinding surface of a grinding wheel, includes a memory unit that stores a trained learning device that has been trained using as learning data a first captured image, which is an image of the grinding surface of the grinding wheel to be evaluated at a first time, a second captured image, which is an image of the grinding wheel to be evaluated at a second time later than the first time, and a tip image, which is an image showing only the tip portion of the grinding surface of the grinding wheel; an extracted image generation unit that uses the first captured image, the second captured image, and the trained learning device to generate an extracted image, which is an image of the tip portion of the grinding surface extracted from a difference image that shows the difference between the first captured image and the second captured image; and a judgment unit that judges the occurrence of a deterioration abnormality in the grinding wheel to be evaluated if the distribution bias of the tip portion of the grinding surface in the extracted image is equal to or greater than a predetermined level.

[0007] The judgment program for solving the above problem is a judgment program that causes an electronic control device provided in the machined surface judgment device to execute the processing of the memory unit, the extracted image generation unit, and the judgment unit provided in the machined surface judgment device.

[0008] A grinding surface evaluation method for solving the above problem is a grinding surface evaluation method for evaluating the condition of the grinding surface of a grinding wheel, and includes a storage step of storing a first captured image, which is an image of the grinding surface of the grinding wheel to be evaluated at a first time, a second captured image, which is an image of the grinding wheel to be evaluated at a second time later than the first time, and a trained learning device that has been trained using as learning data a tip image, which is an image showing only the tip portion of the grinding surface of the grinding wheel; an extraction image generation step of using the first captured image, the second captured image, and the trained learning device to generate an extraction image, which is an image of the tip portion of the grinding surface extracted from a difference image showing the difference between the first captured image and the second captured image; and a judgment step of judging the occurrence of a deterioration abnormality in the grinding wheel to be evaluated if the bias in the distribution of the tip portion of the grinding surface in the extraction image is equal to or greater than a predetermined level.

[0009] It is desirable for the condition of the grinding surface of a grinding wheel to change uniformly over the entire surface. Therefore, if there is a large deviation in the amount of change in condition over different parts of the grinding surface, it can be said that the condition of the grinding surface is changing in an undesirable way.

[0010] In the above-mentioned machined surface judgment device, judgment program, and grinding surface judgment method, the amount of change in the condition of the tip portion of the grinding surface, which determines the finishing quality of the workpiece, appears in an extracted image in which the tip portion of the grinding surface is extracted from a difference image showing the difference between the first and second captured images.

[0011] According to the above-mentioned machined surface evaluation device, evaluation program, and grinding surface evaluation method, the amount of change in the state of the tip of the grinding surface at each part of the grinding surface can be grasped based on the position and size of the tip of the grinding surface in the extracted image. Therefore, if there is a bias in the distribution of the tip of the grinding surface in the extracted image, it can be understood that there is a large bias in the amount of change in state at the tip of the grinding surface. In this case, it can be determined that the grinding wheel has deteriorated abnormally.

[0012] Moreover, by using two captured images (the first captured image and the second captured image) and a trained learning device, an extracted image can be generated that is an image that represents the amount of change in the state of the tip portion of the grinding surface at each part of the grinding surface. Therefore, compared to the case where the state of the grinding surface is analyzed for each finely divided area of ​​the grinding surface to grasp the amount of change in the state of the tip portion of the grinding surface at each part of the grinding surface, the amount of change in the state of the tip portion of the grinding surface can be grasped easily in a short time. Therefore, the occurrence of deterioration abnormalities in the grinding wheel can be determined in a short time. Effect of the Invention

[0013] According to the present invention, occurrence of deterioration or abnormality in a tool can be determined in a short period of time. [Brief description of the drawings]

[0014] [Figure 1] 1 is a schematic diagram showing a schematic configuration of an automatic grinding device to which a machined surface determination device of an embodiment is applied; [Diagram 2] 1 is a block diagram showing a schematic configuration of a machined surface determining device; [Diagram 3] 2 is a schematic diagram showing the structure of a camera unit. [Figure 4] 1 is a schematic diagram showing how an imaging target area of ​​the grinding surface is illuminated by an illumination unit. [Diagram 5] 1 is a schematic diagram conceptually showing the data structure of learning data of a first learning device. [Figure 6] FIG. 13 is a diagram showing an example of learning data corresponding to an evenly abrasive-fell surface; [Figure 7] FIG. 13 is a diagram showing an example of learning data corresponding to an unbalanced abrasive surface; [Figure 8] FIG. 13 is a diagram showing an example of learning data corresponding to a chip welding grinding surface. [Figure 9] 13 is a flowchart showing a procedure for generating a determination image. [Figure 10] 13 is a flowchart showing a procedure for executing a determination process. [Figure 11] 13 is a flowchart showing an execution procedure of a determination process according to another embodiment. [Figure 12] 13 is a graph showing the relationship between the rotational phase of the grindstone and the integrated value when deterioration of the grindstone progresses. [Figure 13] 13 is a flowchart showing an execution procedure of a determination process according to another embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of a machined surface judging device, a judging program, and a grinding surface judging method will be described with reference to FIGS. <Automatic grinding device> As shown in FIG. 1, the machined surface judgment device 30 of this embodiment is applied to an automatic grinding device 20. This automatic grinding device 20 is a numerically controlled (NC) type surface grinding device that grinds the surface of a workpiece (not shown) using a grinding wheel 21 as a tool. The grinding wheel 21 for grinding has a structure in which a large number of abrasive grains are fixed to the outer circumferential surface of a cylindrical base material by a binder. The grinding wheel 21 is supported by a grinding wheel support part 22 in a rotatable state. The grinding wheel 21 and the grinding wheel support part 22 are movable relative to a base 23 in the vertical direction (hereinafter, Y direction) and the horizontal direction, specifically, the depth direction in FIG. 1 (hereinafter, Z direction). In the automatic grinding device 20, the grinding wheel 21 is brought into contact with the workpiece while being rotationally driven, thereby grinding the surface of the workpiece.

[0016] The automatic grinding device 20 has a table 24 as a support base for supporting a workpiece. The table 24 is movable in a horizontal direction perpendicular to both the Z direction and the Y direction, specifically, in the left-right direction in FIG. 1 (hereinafter, referred to as the X direction). In the automatic grinding device 20, the workpiece on the table 24 can be moved by moving the table 24 in the X direction. This makes it possible to change the relative positions of the grinding wheel 21 and the workpiece in the X direction.

[0017] 1 and 2, the automatic grinding device 20 has an operation control unit 25 that controls the operation of the automatic grinding device 20. The operation control unit 25 has an X-axis drive unit 26, a Y-axis drive unit 27, a Z-axis drive unit 28, and a grindstone drive unit 29.

[0018] The automatic grinding device 20 can change the relative positions of the grindstone 21 and the workpiece in the X direction, the Y direction, and the Z direction, respectively, by moving the grindstone 21 in the Y direction and the Z direction and by moving the table 24 in the X direction. The X-axis drive unit 26 is a device for moving the table 24 in the X direction relative to the base 23. The X-axis drive unit 26 has a ball screw mechanism, a servo motor, and the like, which are not shown. The X-direction position of the table 24 relative to the base 23 is controlled through the operation control of the X-axis drive unit 26. The Y-axis drive unit 27 is a device for moving the grindstone 21 and the grindstone support unit 22 in the Y direction relative to the base 23. The Y-axis drive unit 27 has a ball screw mechanism, a servo motor, and the like, which are not shown. The Y-direction position of the grindstone 21 relative to the base 23 is controlled through the operation control of the Y-axis drive unit 27. The Z-axis drive unit 28 is a device for moving the grindstone 21 and the grindstone support unit 22 in the Z direction relative to the base 23. The Y-axis driving unit 27 has a ball screw mechanism, a servo motor, etc. (not shown). Through the operation control of the Y-axis driving unit 27, the position of the grindstone 21 in the Z direction relative to the base 23 is controlled.

[0019] The grindstone driving unit 29 has an electric motor connected to the rotation shaft of the grindstone 21, a rotation phase sensor that detects the rotation phase of the grindstone 21, and the like. In the operation control of the grindstone driving unit 29 (more specifically, the electric motor), the grindstone 21 is basically rotated at a constant speed. The rotation speed of the grindstone 21 is set based on the processing conditions input to the automatic grinding device 20 and is set to a speed that matches the processing conditions.

[0020] <Machine surface determination device> The machined surface judgment device 30 of this embodiment is a device that judges the occurrence of deterioration abnormality in the grinding wheel 21 to be judged, using a captured image 44 obtained by capturing an image of the grinding surface of the grinding wheel 21 to be judged and trained learning devices 61, 62.

[0021] <Camera unit> 1 to 3, the machined surface determining device 30 has a camera unit 40. The camera unit 40 captures an image of the grinding surface 211 of the grindstone 21. The camera unit 40 has a camera 41 as an imaging section, and an illumination section 43.

[0022] <Camera> As shown in FIG. 1 and FIG. 3, the camera 41 is disposed below the grinding wheel 21. The camera 41 captures an image of the grinding surface 211 of the grinding wheel 21 from a direction perpendicular to the grinding surface 211 (specifically, the tangent plane of the grinding surface 211 shown by the dashed line in FIG. 3). In this embodiment, the camera 41 captures the image of the grinding surface 211 at a first time point immediately after the dressing of the grinding wheel 21 is performed. The captured image 44 captured at the first time point is stored in the electronic control device 50, which will be described later, as a basic image 44B. In this embodiment, the camera 41 also captures the image of the grinding surface 211 at a second time point after the first time point, specifically, at any timing during the use of the grinding wheel 21. The captured image 44 captured at the second time point is stored in the electronic control device 50 as a latest image 44N. Among the captured images 44 captured at any timing during use of the grinding wheel 21, a previously captured image 44 may be stored as an N-th (N is a natural number) previous image, for example, by storing the captured image 44 captured immediately before as the previous image. In this embodiment, the grinding wheel 21 attached to the automatic grinding device 20 when capturing an image by the camera 41 corresponds to the tool to be determined. In this embodiment, the basic image 44B corresponds to the first captured image, and the latest image 44N and the N-th previous image correspond to the second captured image.

[0023] <Lighting Department> 3, the illumination unit 43 illuminates the lower end portion of the grinding surface 211, i.e., the area imaged by the camera 41 (hereinafter, image capture target area AR). The illumination unit 43 has a first irradiating unit 45 and a second irradiating unit .

[0024] <1st irradiation section> The first irradiation unit 45 irradiates the above-mentioned imaging target area AR of the grinding surface 211 with a first illumination light 45L having a first emission color (for example, blue) from a direction perpendicular to the grinding surface 211 (specifically, the tangent plane of the grinding surface 211 shown by the dashed line in FIG. 3). Specifically, the first irradiation unit 45 has a first lamp 451 and a reflective translucent member 452. The first lamp 451 is composed of a light-emitting diode. The first lamp 451 is disposed in such a manner that the first illumination light 45L is emitted from a direction perpendicular to the optical axis of the camera 41 toward the optical axis. The reflective translucent member 452 is disposed at a portion where the optical axis of the camera 41 and the optical axis of the first lamp 451 intersect. The reflective translucent member 452 reflects the first illumination light 45L and transmits the reflected light reflected by the grinding surface 211. In this embodiment, the first illumination light 45L emitted from the first lamp 451 is reflected by the reflective light-transmitting member 452 and travels toward the grinding surface 211, thereby irradiating the imaging target area AR on the grinding surface 211. A portion of the reflected light of the first illumination light 45L thus irradiated to the grinding surface 211 is transmitted through the reflective light-transmitting member 452 and enters the camera 41.

[0025] <Second irradiation section> The second irradiating unit 46 irradiates the imaging target area AR of the grinding surface 211 from an oblique direction with respect to the grinding surface 211 (specifically, the tangent plane of the grinding surface 211 shown by the dashed line in FIG. 3) with the second illumination light 46L having a second emission color (for example, white color) different from the first emission color. Specifically, the second irradiating unit 46 has an illumination dome 461 and a second lamp 462. The illumination dome 461 is dome-shaped with an open top, and is disposed so as to cover the lower surface of the grinding wheel 21 (specifically, the imaging target area AR). The second lamp 462 is constituted by a light-emitting diode. A plurality of the second lamps 462 are provided in a line on the inner surface of the illumination dome 461. Each second lamp 462 irradiates the imaging target area AR of the grinding surface 211 with the second illumination light 46L. The second lamp 462 includes a lamp that irradiates the second illumination light 46L onto the imaging target area AR of the grinding surface 211 from a direction along the grinding surface 211 (more specifically, the tangent plane of the grinding surface 211 shown by the dotted line in Figure 3), specifically, from a direction that forms "5 degrees" with respect to the tangent plane of the grinding surface 211.

[0026] <Action by lighting unit> In this embodiment, the camera unit 40 is detachable from the automatic grinding device 20. When judging the state of the grinding surface 211, the camera unit 40 is attached to the automatic grinding device 20 to obtain a captured image 44 used for the judgment. In the camera unit 40, when the camera 41 captures an image of the grinding surface 211, the illumination unit 43 illuminates an imaging target area AR of the grinding surface 211. Note that in this embodiment, when grinding is performed by the automatic grinding device 20, the camera unit 40 is detached from the automatic grinding device 20.

[0027] 4, in this embodiment, the first illumination light 45L irradiated from the first irradiating unit 45 in a direction perpendicular to the grinding surface 211 can be substantially specularly reflected by the top surface of the grinding wheel 21 (including the top surfaces of the abrasive grains 212) and can be made to enter the camera 41. This allows the color of the portion corresponding to the top surface of the grinding wheel 21 in the captured image 44 captured by the camera 41 to be the first emission color (e.g., blue) of the first illumination light 45L.

[0028] On the other hand, the first illumination light 45L from the first irradiating unit 45 and the second illumination light 46L from the second irradiating unit 46 are diffusely reflected by portions of the grindstone 21 other than the top surface (specifically, the side surfaces of the abrasive grains 212, the surface of the binder, etc.). Then, a portion of the diffusely reflected first illumination light 45L and second illumination light 46L enters the camera 41. As a result, the color of the portions of the grindstone 21 other than the top surface in the captured image 44 captured by the camera 41 becomes a mixture of the first emission color of the first illumination light 45L and the second emission color of the second illumination light 46L.

[0029] According to this embodiment, since the color of the top surface of the grinding wheel 21 in the captured image 44 can be made different from the color of the portion other than the top surface, the boundary between the top surface of the grinding wheel 21 and the portion other than the top surface can be made clear. This makes it easy to extract the tip portion of the grinding surface 211 (more specifically, the portion where the abrasive grains 212 function as cutting edges) from the captured image 44. Therefore, it is easy to generate an extracted image, which is an image in which the tip portion of the grinding surface 211 is extracted from the captured image of the grinding surface 211.

[0030] <Electronic control device> The machined surface judgment device 30 of this embodiment includes an electronic control device 50. The electronic control device 50 executes various controls for judging the state of the grinding surface 211 of the grinding wheel 21.

[0031] The electronic control device 50 has a CPU 51, a ROM 52, a RAM 53, an input unit 54, a display unit 55, and a storage unit 56. The input unit 54 is an input device such as a mouse, a keyboard, a touch panel, or a button.

[0032] The input unit 54 is operated when performing various settings of the machined surface judging device 30 or when operating the machined surface judging device 30. In this embodiment, the input unit 54 is operated to input the time when dressing was performed on the grinding wheel 21 to be judged (dressing information).

[0033] The display unit 55 has a display device such as a liquid crystal display, an organic EL display, etc. The display unit 55 displays various information related to the judgment, such as the judgment result of the state of the grinding surface 211. The storage unit 56 is configured with a non-volatile memory that can be written to and read from at any time, such as a hard disk drive or a solid state drive. The storage unit 56 stores various execution programs 60, trained learning devices 61 and 62, and learning data 63 and 64 used for machine learning of the learning devices 61 and 62. The storage unit 56 also stores the captured image 44 captured by the camera unit 40, a judgment image 65 used to judge the grinding surface 211, and dressing information input through the operation of the input unit 54.

[0034] The execution program 60 includes a program for executing machine learning by the learning devices 61 and 62, and a judgment program for executing various processes related to judging the state of the grinding surface 211.

[0035] <First learning unit> The first learning device 61 will be described. In this embodiment, a Generative Adversarial Network (GAN), specifically an image generation model called "pix2pix", is used as the first learning device 61.

[0036] The first learning device 61 is machine-learned using learning data 63. The learning data 63 is data for performing machine learning of the first learning device 61 so as to acquire the ability to generate an extracted image, which is an image obtained by extracting only the tip portion of the grinding surface 211 of the grinding wheel 21 from an image related to the grinding surface 211 of the grinding wheel 21. As shown in FIG. 5, the learning data 63 is composed of an image set in which a pre-conversion image 63B and a post-conversion image 63A form a pair. The pre-conversion image 63B is an image that is expected to be used as input data for the first learning device 61. The post-conversion image 63A is an image that is expected to become output data for the first learning device 61.

[0037] The learning data 63 is generated in the following manner. First, a large number of captured images of the grinding surface 211 of the grinding wheel 21 are prepared. The captured images include a first image captured at the first time and a second image captured at the second time. The first image is an image corresponding to the basic image 44B, and the second image is an image corresponding to the latest image 44N. The first image and the second image are acquired, for example, by using the camera unit 40. The first image and the second image are converted to grayscale and divided into a plurality of divided images in a manner that cuts the grinding surface 211 in a lattice shape in the circumferential direction and the rotation axis direction of the grinding wheel 21. In this embodiment, these divided images are used as the pre-conversion image 63B.

[0038] Then, for each divided image, an extracted image is created by extracting only the tip portion of the grinding surface 211 (more specifically, the portion of the grinding surface 211 that functions as a cutting edge) from the divided image. In this embodiment, these extracted images are used as the converted image 63A. The following method can be used to create the extracted image. First, the grinding surface 211 of the grinding wheel 21 before imaging is placed against an ink pad to adhere ink to the tip portion of the grinding surface 211. Then, the grinding surface 211 of the grinding wheel 21 is imaged. Then, only the colored portion to which ink is adhered is extracted from this captured image (more specifically, the divided image) to create the extracted image. According to this method, the boundary between the tip portion of the grinding surface 211 and the other portions in the captured image becomes clear, so that the tip portion of the grinding surface 211 can be easily extracted from the divided image. Therefore, the extracted image can be easily created.

[0039] Thereafter, a large number of image sets (FIG. 5) are created, each pair consisting of a pre-conversion image 63B and a post-conversion image 63A of the same location on the grinding surface 211. In this embodiment, these image sets are used as learning data 63 for the first learning device 61. In detail, the electronic control device 50 extracts one of the multiple image sets constituting the learning data 63, and performs machine learning on the first learning device 61 using the image set as input data. This learning process is repeatedly executed for each image set constituting the learning data 63. Then, the machine learning model constructed through the execution of the machine learning is stored in the storage unit 56 as the trained first learning device 61. Note that in this embodiment, the process of storing the trained first learning device 61 in the storage unit 56 corresponds to the storage process.

[0040] When an image relating to the grinding surface 211 of the grinding wheel 21, more specifically, a captured image 44 capturing the grinding surface 211, is input to the trained first learning device 61, the first learning device 61 outputs an extracted image in which only the tip portion of the grinding surface 211 is extracted from the captured image 44. In this embodiment, a judgment image 65 used to judge the grinding surface 211 is generated based on the extracted image.

[0041] <Second learning unit> The second learning device 62 will now be described. In this embodiment, as the second learning device 62, a Convolutional Neural Network (CNN) is adopted.

[0042] The second learning device 62 is subjected to machine learning using the learning data 64. The learning data 64 is data for performing machine learning on the second learning device 62 so that the second learning device 62 acquires the ability to discriminate the state of the grinding surface 211. The learning data 64 is basically generated by the following procedure.

[0043] First, an image set is prepared, which is two captured images of the same location on the grinding surface 211 of the grinding wheel 21, the first image being captured at the first time and the second image being captured at the second time. The first image is an image corresponding to the basic image 44B, and the second image is an image corresponding to the latest image 44N. The first and second images are acquired, for example, by using the camera unit 40. The first and second images are converted to grayscale.

[0044] Thereafter, for each of the first and second grayscale images, an extracted image is created by extracting only the tip portion of the grinding surface 211 (more specifically, the portion of the grinding surface 211 that functions as a cutting edge) from the image. The extracted image can be created using the ink pad described above, using the converted image 63A that constitutes the learning data 64 for the first learning device 61, or using the first learning device 61.

[0045] Then, a difference image is generated that indicates the difference between the extracted image corresponding to the first image and the extracted image corresponding to the second image. In this embodiment, this difference image is used as learning data 64 for the second learning device 62.

[0046] In this embodiment, as the differential images constituting the learning data 64, a large number of images corresponding to three types of grinding surface 211 conditions, namely, "evenly shedding grinding surface", "unbalanced shedding grinding surface", and "chip-welded grinding surface", are prepared.

[0047] The "evenly shedding abrasive surface" is defined as the state of the grinding surface 211 when the abrasive grains 212 are evenly shedding from each part of the grinding surface 211. When generating the learning data 64 corresponding to the evenly shedding abrasive surface, a large number of image sets consisting of the first image and the second image of the grinding wheel 21 having the evenly shedding abrasive surface are prepared, and the above-mentioned difference image is generated based on this image set. In this case, as the difference image, an image showing a part where the tip part of the grinding surface 211 is reduced in the extracted image corresponding to the second image is generated based on the extracted image corresponding to the first image. Then, this difference image is used as the learning data 64 corresponding to the evenly shedding abrasive surface. As an example shown in FIG. 6, the learning data 64 corresponding to the evenly shedding abrasive surface is an image in which the region RT showing the tip part of the grinding surface 211 is evenly distributed over the entire surface of the learning data 64 (more specifically, the above-mentioned difference image).

[0048] The "unbalanced shed grinding surface" is defined as the state of the grinding surface 211 when the portions where the abrasive grains 212 have fallen off are unevenly distributed on the grinding surface 211. When generating the learning data 64 corresponding to the unbalanced shed grinding surface, a large number of image sets consisting of the first image and the second image of the grinding wheel 21 of the unbalanced shed grinding surface are prepared, and the above-mentioned difference image is generated based on this image set. In this case, as the difference image, an image showing a portion where the tip portion of the grinding surface 211 is reduced in the extracted image corresponding to the second image is generated based on the extracted image corresponding to the first image. Then, this difference image is used as the learning data 64 corresponding to the unbalanced shed grinding surface. As an example shown in FIG. 7, the learning data 64 corresponding to the unbalanced shed grinding surface is an image in which the region RT showing the tip portion of the grinding surface 211 is distributed unevenly in a part of the learning data 64 (more specifically, the above-mentioned difference image).

[0049] The "chip-welded grinding surface" is defined as the state of the grinding surface 211 when chips are welded to the grinding surface 211. When generating the learning data 64 corresponding to the chip-welded grinding surface, a large number of image sets consisting of a first image and a second image of the grinding wheel 21 with the chip-welded grinding surface are prepared, and the above-mentioned difference image is generated based on this image set. In this case, as the difference image, an image showing an increased tip portion of the grinding surface 211 in the extracted image corresponding to the second image is generated based on the extracted image corresponding to the first image. Then, this difference image is used as the learning data 64 corresponding to the chip-welded grinding surface. As shown in an example in FIG. 8, the learning data 64 corresponding to the chip-welded grinding surface is an image in which the region RT showing the tip portion of the grinding surface 211 extends over a wide range including a plurality of abrasive grains 212.

[0050] In this embodiment, a learning process in which one of the multiple images constituting the learning data 64 is extracted and the second learning device 62 is made to learn the image by machine learning using the extracted image as input data is repeatedly executed for each image constituting the learning data 64. Then, the machine learning model constructed through the execution of the machine learning is stored in the storage unit 56 as the trained second learning device 62.

[0051] An image (a judgment image 65, described later) relating to the grinding surface 211 of the grinding wheel 21 is input to the second learning device 62 that has been trained in this manner. As a result, the second learning device 62 performs so-called classification, in which the input image is classified into one of an evenly-shed grinding surface (class 1), an unbalancedly-shed grinding surface (class 2), and a chip-welded grinding surface (class 3).

[0052] The machined surface judging device 30 (more specifically, the electronic control device 50) of this embodiment has an extracted image generating section 57 and a judging section 58 as functional sections. <Extracted image generation section> The extracted image generation unit 57 uses the basic image 44B, the latest image 44N, and the trained first learning device 61 to generate an extracted image (hereinafter, judgment image 65) that is an image obtained by extracting the tip portion of the grinding surface 211 from a difference image that shows the difference between the basic image 44B and the latest image 44N.

[0053] FIG. 9 shows a procedure for generating the judgment image 65. As shown in FIG. 9, first, the camera unit 40 is attached to the automatic grinding device 20 on which the grinding wheel 21 to be judged is provided, and the camera unit 40 and the operation control unit 25 of the automatic grinding device 20 are connected to the electronic control device 50 (step S11). Then, the electronic control device 50 controls the operation of the camera unit 40 and the operation control unit 25. In detail, illumination is performed by the illumination unit 43. As a result, the imaging target area AR on the grinding surface 211 is illuminated. In addition, the operation control of each of the driving units 26, 27, 28, and 29 of the automatic grinding device 20 is performed. As a result, the grinding wheel 21 is rotated at a speed suitable for imaging the grinding surface 211, and the relative position between the camera unit 40 and the grinding wheel 21 is changed in a predetermined pattern. Furthermore, the operation control of the camera 41 is performed based on the detection signal of the rotation phase sensor. As a result, a captured image 44 of the grinding surface 211 is acquired. In this embodiment, the grinding surface 211 is divided into a plurality of regions by dividing the grinding surface 211 into a lattice shape in the circumferential direction and the rotation axis direction of the grinding wheel 21. Then, the camera 41 captures an image of the grinding surface 211 for each of the regions. As a result, a plurality of captured images 44 are obtained. In this embodiment, an image including a large number of abrasive grains 212 is obtained as each captured image 44.

[0054] Thereafter, the electronic control unit 50 executes the processes in the following steps S12 to S17. First, it is determined whether it is the first period immediately after dressing of the grinding wheel 21 (step S12). If it is the first period (step S12: YES), the acquired multiple captured images 44 are stored in the storage unit 56 as a basic image 44B together with position information on the grinding surface 211 (step S13). In this case, the process is temporarily terminated without executing the processes of steps S14 to S17.

[0055] On the other hand, if it is the second time period that is later than the first time period (step S12: NO), the plurality of captured images 44 are stored in the storage unit 56 as the latest image 44N together with the position information on the grinding surface 211 (step S14). In this embodiment, the process of step S13 and the process of step S14 correspond to a storage step.

[0056] In this case, the multiple latest images 44N and the multiple basic images 44B are converted into grayscale separately (step S15). Thereafter, a process is executed in which the first learning device 61 outputs an extracted image (hereinafter, converted basic image) using the grayscale basic image 44B as input data (step S16). Also, a process is executed in which the first learning device 61 outputs an extracted image (hereinafter, converted latest image) using the grayscale latest image 44N as input data. In the process of step S16, in detail, a generation process is repeatedly executed for each of the multiple basic images 44B, such as extracting one of the multiple basic images 44B and causing the first learning device 61 to output a converted basic image using the basic image 44B as input data. Also, a generation process is repeatedly executed for each of the multiple latest images 44N, such as extracting one of the multiple latest images 44N and causing the first learning device 61 to output a converted latest image using the latest image 44N as input data.

[0057] Thereafter, a difference image (hereinafter, judgment image 65) showing the difference between two images (the converted basic image and the converted latest image) corresponding to the same location on the grinding surface 211 is generated (step S17). The judgment image 65 is generated for each imaging position on the grinding surface 211, i.e., for each of the aforementioned regions. Furthermore, as the judgment image 65, an image showing a portion where the tip portion of the grinding surface 211 has decreased in the converted latest image and an image showing a portion where the tip portion has increased are generated based on the converted basic image. The judgment image 65 generated in the process of step S17 is stored in the memory unit 56. Note that in this embodiment, the process of steps S16 and S17 correspond to the extraction image generation process.

[0058] <Judgment part> The judgment unit 58 judges the occurrence of deterioration abnormality in the grinding wheel 21 to be judged, using a plurality of judgment images 65 generated by the extracted image generating unit 57 and the second learning device 62 that has already been trained.

[0059] Here, when the grindstone 21 is used, it is desirable that the condition of the grindstone 211 of the grindstone 21 changes uniformly over the entirety of the grindstone 211. In a general grindstone having general abrasive grains, the condition of the tip of the grindstone 211 changes in such a way that the abrasive grains 212 fall off. As the grindstone 211 deteriorates, the abrasive grains 212 fall off unevenly in each part of the grindstone 211. For this reason, in a general grindstone, when the abrasive grains 212 fall off unevenly, it can be said that the condition of the grindstone 211 is changing in an undesirable way.

[0060] In this embodiment, the machined surface judgment device 30 is constructed on the assumption that the occurrence of deterioration abnormality in a general grinding wheel having general abrasive grains is judged. In this embodiment, when the distribution bias of the tip portion of the grinding surface 211 in the judgment image 65 is equal to or greater than a predetermined level, the occurrence of deterioration abnormality in the grinding wheel 21 to be judged is judged.

[0061] Hereinafter, the execution procedure of the determination process for determining deterioration abnormality of the grinding wheel 21 will be described with reference to Fig. 10. Fig. 10 is a flowchart showing the execution procedure of the determination process. Note that the series of processes shown in this flowchart conceptually show the execution procedure of the determination process, and the actual process is executed by the electronic control device 50 as a process at a predetermined cycle.

[0062] 10, in this process, first, classification (so-called classification) of each judgment image 65 is executed using the multiple judgment images 65 and the trained second learning device 62 (step S21). In detail, one of the multiple judgment images 65 is extracted, and the judgment image 65 is used as input data to classify the judgment image 65 by the second learning device 62. This classification process is executed repeatedly for each of the multiple judgment images 65.

[0063] Then, based on the classification result for each judgment image 65 and the position information on the grinding surface 211, the degree of distribution bias of the tip portion of the grinding surface 211 in the judgment image 65 is calculated (step S22). In the process of step S22, first, the proportion R1 of the portion classified as the evenly-shed abrasive surface (class 1) in the process of step S21 is calculated for each rotation phase of the grinding wheel 21. Here, when a predetermined number (N2) of judgment images 65 out of a plurality (N1) of judgment images 65 corresponding to the same rotation phase are classified as the evenly-shed abrasive surface (class 1), the value "N2 / N1" is calculated as the above-mentioned proportion R1. Then, as the degree of bias RR1, the difference ΔR1 (=R1max-R1min) between the maximum value R1max and the minimum value R1min of the above-mentioned proportion R1 calculated for each rotation phase is calculated.

[0064] Thereafter, it is determined whether the difference ΔR1 is equal to or greater than a predetermined value J1 (step S23). As the predetermined value J1, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 is determined in advance. In detail, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 (predetermined value J1) is obtained based on the results of various experiments and simulations performed by the inventors, and the predetermined value J1 is stored in advance in the storage unit 56.

[0065] Then, when the difference ΔR1 is less than a predetermined value J1 (step S23: NO), it is determined that the distribution bias of the tip portion of the grinding surface 211 in the determination image 65 is less than a predetermined level, and it is determined to be normal (step S24). On the other hand, when the difference ΔR1 is equal to or greater than the predetermined value J1 (step S23: YES), it is determined that the distribution bias of the tip portion of the grinding surface 211 in the determination image 65 is equal to or greater than a predetermined level, and it is determined that the grinding wheel 21 has deteriorated abnormally (step S25). In this case, it is determined that the grinding wheel 21 has deteriorated to such an extent that dressing of the grinding surface 211 is required. In this embodiment, the process of step S23 and the process of step S25 correspond to the determination process.

[0066] <Action and effect> According to this embodiment, the following advantageous effects can be obtained. (1) The machined surface judgment device 30 has a storage unit 56, an extracted image generating unit 57, and a judgment unit 58. The storage unit 56 stores a basic image 44B, which is an image of the grinding surface 211 captured at a first time immediately after the grinding wheel 21 is dressed, a latest image 44N, which is an image of the grinding surface 211 captured at a second time during the use of the grinding wheel 21, and a trained first learning device 61. The extracted image generating unit 57 uses the basic image 44B, the latest image 44N, and the trained first learning device 61 to generate a judgment image 65, which is an image obtained by extracting the tip portion of the grinding surface 211 from a difference image showing the difference between the basic image 44B and the latest image 44N. The judgment unit 58 judges the occurrence of a deterioration abnormality in the grinding wheel 21 when the distribution bias of the tip portion of the grinding surface 211 in the judgment image 65 is equal to or greater than a predetermined level.

[0067] As described above, in the case of the grinding wheel 21, which is a general grinding wheel, if there is a large bias in the shedding of the abrasive grains 212 at various parts of the grinding surface 211, it can be said that the condition of the grinding surface 211 has changed in an undesirable way.

[0068] In this embodiment, an image is generated as the judgment image 65 by extracting the tip portion of the grinding surface 211 from a difference image showing the difference between the basic image 44B and the latest image 44N. The judgment image 65 shows the amount of change in the state of the tip portion of the grinding surface 211, which is a portion that affects the finishing quality of the workpiece. Therefore, based on the position and size of the tip portion of the grinding surface 211 in the judgment image 65, the amount of change in the state of the tip portion of the grinding surface 211 in each part of the grinding surface 211 can be grasped. Therefore, according to this embodiment, it is understood that the deviation in the distribution of the tip portion of the grinding surface 211 in the judgment image 65 is large in the amount of change in the state of the tip portion of the grinding surface 211. In this embodiment, in such a case (step S23: YES in FIG. 10), it can be determined that the deterioration abnormality of the grinding wheel 21 has occurred.

[0069] Moreover, according to this embodiment, the judgment image 65 can be generated by using two captured images 44 (basic image 44B and latest image 44N), both of which contain a large number of abrasive grains 212, and the trained first learning device 61. Therefore, the amount of change in state in each part of the grinding surface 211 can be easily grasped in a short time compared to the case where the amount of change in state in each part of the grinding surface 211 is grasped by analyzing the state of the grinding surface 211 for each finely divided area containing an extremely small number of abrasive grains 212. Therefore, the occurrence of deterioration abnormality in the grinding wheel 21 can be judged in a short time.

[0070] Furthermore, according to this embodiment, by periodically executing the operation of attaching the camera unit 40, the process of generating the judgment image 65, and the judgment process, it is possible to perform grinding processing of the workpiece by the automatic grinding device 20 while checking the condition of the grinding surface 211 at an appropriate timing.

[0071] (2) The machined surface judgment device 30 is constructed on the assumption that it will judge the occurrence of deterioration abnormalities in a general grinding wheel having general abrasive grains. The extracted image generating unit 57 generates, as the judgment image 65, an image showing a portion where the tip portion of the grinding surface 211 has been reduced in the latest image 44N (more specifically, the latest image after conversion) based on the basic image 44B (more specifically, the basic image after conversion).

[0072] In this embodiment, the amount of abrasive grains that have fallen off at each part of the grinding surface 211 during the period from the first time period to the second time period appears in the determination image 65. Therefore, the amount of abrasive grains that have fallen off at each part of the grinding surface 211 can be understood from the position and size of the tip portion of the grinding surface 211 in the determination image 65. Therefore, according to this embodiment, it can be seen that the distribution of the tip portions of the grinding surface 211 in the determination image 65 is uneven, and therefore the uneven distribution of the abrasive grains 212 is large at the tip portion of the grinding surface 211. In this embodiment, in such a case (step S23 in FIG. 10: YES), it can be determined that deterioration abnormality has occurred in the grinding wheel 21.

[0073] (3) The processed surface judgment device 30 includes a camera 41 that captures an image of the grinding surface 211 from a direction perpendicular to the grinding surface 211, and an illumination unit 43 that illuminates an imaging target area AR on the grinding surface 211. The illumination unit 43 has a first illumination unit 45 and a second illumination unit 46. The first illumination unit 45 irradiates the imaging target area AR with first illumination light 45L having a first emission color from a direction perpendicular to the grinding surface 211. The second illumination unit 46 irradiates the imaging target area AR with second illumination light 46L having a second emission color different from the first emission color from a direction along the grinding surface 211.

[0074] According to this embodiment, the color of the top surface of the grinding wheel 21 in the captured image 44 captured by the camera 41 can be made different from the color of the other parts, thereby making it possible to clearly define the boundary between the top surface of the grinding wheel 21 and the other parts. This makes it easy to extract the tip part of the grinding surface 211 from the captured image 44, making it easy to generate the judgment image 65.

[0075] <Example of change> The above embodiment can be modified as follows: The above embodiment and the following modifications can be combined with each other to the extent that no technical contradiction occurs.

[0076] When generating the judgment image 65, the process of converting each of the latest images 44N and the base images 44B to grayscale (the process of step S15 in FIG. 9) can be omitted. In this case, the process of converting the first image and the second image to grayscale can be omitted when creating the learning data 63 of the first learning device 61. Also, the process of converting the first image and the second image to grayscale can be omitted when creating the learning data 64 of the second learning device 62.

[0077] When creating the learning data 63 for the first learning device 61, the procedure of dividing the first and second images into a plurality of divided images can be omitted. In this case, a large number of image sets, each pair consisting of a captured image 44 of the grinding surface 211 and an extracted image obtained by extracting the tip portion of the grinding surface 211 from the captured image 44, may be prepared, and these image sets may be used as the learning data 63. As in the machined surface judgment device 30 of the above embodiment, by dividing the first and second images into a plurality of divided images and using them to create the learning data 63, it becomes possible to create the learning data 63 using an electronic control device 50 with a relatively low processing capacity.

[0078] The manner in which the judgment process (see FIG. 10) is executed can be changed arbitrarily, so long as it is possible to judge that a deterioration abnormality has occurred in the grinding wheel 21 being judged when the distribution bias of the tip portion of the grinding surface 211 in the judgment image 65 is at or above a predetermined level.

[0079] The determination process can be executed, for example, as the following determination process A, determination process B, and determination process C. <Determination process A> The judgment process A can be executed as follows when the grindstone 21 to be judged is a general grindstone.

[0080] 11, in the judgment process A, first, a plurality of converted judgment images are generated by converting each of the plurality of judgment images 65 into a bitmap image (step S31). Note that in the process of step S31, an image showing a portion where the tip portion of the grinding surface 211 has been reduced in the latest post-conversion image based on the post-conversion basic image is used as the judgment image 65.

[0081] Thereafter, an integrated value PP2 is calculated by integrating the number of pixels (hereinafter, specific pixels) indicating the tip portion of the grinding surface 211 for each rotation phase of the grinding wheel 21 based on the multiple conversion determination images (step S32).

[0082] Then, based on the integrated value PP2 calculated for each rotation phase, a degree of bias in distribution of the tip portions of the grinding surface 211 in the conversion determination image (more specifically, a single image showing the entire grinding surface 211 made up of a plurality of conversion determination images) is calculated (step S33). In the process of step S33, the difference ΔPP2 (=PP2max-PP2ave) between the average value PP2ave and the maximum value PP2max of the integrated value PP2 calculated for each rotation phase is calculated as the degree of bias RR2.

[0083] Thereafter, it is determined whether the difference ΔPP2 is equal to or greater than a predetermined value J2 (step S34). As the predetermined value J2, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 is set in advance. In detail, a value (predetermined value J2) capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 is obtained based on the results of various experiments and simulations performed by the inventors, and the predetermined value J2 is stored in advance in the storage unit 56.

[0084] If the difference ΔPP2 is less than a predetermined value J2 (step S34: NO), it is determined that the distribution bias of the tip portion of the grinding surface 211 in the conversion assessment image is less than a predetermined level, and a normal determination is made (step S35). On the other hand, if the difference ΔPP2 is equal to or greater than the predetermined value J2 (step S34: YES), it is determined that the distribution bias of the tip portion of the grinding surface 211 in the conversion assessment image is equal to or greater than a predetermined level, and a deterioration abnormality has occurred in the grinding wheel 21 (step S36). In the above configuration, the processes of steps S34 and S36 correspond to the determination process.

[0085] In the above configuration, when the abrasive grains 212 fall off the grindstone 21, this fact appears in the display of the pixel (the specific pixel) in the conversion determination image that corresponds to the part where the abrasive grains 212 have fallen off. Therefore, it is possible to grasp the state in which the abrasive grains 212 have fallen off in each part of the grinding surface 211 based on the position and range of the specific pixel in the conversion determination image.

[0086] In the above configuration, as deterioration of the grindstone 21 progresses, the distribution of the portions (i.e., specific pixels) where the abrasive grains 212 have fallen off across the entire grinding surface 211 becomes more biased. In this case, the variation in the integrated value PP2 calculated for each rotation phase becomes larger, as shown in an example in Fig. 12. In Fig. 12, the solid line indicates the integrated value PP2, and the dashed line indicates the average value PP2ave of the integrated value PP2.

[0087] According to the above configuration, it can be seen that the difference ΔPP2 between the average value PP2ave and the maximum value PP2max of the integrated value PP2 is large, which indicates that the amount of the abrasive grains 212 that have fallen off is largely biased at the tip portion of the grinding surface 211. In the above configuration, in such a case (step S34 in FIG. 11: YES), it can be determined that the grindstone 21 has deteriorated abnormally.

[0088] <Determination process B> Judgment process B can be executed as follows when the grindstone 21 to be judged is a superabrasive grindstone having superabrasive grains. Judgment process B is constructed assuming that occurrence of deterioration abnormality in a superabrasive grindstone having superabrasive grains is judged.

[0089] 13, in the judgment process B, first, classification of each judgment image 65 is executed using the multiple judgment images 65 and the trained second learning device 62 (step S41). In detail, one of the multiple judgment images 65 is extracted, and the judgment image 65 is used as input data to classify the judgment image 65 by the second learning device 62. This classification process is executed repeatedly for each of the multiple judgment images 65.

[0090] Then, based on the classification result for each judgment image 65 and the position information on the grinding surface 211, the degree of distribution bias of the tip portion of the grinding surface 211 in the judgment image 65 is calculated (step S42). In the process of step S42, first, the ratio R3 of the portion classified as the chip-welded grinding surface (class 3) in the process of step S41 is calculated for each rotation phase of the grindstone 21. Here, when a predetermined number (N4) of judgment images 65 out of a plurality (N3) of judgment images 65 corresponding to the same rotation phase are classified as the chip-welded grinding surface (class 3), the value "N4 / N3" is calculated as the ratio R3. Then, the difference ΔR3 (=R3max-R3min) between the maximum value R3max and the minimum value R3min of the ratio R3 calculated for each rotation phase is calculated as the degree of bias.

[0091] Thereafter, it is determined whether the difference ΔR3 is equal to or greater than a predetermined value J3 (step S43). As the predetermined value J3, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 is set in advance. In detail, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 (predetermined value J3) is obtained based on the results of various experiments and simulations performed by the inventors, and the predetermined value J3 is stored in advance in the storage unit 56.

[0092] Then, when the difference ΔR3 is less than a predetermined value J3 (step S43: NO), it is determined that the distribution bias of the tip portion of the grinding surface 211 in the determination image 65 is less than a predetermined level, and it is determined to be normal (step S44). On the other hand, when the difference ΔR3 is equal to or greater than the predetermined value J3 (step S43: YES), it is determined that the distribution bias of the tip portion of the grinding surface 211 in the determination image 65 is equal to or greater than a predetermined level, and it is determined that the deterioration abnormality of the grinding wheel 21 has occurred (step S45). In this case, it is determined that the grinding wheel 21 has deteriorated to such an extent that dressing of the grinding surface 211 is required. In the above configuration, the process of step S43 and the process of step S45 correspond to the determination process.

[0093] In the case of an ultra-abrasive grinding wheel, the condition of the tip of the grinding surface 211 changes in such a way that foreign matter such as cutting powder adheres to the grinding surface 211. For this reason, when the degree of adhesion of foreign matter to each part of the grinding surface 211 is uneven, it can be said that the condition of the grinding surface 211 is changing in an undesirable way.

[0094] In the above configuration, the amount of foreign matter adhering to each part of the grinding surface 211 during the period from the first time to the second time appears in the judgment image 65 (more specifically, an image showing an increased portion of the tip part of the grinding surface 211 in the latest post-conversion image with the post-conversion basic image as a reference). Therefore, the amount of foreign matter adhering to each part of the grinding surface 211 can be grasped from the position and size of the tip part of the grinding surface 211 in the judgment image 65. Therefore, according to the above configuration, it is understood that the deviation in the distribution of the tip part of the grinding surface 211 in the judgment image 65 is large in the deviation of the adhesion of foreign matter at the tip part of the grinding surface 211. In this embodiment, in such a case (step S43: YES in FIG. 13), it can be determined that the deterioration abnormality of the grinding wheel 21 has occurred.

[0095] <Determination process C> Judgment process C can be executed as follows when the grindstone 21 to be judged is a superabrasive grindstone having superabrasive grains. Judgment process C is constructed assuming that occurrence of deterioration abnormality in a superabrasive grindstone having superabrasive grains is judged.

[0096] In the judgment process C, first, a plurality of converted judgment images are created by converting each of the judgment images 65 into a bitmap image. In this example, an image showing an increased portion of the tip of the grinding surface 211 in the latest image after conversion based on the converted base image is used as the judgment image 65.

[0097] Thereafter, an integrated value PP4 is calculated by integrating the number of pixels indicating the tip portion of the grinding surface 211 (hereinafter, specific pixels) for each rotation phase of the grindstone 21 based on the multiple conversion determination images. Then, based on the integrated value PP4 calculated for each rotation phase, a degree of bias in distribution of the tip portions of the grinding surface 211 in the conversion determination image (more specifically, a single image showing the entire grinding surface 211 made up of a plurality of conversion determination images) is calculated. In this process, the difference ΔPP4 (=PP4max-PP4ave) between the average value PP4ave and the maximum value PP4max of the integrated value PP4 calculated for each rotation phase is calculated as the degree of bias RR4.

[0098] Thereafter, it is determined whether the difference ΔPP4 is equal to or greater than a predetermined value J4. As the predetermined value J4, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 is set in advance. In detail, a value capable of properly determining the occurrence of deterioration abnormality in the grinding wheel 21 (predetermined value J4) is obtained based on the results of various experiments and simulations performed by the inventors, and the predetermined value J4 is stored in advance in the storage unit 56.

[0099] If the difference ΔPP4 is less than a predetermined value J4, it is determined that the distribution bias of the tip portions of the grinding surface 211 in the conversion assessment image is less than a predetermined level, and it is determined to be normal. On the other hand, if the difference ΔPP4 is equal to or greater than the predetermined value J4, it is determined that the distribution bias of the tip portions of the grinding surface 211 in the conversion assessment image is equal to or greater than a predetermined level, and it is determined that the grinding wheel 21 has deteriorated abnormally.

[0100] In the above configuration, if a foreign substance adheres to the grinding surface 211, this fact will appear in the display of a pixel (the specific pixel) in the conversion determination image that corresponds to the part where the foreign substance adheres. Therefore, the state of adhesion of the foreign substance to each part of the grinding surface 211 can be grasped based on the position and range of the specific pixel in the conversion determination image.

[0101] In the above configuration, as deterioration of the grindstone 21 progresses, the distribution of foreign matter adhering portions (i.e., specific pixels) becomes more uneven across the entire grinding surface 211. In this case, the variation in the integrated value PP4 calculated for each rotation phase becomes larger.

[0102] According to the above configuration, it is understood that the difference ΔPP4 between the average value PP4ave and the maximum value PP4max of the integrated value PP4 is large, and this indicates that the deviation in the amount of foreign matter adhesion is large at the tip portion of the grinding surface 211. In such a case, the above configuration makes it possible to determine that the grinding wheel 21 has deteriorated abnormally.

[0103] As a judgment process, a process of judging the state of the grinding surface 211 using a fractal analysis method can be executed. The procedure for generating the judgment image 65 can be as follows. First, a difference image showing the difference between the base image 44B and the latest image 44N is generated. Then, the difference image is converted to grayscale. Then, an extracted image is generated by extracting the tip portion of the grinding surface 211 from the difference image using the grayscale difference image and a trained learning device that has been trained to output the extracted image. This extracted image is used as the judgment image 65.

[0104] The following method can be used to create the learning data 64 for the second learning device 62. First, a difference image showing the difference between the first image and the second image is generated. Then, the difference image is converted to grayscale. Then, an extracted image is generated by extracting the tip portion of the grinding surface 211 from the grayscale difference image. This extracted image is used as the learning data 64 for the second learning device 62.

[0105] As the difference image constituting the judgment image 65, a difference image showing the difference between the converted basic image and the converted latest image can be used, or a difference image showing the difference between the converted latest image and an extracted image corresponding to the Nth previous image (for example, the image one time ago) can be used. In the above configuration, the timing when the grinding surface 211 was imaged N times ago corresponds to the first period, and the Nth previous image corresponds to the first captured image.

[0106] According to the above configuration, it is understood that there is a large bias in the amount of change in the state of the tip portion of the grinding surface 211 from the first period to the second period, because there is a bias in the distribution of the tip portion of the grinding surface 211 in the judgment image 65. In such a case, the above configuration makes it possible to judge that the grinding wheel 21 has deteriorated abnormally.

[0107] The structure of the lighting unit 43 can be changed as desired. For example, instead of irradiating a part of the second lamp 462 with the second illumination light 46L from a direction that forms "5 degrees" with respect to the grinding surface 211, the second lamp 462 may be irradiated with the second illumination light 46L from a direction that forms "10 degrees" with respect to the grinding surface 211. Alternatively, the second lamp 462 may be irradiated with the second illumination light 46L from a direction that forms "0 degrees" with respect to the grinding surface 211, that is, from a direction parallel to the tangent plane of the grinding surface 211. In order to clarify the boundary between the top surface of the grinding wheel 21 and the portion other than the top surface in the captured image 44, it is preferable that the angle between the optical axis of the part of the second lamp 462 and the tangent plane in the imaging target area AR of the grinding surface 211 is set to a predetermined angle range (0 degrees≦angle≦10 degrees).

[0108] The second irradiating section 46 may be configured solely by the second lamp 462 that irradiates the imaging target area AR of the grinding surface 211 with the second illumination light 46L from a direction along the grinding surface 211. The second irradiating section 46 can be configured solely by a second lamp 462 that irradiates the imaging target area AR of the grinding surface 211 with the second illumination light 46L from a direction parallel to the tangent plane of the grinding surface 211.

[0109] One of the first irradiating section 45 and the second irradiating section 46 may be omitted. The first emission color of the first illumination light 45L in the first irradiating unit 45 and the second emission color of the second illumination light 46L in the second irradiating unit 46 can be changed arbitrarily. In order to clarify the boundary between the top surface of the grinding wheel 21 and a portion other than the top surface in the captured image 44, it is preferable to determine colors different from the first emission color and the second emission color.

[0110] The camera unit 40 may be disposed to the side of the grindstone 21 or above the grindstone 21. In this case, the camera 41 may be disposed so as to capture an image of the grindstone 211 from a direction perpendicular to the grindstone 211. The illumination unit 43 may be disposed so as to illuminate the side or upper end of the grindstone 211, i.e., the area imaged by the camera 41.

[0111] The camera unit 40 may be provided integrally with the automatic grinding device 20 in a non-detachable manner. According to this configuration, the operation of capturing an image of the grinding surface 211 and the operation of storing the captured image 44 in the electronic control device 50 can be easily performed with the grinding wheel 21 attached to the automatic grinding device 20 without attaching or detaching the camera unit 40 to the automatic grinding device 20. In addition, it is possible to execute a series of combined automatic controls, including control for determining the state of the grinding surface 211, control for grinding the workpiece by the automatic grinding device 20, and control for dressing the grinding wheel 21 by the automatic grinding device 20. Therefore, by periodically executing the process for generating the determination image 65 and the determination process, the grinding of the workpiece by the automatic grinding device 20 and dressing of the grinding wheel 21 can be executed while checking the state of the grinding surface 211 at an appropriate timing.

[0112] When the above-mentioned configuration is adopted, it is preferable to provide a shutter section that opens and closes the space between the camera unit 40 and the grindstone 21. According to this configuration, when performing grinding processing by the automatic grinding device 20, the shutter section can be closed to create a closed state in which the space between the camera unit 40 and the grindstone 21 is partitioned. This makes it possible to prevent foreign matter (such as cutting chips and oil) from adhering to the camera unit 40. On the other hand, when capturing an image of the grinding surface 211 by the camera unit 40, the shutter section can be opened to create an open state in which the space between the camera unit 40 and the grindstone 21 is not partitioned. This makes it possible to capture an image of the grinding surface 211 by the camera unit 40.

[0113] The task of storing the learned learning devices 61, 62 in the electronic control device 50 can be performed in any manner, such as the following (task A) to (task C). (Task A) Using the automatic grinding device 20 on which the grinding wheel 21 to be evaluated is provided, learning data 63, 64 are acquired, and the learning devices 61, 62 are trained by machine learning using the learning data 63, 64.

[0114] (Work B) The learning data 63, 64 corresponding to the grinding wheel 21 that is actually used is provided by the manufacturer of the automatic grinding device 20 or the manufacturer of the grinding wheel 21. The learning data 63, 64 is then stored in the electronic control device 50 of the machined surface determination device 30 used by the user, thereby allowing the learners 61, 62 to perform machine learning.

[0115] (Operation C) When manufacturing the machined surface determining device 30, the learned learning devices 61, 62 are stored in the electronic control device 50. The first learning device 61 may be a type of generative adversarial network other than "pix2pix", such as "cycleGAN" or "CGAN". Also, the first learning device 61 may be an image generation model other than a generative adversarial network (GAN), such as a variational autoencoder (VAE). In short, any image generation model that can generate an extracted image, which is an image in which only the tip portion of the grinding surface 211 of the grinding wheel 21 is extracted from an image related to the grinding surface 211, may be used as the first learning device 61.

[0116] An object detection model called YOLO (You Only Look Once) can be used as the second learning device 62. In addition, a general forward propagation type neural network having a multi-layer structure, a support vector machine, or the like can be used as the second learning device 62. In short, any object detection model that can determine the state of the grinding surface 211 can be used as the second learning device 62.

[0117] The machined surface assessment device according to the above embodiment can also be applied to a grindstone having a structure in which a large number of abrasive grains are fixed to the bottom surface of a cylindrical base material by a bonding agent. The machined surface determination device, determination program, and machined surface determination method according to the above-described embodiment can be applied to a grinding wheel for grinding, as well as to a saw wire, which is a tool for a wire saw, a wire electrode, which is a tool for wire electric discharge machining, etc. When the machined surface determination device, determination program, and machined surface determination method according to the above-described embodiment are applied to the wire electrode, the surface of the wire electrode corresponds to the "machined surface", and the part of the surface that is not cracked or chipped corresponds to the "tip portion of the machined surface".

[0118] <Additional Notes> The above embodiment includes the configurations described in the following supplementary notes. [Appendix 1] A machined surface judgment device for judging the condition of a machined surface of a tool, comprising: a memory unit that stores a trained learning device that has been trained using as learning data a first captured image, which is an image of the machined surface of the tool to be judged at a first time, a second captured image, which is an image of the machined surface of the tool to be judged at a second time later than the first time, and a tip image, which is an image showing only the tip portion of the machined surface of the tool; an extracted image generation unit that uses the first captured image, the second captured image, and the trained learning device to generate an extracted image, which is an image obtained by extracting the tip portion of the machined surface from a difference image showing the difference between the first captured image and the second captured image; and a judgment unit that judges the occurrence of a deterioration abnormality in the tool to be judged when a bias in the distribution of the tip portion of the machined surface in the extracted image is equal to or greater than a predetermined level.

[0119] [Appendix 2] The machined surface judgment device according to [Appendix 1], wherein the tool is a grinding wheel for grinding. [Appendix 3] The grinding wheel is a general grinding wheel having general abrasive grains, and the extracted image generation unit generates, as the extracted image, an image showing a portion in which the tip portion of the processed surface is reduced in the second captured image based on the first captured image.The processed surface judgment device described in [Appendix 2]

[0120] [Appendix 4] The grinding wheel is a superabrasive grinding wheel having superabrasive grains, and the extracted image generation unit generates, as the extracted image, an image showing an increased portion of the tip portion of the processed surface in the second captured image based on the first captured image.The processed surface judgment device described in [Appendix 2]

[0121] [Appendix 5] The machined surface judgment device comprises an imaging unit that images the machined surface from a direction perpendicular to the machined surface, and an illumination unit that illuminates an area on the machined surface to be imaged by the imaging unit, wherein the illumination unit has a first irradiation unit that irradiates the imaged area with a first illumination light having a first emission color from a direction perpendicular to the machined surface, and a second irradiation unit that irradiates the imaged area with a second illumination light having a second emission color different from the first emission color from a direction along the machined surface. The machined surface judgment device described in any one of [Appendix 1] to [Appendix 4].

[0122] [Appendix 6] [Appendix 1] to [Appendix 5]. A judgment program that causes an electronic control device provided in the machined surface judgment device to execute the processing of the memory unit, the extracted image generation unit, and the judgment unit provided in the machined surface judgment device.

[0123] [Appendix 7] A machined surface judgment method for judging the state of a machined surface of a tool, comprising: a storage step of storing in a memory unit of a machined surface judgment device a first captured image which is an image of the machined surface of the tool to be judged at a first time, a second captured image which is an image of the machined surface of the tool to be judged at a second time later than the first time, and a trained learning device which has learned using as learning data a tip image which is an image showing only the tip portion of the machined surface of the tool; an extraction image generation step of generating an extraction image which is an image obtained by extracting the tip portion of the machined surface from a difference image which shows the difference between the first captured image and the second captured image, using the first captured image, the second captured image, and the trained learning device; and a judgment step of judging the occurrence of a deterioration abnormality in the tool to be judged when the uneven distribution of the tip portion of the machined surface in the extraction image is equal to or greater than a predetermined level. [Explanation of symbols]

[0124] 20...Automatic grinding device 21...Whetstone 211...Abrasive surface 212…Abrasive grain 22...Whetstone support part 23… Foundation 24…Table 25...Operation control unit 26…X-axis drive unit 27…Y-axis drive unit 28…Z-axis drive unit 29…Grinding wheel drive unit 30…Machine surface determination device 40…Camera unit 41...Camera 43…Lighting department 44…Image 44B...Basic image 44N...Latest images 45...First irradiation section 451…First ramp 452...Reflective translucent member 45L…First lighting 46…Second irradiation section 461…Lighting dome 462…2nd Ramp 46L…Second illumination light 50...Electronic control device 51…CPU 52...ROM 53…RAM 54...Input section 55...Display section 56...Storage section 57...Extracted image generating unit 58…Judgment section 60...Executing program 61…First learning unit 62…Second learning unit 63…Learning data 63B…Image before conversion 63A…Converted image 64…Learning data 65...Judgment image

Claims

1. A machined surface judgment device for judging a state of a machined surface of a tool, comprising: a memory unit that stores a learned learning device that has been trained using as learning data a first captured image, which is an image of the machining surface of the tool to be determined at a first time, a second captured image, which is an image of the machining surface of the tool to be determined at a second time after the first time, and a tip image, which is an image showing only the tip portion of the machining surface of the tool; an extraction image generating unit that generates an extraction image, which is an image obtained by extracting a tip portion of the processing surface from a difference image showing a difference between the first captured image and the second captured image, by using the first captured image, the second captured image, and the trained learning device; a determination unit that determines the occurrence of a deterioration abnormality in the tool to be determined when a distribution bias of the tip portion of the machined surface in the extracted image is equal to or greater than a predetermined level; A machined surface determination device comprising:

2. The tool is a grinding wheel. The machined surface determination device according to claim 1 .

3. The grindstone is a general grindstone having general abrasive grains, The extraction image generating unit generates, as the extraction image, an image showing a portion in which a tip portion of the processing surface is reduced in the second captured image based on the first captured image. The machined surface determination device according to claim 2 .

4. The grindstone is a superabrasive grindstone having superabrasive grains, The extraction image generating unit generates, as the extraction image, an image showing an increased portion of the tip portion of the processing surface in the second captured image based on the first captured image. The machined surface determination device according to claim 2 .

5. The machining surface judgment device includes an imaging unit that images the machining surface from a direction perpendicular to the machining surface, and an illumination unit that illuminates an imaging target area on the machining surface by the imaging unit, The illumination unit includes a first irradiation unit that irradiates the imaging target area with a first illumination light having a first emission color from a direction perpendicular to the processing surface, and a second irradiation unit that irradiates the imaging target area with a second illumination light having a second emission color different from the first emission color from a direction along the processing surface. The machined surface determining device according to any one of claims 1 to 4.

6. A judgment program that causes an electronic control device provided in the machined surface judgment device to execute the processing of the memory unit, the extracted image generation unit, and the judgment unit provided in the machined surface judgment device according to any one of claims 1 to 4.

7. A machined surface determination method for determining a state of a machined surface of a tool, comprising: a storage process for storing in a storage unit of the machining surface judgment device a first captured image which is an image of the machining surface of the tool to be judged at a first time, a second captured image which is an image of the machining surface of the tool to be judged at a second time after the first time, and a trained learning device which has learned using a tip image which is an image showing only a tip portion of the machining surface of the tool as learning data; an extraction image generating step of generating an extraction image, which is an image obtained by extracting a tip portion of the processing surface from a difference image showing a difference between the first captured image and the second captured image, by using the first captured image, the second captured image, and the trained learning device; a determination step of determining occurrence of deterioration abnormality in the tool to be determined when the distribution bias of the tip portion of the machined surface in the extracted image is equal to or greater than a predetermined level; A machining surface determination method comprising:

Images