Surface condition estimation method and surface condition estimation system

Abstract

To provide an area setting method configured to prevent displacement of an area to be set in an image.SOLUTION: An area setting method for setting an area in a captured image includes: determining a first center position Q13 in a first direction on the basis of changes of luminance of pixels that form an image in the first direction of an image Z captured by a camera 13; determining a second center position Q23 in a second direction on the basis of changes of luminance of pixels in the second direction which is different from the first direction; determining a center RC of an area on the basis of the first center position Q13 and the second center position Q23; and setting the area on the basis of the center RC.SELECTED DRAWING: Figure 14

Description

[Technical field]

[0001] The present invention Surface The present invention relates to a state estimation method and a surface state estimation system. [Background technology]

[0002] In general, when a workpiece is machined using a tool of a machine tool, the tool may deteriorate, resulting in a decrease in machining accuracy. Therefore, it is extremely important to maintain the machining accuracy of the workpiece by managing the deterioration of the tool and appropriately correcting the tool. For this reason, Patent Document 1 and Patent Document 2 have conventionally disclosed techniques for acquiring an image of an evaluation area of ​​a workpiece and estimating the surface roughness of the workpiece machined by the tool. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2006-162558 A [Patent Document 2] JP 2018-40599 A Summary of the Invention [Problem to be solved by the invention]

[0004] By the way, when the tool is corrected, the machining accuracy of the workpiece can be always maintained at a good level by performing the correction frequently. However, when the tool is corrected, it takes time to correct the tool and costs to correct the tool. Therefore, it is extremely effective to appropriately set the timing to correct the tool, since it is possible to shorten the time required for the correction and reduce the cost required for the correction. According to the above-mentioned conventional technology, it is possible to set the timing to correct the tool based on the estimated surface roughness. However, when an image is used, for example, a position shift may occur in the evaluation area every time the workpiece is changed, and as a result, it may be difficult to accurately estimate the surface condition of the workpiece.

[0005] The present invention can suppress positional deviation of an area set in an image. Can be done, craft An object of the present invention is to provide a surface condition estimation method and a surface condition estimation system that can estimate the surface condition of an object with high accuracy. [Means for solving the problem]

[0008] (Surface condition estimation method) According to one aspect of the present invention, a region is set for a captured image. A machine tool that uses a region setting method to machine a workpiece with a tool The above Tools and The above Method for estimating surface condition of machined surface of workpiece And, The region setting method includes: determining a first central position in a first direction of the image based on a variation in luminance of a plurality of pixels forming the image in the first direction of the image; determining a second center position in a second direction different from the first direction based on the change in luminance of the pixel in the second direction; determining a center of the region based on the first center location and the second center location; Setting the region based on the center; The surface state estimation method includes: The above The entire processed surface was imaged. The above For images The above Set the evaluation area according to the area setting method, The above Multiple elements forming the evaluation area The above Adjacent pixels The above By multiple areas formed from pixels The above Divide the evaluation area, Each The above Multiple adjacent areas The above Each pixel The above Calculating the average luminance value using the luminance; Each The above Multiple adjacent areas The above Each pixel The above Brightness and The above A luminance difference value representing a difference from an average luminance value is calculated; The above The square root of the sum of the squares of the brightness difference values ​​is calculated as an index value. The above Using the index value, The above Correlated with index value The above Estimating the surface condition of the machined surface , a surface condition estimation method.

[0009] (Surface condition estimation system) Another aspect of the present invention is to set a region for a captured image. area setting device Including, A machine tool that processes workpieces using tools. The above Tools and The above Surface condition estimation system for estimating the surface condition of a machined surface of a workpiece And, The area setting device includes: a first center position determiner for determining a first center position in a first direction of the image based on a variation in luminance of a plurality of pixels forming the image in the first direction; a second center position determiner that determines a second center position in a second direction different from the first direction based on a change in the luminance of the pixel in the second direction; a region center determination unit that determines a center of the region based on the first center position and the second center position; a region setting unit that sets the region based on the center, The surface state estimation system includes: The above The processed surface was imaged. The above an imaging device that outputs an image; The above Output from the imaging device The above The entire processed surface was imaged. The above For images The above The evaluation area set by the area setting device is divided into several adjacent The aboveAn area dividing unit that divides the image into a plurality of areas formed from pixels, The above For an area, multiple adjacent The above Each pixel The above a luminance average value calculation unit that calculates a luminance average value using the luminance; The above About the area The above The average brightness and adjacent The above Each pixel The above a luminance difference value calculation unit that calculates a luminance difference value that represents a difference between the luminance and the image; and The above The brightness difference value calculated by the brightness difference value calculation unit The above an index value calculation unit that calculates a square root of the sum of squares of luminance difference values ​​as an index value; The above Calculated by image evaluation device The above Using the index value, The above Correlated with index value The above An estimation calculation device having an estimation unit that estimates the surface condition of a processed surface. Also, it is a surface condition estimation system.

[0010] These features make it possible to suppress misalignment of the evaluation area, and to accurately estimate the surface condition of the machined surface of the workpiece using an index value attributable to the brightness of the machined surface of the workpiece. The surface condition estimation system can also accurately estimate the condition of the tool of the machine tool that machines the workpiece using the estimated surface condition of the machined surface of the workpiece. This allows the machine tool to maintain good machining accuracy and correct the tool at an appropriate time.

[0011] This allows the tool of the machine tool to be corrected at an appropriate time, thus preventing excessive correction of the tool of the machine tool and delaying the correction, thereby shortening the correction time required to correct the tool and reducing the cost of correcting the tool. [Brief description of the drawings]

[0012] [Figure 1]FIG. 1 is a diagram showing a configuration of a surface state estimation system. [Diagram 2] 2 is a diagram showing a configuration of a grinding device which is an example of the machine tool in FIG. 1. [Diagram 3] Fig. 3 is a diagram showing the configuration of a camera which is an example of the image acquisition device in Fig. 2. Fig. 4 is a front view showing a rack bar which is an example of an article (product). [Figure 4] 2 is a diagram showing a functional block configuration of the image evaluation device in FIG. 1. [Diagram 5] 5 is a diagram for explaining an entire image captured by the camera of FIG. 3 and an evaluation area set by the image evaluation device of FIG. 4. [Figure 6] 5 is a diagram for explaining an area set by the image evaluation device of FIG. 4. [Figure 7] FIG. 13 is a diagram for explaining the numbers of pixels that make up an area. [Figure 8] FIG. 13 is a diagram for explaining a change in luminance in the circumferential direction of a workpiece. [Figure 9] FIG. 11 is a diagram for explaining a luminance difference value. [Figure 10] 13 is a diagram for explaining acquisition of luminance in a first direction in setting an evaluation area. FIG. [Figure 11] 13 is a diagram for explaining how to determine the center position of luminance in a first direction in setting an evaluation area. FIG. [Figure 12] 13 is a diagram for explaining acquisition of luminance in a second direction in setting an evaluation area. FIG. [Figure 13] 13 is a diagram for explaining how to determine the center position of luminance in the second direction in setting the evaluation area. FIG. [Figure 14] 13 is a diagram for explaining how to determine the center position of an evaluation area when setting the evaluation area. FIG. [Figure 15] 13 is a diagram for explaining the setting of an evaluation area when the center position is shifted in setting the evaluation area. FIG. [Figure 16] 13 is a diagram for explaining the setting of an evaluation area when the center position is shifted in setting the evaluation area. FIG. [Figure 17]FIG. 13 is a diagram for explaining setting of an evaluation area. [Figure 18] FIG. 13 is a diagram for explaining setting of an evaluation area. [Figure 19] FIG. 2 is a diagram showing a functional block configuration of the surface state estimation system. [Figure 20] FIG. 13 is a diagram for explaining the correlation between an index value and surface roughness. [Figure 21] 13A and 13B are diagrams illustrating the influence of average luminance on a linear approximation equation according to the first modified example. [Figure 22] 13A and 13B are diagrams illustrating changes in the slope and intercept of a linear approximation equation with respect to changes in average luminance according to a first modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (1. Machine tools to which the surface condition estimation system can be applied) The surface condition estimation system estimates the surface condition of the machined surface of a workpiece machined by a machine tool. Machine tools that machine the workpiece include various cutting machines that perform cutting (e.g., gear machining machines, machining centers, etc.) and various grinding machines that perform grinding (e.g., cylindrical grinders, cam grinders, surface grinders, etc.).

[0014] In this example, the machine tool is exemplified by a grinding device that grinds a workpiece through grinding processes such as rough grinding, fine grinding, fine grinding, spark out, etc. Examples of the workpiece ground by the grinding device include a simple shaft-shaped member, a crankshaft, a camshaft, a flat plate, etc. Note that, as an example of the workpiece in this example, a simple shaft-shaped member with a circular cross section is ground by a cylindrical grinding machine, as described later.

[0015] (2. Overview of the configuration of surface condition estimation system 1) Next, an overview of the configuration of the surface condition estimating system 1 will be described with reference to Fig. 1. The surface condition estimating system 1 includes at least one grinding machine 10, an image evaluation device 20, and at least one calculation device (30, 40). The surface condition estimating system 1 may target one grinding machine 10, or may target multiple grinding machines as shown in Fig. 1.

[0016] The surface condition estimating system 1 of this embodiment is illustrated as having a plurality of grinding machines 10. The surface condition estimating system 1 of this embodiment is illustrated as having an image evaluation device 20 provided in each of the grinding machines 10, one calculation device 30, and a plurality of calculation devices 40 provided in each of the grinding machines 10.

[0017] The grinding device 10 grinds the workpiece W through grinding processes such as rough grinding, fine grinding, fine grinding, spark out, etc. The grinding device 10 is provided with at least a camera 13 as an imaging device that images a grinding surface W1 that can be observed during grinding of the workpiece W.

[0018] The camera 13 captures an image of the ground surface W1 of the workpiece W that is ground by the grinding device 10. The camera 13 outputs overall image data representing an overall image of the entire ground surface W1 of the workpiece W for each grinding process. In this example, the camera 13 is used as an image acquisition device to capture an image of the ground surface W1 of the workpiece W, for example, the outer peripheral surface. However, the image acquisition device is not limited to capturing an image of the ground surface W1 of the workpiece W to obtain an overall image, as with the camera 13, and it is also possible to use, for example, a 3D scanner device capable of capturing an overall image of the unevenness of the ground surface W1.

[0019] The image evaluation device 20 acquires the entire image (entire image data) output from the camera 13. Then, the image evaluation device 20 outputs an index value representing the brightness that changes in response to the surface condition of an evaluation area in the entire image (entire image data) for estimating the surface condition.

[0020] The image evaluation device 20 of this example calculates an area index value for each of a plurality of areas including adjacent pixels for an imaged evaluation area, and calculates an index value for the evaluation area by taking the square root of the sum of the squares of all the area index values. Here, the image evaluation device 20 of this example calculates, as the area index value, the difference between the luminance of each pixel constituting the area (for example, luminance expressed in 256 levels from 0 to 255) and the average luminance value in the area.

[0021] In this example, the computing devices (30, 40) apply machine learning using the index value output from the image evaluation device 20 to estimate surface roughness as a surface condition of the ground surface W1 of the workpiece W, which has a correlation with the index value. Note that the correlation obtained by applying machine learning represents the correlation between the index value obtained from the image evaluation device 20 and the surface roughness (actual roughness value) of the ground surface W1 of the workpiece W actually measured by the roughness meter 2.

[0022] 1, in this example, the calculation device (30, 40) is composed of a learning processing device 30 and an estimation calculation device 40. Although the learning processing device 30 and the estimation calculation device 40 are shown as independent configurations, they can also be one device. Also, some or all of the calculation devices (30, 40) can be incorporated into a production line composed of multiple grinding machines 10.

[0023] In this example, the learning processing device 30 has a so-called server function, and is communicatively connected to a plurality of estimation calculation devices 40 provided in each of the grinding machines 10. Moreover, the estimation calculation device 40 is provided in a one-to-one correspondence with each grinding machine 10, functions as a so-called edge computer, and enables high-speed calculation processing.

[0024] (3. Details of the configuration of surface condition estimation system 1) The configuration of the surface condition estimating system 1 will be described in more detail with reference to Fig. 1. The surface condition estimating system 1 includes a plurality of grinding machines 10 (two in this example), an image evaluation device 20 provided in each of the grinding machines 10, one learning processing device 30 functioning as a part of the calculation device, and an estimation calculation device 40 provided in each of the grinding machines 10 and functioning as another part of the calculation device.

[0025] The grinding apparatus 10 mainly comprises a grinding machine 11 that uses a grinding wheel T to grind a grinding surface W1 of a workpiece W, a control device 12 that controls the grinding machine 11, a camera 13 as an image acquisition device, and an interface 14.

[0026] In this embodiment, as shown in Fig. 2, a wheelhead traverse type cylindrical grinding machine 50 is taken as an example of the grinding machine 11. Note that the grinding machine 11 may also be of a table traverse type.

[0027] The cylindrical grinding machine 50 is a machine tool for grinding the outer peripheral surface, i.e., the grinding surface W1, of a workpiece W, which is a shaft-shaped member. As shown in Fig. 2, the cylindrical grinding machine 50 mainly includes a bed 51, a headstock 52, a tailstock 53, a traverse base 54, a grinding wheel head 55, a grinding wheel 56 (grinding wheel T), a sizing device 57, and a grinding wheel dressing device 58.

[0028] The bed 51 is fixed on an installation surface. The headstock 52 is provided on the upper surface of the bed 51, at the front side in the X-axis direction (the lower side in FIG. 2) and at one end side in the Z-axis direction (the left side in FIG. 2). The headstock 52 supports the workpiece W rotatably about the Z-axis. The workpiece W is rotated by the drive of a motor 52a provided on the headstock 52.

[0029] The tailstock 53 is provided on the upper surface of the bed 51 at a position facing the headstock 52 in the Z-axis direction, i.e., on the near side in the X-axis direction (the lower side in FIG. 2) and on the other end side in the Z-axis direction (the right side in FIG. 2). As a result, both ends of the workpiece W are rotatably supported by the headstock 52 and the tailstock 53.

[0030] The traverse base 54 is provided on the upper surface of the bed 51 so as to be movable in the Z-axis direction. The traverse base 54 is moved by the drive of a motor 54a provided on the bed 51. The grinding wheel head 55 is provided on the upper surface of the traverse base 54 so as to be movable in the X-axis direction. The grinding wheel head 55 is moved by the drive of a motor 55a provided on the traverse base 54. The grinding wheel 56 (tool T) is rotatably supported by the grinding wheel head 55. The grinding wheel 56 is rotated by the drive of a motor 56a provided on the grinding wheel head 55. The grinding wheel 56 is configured by a plurality of abrasive grains fixed by a bond material.

[0031] The sizing device 57 measures the dimension (diameter) of the workpiece W. The sizing device 57 is provided on the upper surface of the bed 51 so as to be movable in the Z-axis direction. The position of the sizing device 57 in the Z-axis direction is controlled by a feed mechanism 57a provided on the bed 51.

[0032] The grinding wheel dressing device 58 dresses the shape of the grinding wheel 56. In other words, the grinding wheel dressing device 58 is a device for truing and dressing the grinding wheel 56. Truing is a reshaping operation, which is an operation for shaping the grinding wheel 56 to match the shape of the workpiece W when the grinding wheel 56 is deteriorated by the grinding process, for example, worn or abrasive, the abrasive grains fall off, or the abrasive grains are broken, and is an operation for removing the runout of the grinding wheel 56 due to uneven wear. Dressing is an operation for dressing (sharpening) the grinding wheel 56, which is an operation for adjusting the protruding amount of the abrasive grains, and an operation for creating the cutting edge of the abrasive grains. In other words, dressing is an operation for correcting dulling, clogging, missing grains, etc., and is usually performed after truing.

[0033] The control device 12 includes a CNC device, a PLC device, etc., and is provided in the cylindrical grinding machine 50 (grinding machine 11) in this example, as shown in Fig. 2. The control device 12 controls the motors 52a, 54a, 55a, 56a, etc. in the cylindrical grinding machine 50 in accordance with an NC program generated based on operation command data such as the shape of the workpiece W, processing conditions, the shape of the grinding wheel 56 (tool T), and coolant supply timing information. That is, the control device 12 generates an NC program based on the operation command data by inputting the operation command data.

[0034] As a result, the control device 12 controls the operation of the cylindrical grinding machine 50 (grinding machine 11) to grind the ground surface W1 of the workpiece W. Here, the control device 12 performs grinding until the ground surface W1 of the workpiece W has a finished shape based on the dimension (diameter) of the workpiece W measured by the sizing device 57. For this reason, the NC program generated by the control device 12 is generated according to the grinding content, i.e., rough grinding, fine grinding, fine grinding, spark out, etc.

[0035] The control device 12 is provided on the bed 51 and is capable of communicating with the estimation calculation device 40 via the interface 14 (see FIG. 1). As a result, the control device 12 of this embodiment controls the motors 52a, 54a, 55a, 56a and the grinding wheel correction device 58, etc., at the timing of acquiring a correction signal R output from the estimation calculation device 40 as the timing for correcting the grinding wheel 56, thereby correcting (truing and / or dressing) the grinding wheel 56. Here, the estimation calculation device 40 of this embodiment outputs the correction signal R to the control device 12 based on the estimated surface condition, i.e., surface roughness, of the ground surface W1, as described later.

[0036] The camera 13 is provided on the traverse base 54 as shown in Fig. 2, and captures an image of the ground surface W1 of the workpiece W with light for imaging irradiated onto the ground surface W1. For this purpose, the camera 13 is provided with, for example, an illumination device 13a arranged concentrically with the lens (not shown) of the camera 13 and irradiating annular light for imaging as shown in Fig. 3. The camera 13 outputs overall image data Z representing an overall image of the ground surface W1 reflecting the light for imaging to the image evaluation device 20 via, for example, the interface 14 of the control device 12 (see Fig. 1).

[0037] As shown in FIG. 4, the image evaluation device 20 includes an evaluation region setting unit 21, an area dividing unit 22, a brightness acquiring unit 23, and an index value calculating unit 24. As shown in FIG. 5, the image evaluation device 20 acquires overall image data Z representing an overall image of the ground surface W1 captured by the camera 13. Here, as described above, the camera 13 irradiates annular imaging light. Therefore, in the overall image data Z capturing the ground surface W1 of the workpiece W, there are a first portion (shown in white in FIG. 5) that strongly reflects light, and a second portion (shown in matte in FIG. 5) that weakly reflects light. In this example, a case is illustrated in which there are two first portions that strongly reflect light in the overall image data Z.

[0038] The evaluation area setting unit 21 acquires the overall image data Z output from the camera 13, and sets an evaluation area HR for evaluating the surface condition, i.e., surface roughness, of the ground surface W1 represented by the overall image data Z. In this example, as shown by the thick solid line in Fig. 5, the evaluation area setting unit 21 sets the evaluation area HR so as to include two first portions.

[0039] Meanwhile, the grinding device 10 performs grinding on the ground surfaces W1 of a plurality of workpieces W that are sequentially supplied. In this case, the state of the ground surfaces W1 of each workpiece W changes due to, for example, a processing position error or a processing time difference between the first half or second half of a grinding lot. In this case, if the evaluation area HR is set based on the same preset position, deviations will occur in the evaluation areas HR set for each workpiece W, and as a result, there is a possibility that the estimation accuracy of the surface roughness SS will decrease.

[0040] Therefore, the image evaluation device 20 is configured so that the evaluation area setting unit 21 can set the evaluation area HR without causing any deviation between different workpieces W. Specifically, as shown in Fig. 4, the evaluation area setting unit 21 includes a first center position determining unit 211, a second center position determining unit 212, an area center determining unit 213, and an area setting unit 214, which will be described in detail later.

[0041] As shown in FIG. 6, the area dividing unit 22 sets four adjacent pixels (shown as small squares in FIG. 6) constituting the evaluation area HR as area A, and divides the entire evaluation area HR into n areas A. Here, in this example, the area A has four pixels, but the number of pixels constituting the area A is not limited to this. Also, in this example, as shown in FIG. 6, the evaluation area HR may be divided into n areas A. N ×2 M Pixels are used as an example.

[0042] In this case, each pixel is assigned a number to identify it. For example, as shown in FIG. 6, the pixel in the upper left corner of the evaluation area HR is assigned the number "0", the pixel adjacent to the right of pixel "0" is assigned the number "1", and the last pixel in the first column of the evaluation area HR is assigned the number "2". N Similarly, the pixel immediately below and adjacent to pixel "0" in the evaluation area HR is assigned the value "2 N " is assigned, and pixel "2 N The pixel to the right of N+1" is assigned to the last pixel in the second column of the evaluation area HR, and "2 × 2 N Similarly, pixel "2 -1" in the evaluation area HR is assigned N The pixel immediately below and adjacent to N " is assigned to the first pixel of the Mth column of the evaluation area HR, and "(2 M -1)×2 N " and the last pixel in the Mth column is assigned "2 (M+N) -1" is assigned.

[0043] As a result, the numbers assigned to the four pixels constituting the m-th area Am shown in FIG. 6 are as follows: the first pixel P1 located in the upper left corner of the area Am is “2i+2j×2 N ", and the second pixel P2 adjacent to the right of the first pixel P1 is "2i + 2j × 2 N In the area Am, the third pixel P3 immediately below the first pixel P1 is assigned the value "2i+2 N +2j×2 N ", and the fourth pixel P4 adjacent to the right of the third pixel P3 (i.e., adjacent directly below the second pixel P2) is "2i+2 N +2j×2 N +1 is assigned to each i, where i ranges from 0 to 2 N-1 -1, and "j" ranges from "0" to "2 M-1 It is a value that can range from "-1".

[0044] Then, the area dividing unit 22 outputs brightness data SP(n) including the respective brightness φ of the four pixels, a first pixel P1, a second pixel P2, a third pixel P3, and a fourth pixel P4, constituting each of the n divided areas A, that is, the brightness φ1 of the first pixel P1, the brightness φ2 of the second pixel P2, the brightness φ3 of the third pixel P3, and the brightness φ4 of the fourth pixel P4 constituting the area A, to the brightness acquiring unit 23. Here, in this example, the brightness data SP(n) is data expressing each brightness φ in 256 stages, for example, from 0 to 255.

[0045] The brightness φ approaches "0" as the brightness becomes lower (darker), and approaches "255" as the brightness becomes higher (brighter). For this reason, when the brightness φ of each pixel is arranged in numerical order for the entire evaluation area HR, as shown in Fig. 8, for example, as shown by the arrow in Fig. 5, the brightness φ changes along the circumferential direction of the ground surface W1 of the workpiece W. In particular, the brightness φ of the pixels corresponding to the first portion of the overall image data Z is constant at "255". In other words, the pixels corresponding to the first portion are so-called "whiteout."

[0046] Here, the distribution of the luminance φ in the evaluation region HR changes depending on the surface condition of the ground surface W1, specifically, the surface roughness. That is, when the light irradiated from the lighting device 13a hits the ground surface W1, specular reflection light and scattered reflection light are generated according to the microscopic inclination angle of the surface, but as the surface roughness increases, a microscopically steep change occurs, resulting in the occurrence of areas with large differences in brightness. As a result, the change in the distribution of the luminance φ tends to increase as the surface roughness (actual roughness value JS) of the ground surface W1 becomes rougher (larger), and to decrease as the surface roughness (actual roughness value JS) becomes smoother (smaller).

[0047] The luminance acquisition unit 23 acquires luminance data SP(n) for each of the n areas output from the area division unit 22. Here, the luminance data SP(n) acquired by the luminance acquisition unit 23 is data expressed as SP(n)=(φ1, φ2, φ3, φ4) using the luminance φ1 of the first pixel P1, the luminance φ2 of the second pixel P2, the luminance φ3 of the third pixel P3, and the luminance φ4 of the fourth pixel P4. As shown in FIG. 4, the luminance acquisition unit 23 includes a luminance average value calculation unit 231 and a luminance difference value calculation unit 232.

[0048] The luminance average calculation unit 231 calculates an average luminance value φ using the luminances (φ1, φ2, φ3, φ4) of the first pixel P1, the second pixel P2, the third pixel P3, and the fourth pixel P4 that constitute the area A. aThat is, the luminance average value calculation unit 231 calculates the luminance average value φ a Calculate (n).

number

[0049] The luminance difference value calculation unit 232 calculates the luminance (φ1, φ2, φ3, φ4) of each of the first pixel P1, the second pixel P2, the third pixel P3, and the fourth pixel P4 constituting the area A and the average luminance value φ calculated by the luminance average value calculation unit 231. a That is, the brightness difference value calculation unit 232 calculates the brightness difference value Δφ(n) from φ(n). a The luminance difference values ​​Δφ1 and φ between (n) and the luminance φ1 of the first pixel P1 a The luminance difference value Δφ2, φ between (n) and the luminance φ2 of the second pixel P2 a The luminance difference value Δφ3, φ between (n) and the luminance φ3 of the third pixel P3 a Using a luminance difference value Δφ4 between (n) and the luminance φ4 of the fourth pixel P4, the luminance difference value Δφ(n) is expressed as Δφ(n)=(Δφ1, Δφ2, Δφ3, Δφ4).

number

number

number

number

[0050] Here, as for the brightness difference value Δφ(n) calculated by the brightness difference value calculation unit 232, as shown in Fig. 9 corresponding to Fig. 8, the brightness difference value Δφ(n) of the overexposed pixel corresponding to the first portion of the ground surface W1 is "0". That is, the overexposed state of the pixel is when the pixel has a brightness exceeding the upper limit of brightness (e.g., "255"), and does not accurately reflect the surface condition (surface roughness) of the ground surface W1 of the workpiece W. Therefore, by calculating the brightness difference value Δφ(n), it is possible to eliminate the influence of the overexposed pixel on the estimation of the surface condition (surface roughness), and as a result, it becomes possible to accurately estimate the surface condition (surface roughness) of the ground surface W1 described later.

[0051] In this example, when the brightness difference value calculation unit 232 calculates the brightness difference values ​​Δφ(n)=(Δφ1, Δφ2, Δφ3, Δφ4), it can calculate a brightness difference squared sum value Δφg(n) by squaring and adding up each of the brightness difference values ​​(Δφ1, Δφ2, Δφ3, Δφ4) for the area A. That is, the brightness difference value calculation unit 232 can calculate the brightness difference squared sum value Δφg(n) for each area A according to the following formula 6.

number

[0052] The index value calculation unit 24 calculates the index value KI by calculating the square sum average value using the brightness difference square sum total value Δφg(n) (or the brightness difference value Δφ(n)) calculated by the brightness difference value calculation unit 232 for all of the n areas A. That is, the index value calculation unit 24 calculates the square sum average value of the brightness difference square sum total value Δφg(n) (or the brightness difference value Δφ(n)) of all areas A according to the following formula 7 as the index value KI.

number

[0053] (3-1. Details of the evaluation area setting unit 21) Next, details of the evaluation area setting unit 21 will be described. The evaluation area setting unit 21 of this example acquires the brightness φ of each pixel in the overall image data Z output from the camera 13, for example, in a first direction corresponding to the circumferential direction of the workpiece W and a second direction corresponding to the axial direction of the workpiece W, i.e., perpendicular to the first direction. Then, the evaluation area setting unit 21 identifies the center position of the overall image data Z in the first direction based on the change in brightness φ acquired along the first direction, and identifies the center position of the overall image data Z in the second direction based on the change in brightness φ acquired along the second direction.

[0054] Then, the intersection point between a line passing through the center position in the first direction and a line passing through the center position in the second direction is determined as the center of the evaluation area HR, and an evaluation area HR of a preset size is set. This makes it possible to appropriately set the evaluation area HR even for different workpieces W. For this reason, the evaluation area setting unit 21 of this example includes the first center position determining unit 211, the second center position determining unit 212, the area center determining unit 213, and the area setting unit 214, as described above. The setting of the evaluation area HR by the evaluation area setting unit 21 will be described in detail below.

[0055] The first central position determination unit 211 acquires the brightness φ of a plurality of pixels along a first direction corresponding to the circumferential direction of the workpiece W (ground surface W1) in the overall image data Z, as shown by the arrow in Fig. 10, and calculates the brightness sum average value φsa1 by averaging the acquired plurality of brightness φ. Then, the first central position determination unit 211 calculates the brightness sum average value φsa1 in the first direction for only a plurality of columns along the axial direction (i.e., the second direction) of the workpiece W (ground surface W1). This allows the first central position determination unit 211 to acquire the change in the brightness sum average value φsa1 in the first direction, as shown in Fig. 11.

[0056] The first central position determination unit 211 sets, in the change of the acquired luminance sum average value φsa1, a point where the luminance sum average value φsa1 becomes larger than a preset threshold value L1 (shown by a dashed line in FIG. 11) from a small value as a lower limit point Q11, and sets, in the change of the acquired luminance sum average value φsa1, a point where the luminance sum average value φsa1 becomes smaller than the threshold value L1 from a large value as an upper limit point Q12. Then, the first central position determination unit 211 determines the median value between the lower limit point Q11 and the upper limit point Q12 as a central position Q13 in the first direction, as shown by a dashed line in FIG. 11.

[0057] The second central position determination unit 212 acquires the brightness φ of a plurality of pixels along a second direction corresponding to the axial direction of the workpiece W (ground surface W1) in the overall image data Z, as shown by the arrow in Fig. 12, and calculates the average brightness summation value φsa2 by averaging the acquired plurality of brightness φ. The second central position determination unit 212 then calculates the brightness summation average value φsa2 in the second direction for only a plurality of rows along the circumferential direction (i.e., the first direction) of the workpiece W (ground surface W1). This allows the second central position determination unit 212 to acquire the change in the brightness summation average value φsa2 in the second direction, as shown in Fig. 13.

[0058] The second center position determination unit 212 sets a point where the acquired brightness sum average value φsa2 exceeds a preset threshold value L2 (shown by a dashed line in FIG. 13) from a small value to a large value as a lower limit point Q21, and sets a point where the brightness sum average value φsa2 exceeds the threshold value L2 from a large value to a small value as an upper limit point Q22. Then, the second center position determination unit 212 determines the median value between the lower limit point Q21 and the upper limit point Q22 as a center position Q23 in the second direction, as shown by a dashed line in FIG. 13.

[0059] 14, the region center determination unit 213 sets a straight line passing through the center position Q13 in the first direction determined by the first center position determination unit 211. The region center determination unit 213 also sets a straight line passing through the center position Q13 in the second direction determined by the second center position determination unit 212. Then, the region center determination unit 213 determines the intersection of the straight line set by the first center position determination unit 211 and the straight line set by the second center position determination unit 212 as the center RC of the evaluation region HR.

[0060] The area setting unit 214 finally determines the evaluation area HR using points RP1 and RP2 (in FIG. 14, point RP1 corresponding to the upper left of the evaluation area HR and point RP2 corresponding to the lower right of the evaluation area HR) that are set in advance to determine the evaluation area HR. Here, points RP1 and RP2 are expressed as coordinates with the center RC as the origin, and are set in advance according to the size of the evaluation area RP, for example.

[0061] As a result, even if the grinding surface W1 is displaced from the center of the image in the overall image data Z as shown in Fig. 15 (a case where the grinding surface W1 is displaced to the left is exemplified in Fig. 15), the evaluation area setting unit 21 can appropriately determine the evaluation area HR in response to this displacement. Similarly, even if the apex of the grinding surface W1 in the circumferential direction in the overall image data Z is displaced from the center of the image as shown in Fig. 16 (a case where the apex of the grinding surface W1 is displaced upward is exemplified in Fig. 16), the evaluation area setting unit 21 can appropriately determine the evaluation area HR in response to this displacement.

[0062] As shown in FIG. 17, even if the entire image data Z is the ground surface W1, the evaluation area setting unit 21 can set the evaluation area HR in the same manner as described above. That is, even in this case, the first center position determination unit 211 determines the lower limit point Q11, the upper limit point Q12, and the center position Q13 in the first direction and sets a straight line passing through the center position Q13. The center position Q13 may be set as the center of the entire image data Z. The second center position determination unit 212 determines the lower limit point Q21, the upper limit point Q22, and the center position Q23 in the second direction and sets a straight line passing through the center position Q23. Then, the area center determination unit 213 sets the intersection point of the straight line passing through the center position Q13 and the straight line passing through the center position Q23 as the center RC of the evaluation area HR, so that the area setting unit 214 can determine the evaluation area HR.

[0063] Also, as in the present example, even if there are two first parts as shown in FIG. 18, the evaluation area setting unit 21 can set the evaluation area HR to include the two first parts, as in the above-mentioned case. That is, even in this case, the first center position determination unit 211 determines the lower limit point Q11, the upper limit point Q12, and the center position Q13 in the first direction, and sets a straight line passing through the center position Q13. Also, the second center position determination unit 212 determines the lower limit point Q21, the upper limit point Q22, and the center position Q23 in the second direction, and sets a straight line passing through the center position Q13. In this case, the center position Q23 is determined between two vertices in the change of the luminance sum average value φsa1. Then, the area center determination unit 213 sets the intersection point of the straight line passing through the center position Q13 and the straight line passing through the center position Q23 as the center RC of the evaluation area HR, so that the area setting unit 214 can determine the evaluation area HR.

[0064] 1, the learning processing device 30 is configured to include a processor 31, a storage device 32, an interface 33, etc. The learning processing device 30 also has a server function, and is communicably connected to a plurality of grinding machines 10, i.e., grinding machines 11, and an image evaluation device 20 provided on each of the cylindrical grinding machines 50 (or including an image evaluation device 20 provided on a cylindrical grinding machine 50 operating in another remote factory).

[0065] The learning processing device 30 performs machine learning based on the index value KI output from the image evaluation device 20 and the actual roughness value JS corresponding to the surface condition of the ground surface W1 of the workpiece W output from the roughness meter 2 connected via an interface 33. Then, the learning processing device 30 generates a learned model for estimating the surface condition, i.e., the surface roughness, of the ground surface W1 of the workpiece W.

[0066] 1, each estimation calculation device 40 is configured to include a processor 41, a storage device 42, an interface 43, etc. Moreover, each estimation calculation device 40 is connected to the learning processing device 30 serving as a server and the corresponding image evaluation device 20 so as to be able to communicate with each other.

[0067] The estimation calculation device 40 is installed so as to be able to communicate with the control device 12 of each grinding device 10, and functions as an edge computer. Using the learned model generated by the learning processing device 30, the estimation calculation device 40 estimates the surface condition, i.e., surface roughness, of the ground surface W1 of the workpiece W based on the index value KI acquired as a feature value from the image evaluation device 20 during grinding of the workpiece W (or after grinding).

[0068] (4. Functional block configuration of surface condition estimation system 1) Functional blocks of the surface condition estimating system 1 will be described with reference to Fig. 19. As described above, the surface condition estimating system 1 includes the camera provided in the grinding device 10, the image evaluation device 20, the learning processing device 30, and the estimation calculation device 40.

[0069] The learning processing device 30 generates a learned model for estimating the surface roughness of the ground surface W1 based on the index value KI output from the image evaluation device 20 and the actual roughness value JS representing the surface roughness of the ground surface W1 measured by the roughness meter 2 (see FIG. 1). The learning processing device 30 includes a training data set acquisition unit 61, a training data set storage unit 62, and a model generation unit 63.

[0070] The training dataset acquisition unit 61 acquires a training dataset for performing machine learning. The training dataset acquisition unit 61 includes an index value acquisition unit 61a and an actual roughness value acquisition unit 61b. The index value acquisition unit 61a acquires an index value KI from the image evaluation device 20 as a training dataset. The actual roughness value acquisition unit 61b acquires an actual roughness value JS from the roughness meter 2 as a training dataset.

[0071] The training dataset storage unit 62 associates the index value KI and the actual roughness value JS acquired by the training dataset acquisition unit 61 and stores them as a training dataset. That is, the training dataset storage unit 62 associates the index value KI, which varies depending on the surface condition, i.e., the surface roughness, of the ground surface W1 of the workpiece W, with the actually measured surface roughness (actual roughness value) of the ground surface W1 and stores them.

[0072] The model generation unit 63 performs machine learning using the training dataset stored in the training dataset storage unit 62. Specifically, the model generation unit 63 performs machine learning using the index values ​​KI stored in the training dataset storage unit 62 in association with each other as explanatory variables and the surface roughness (actual roughness value) as a response variable. Then, the model generation unit 63 generates a trained model that represents the correlation between the index value KI and the surface roughness JS (actual roughness value).

[0073] Here, an example of a trained model generated by the model generation unit 63 is shown. When performing machine learning, the model generation unit 63 uses the index value KI and surface roughness JS for the ground surface W1 of multiple workpieces W. The surface roughness JS is measured at multiple grinding points (for example, seven points) on the ground surface W1 of one workpiece W. In this case, as shown in FIG. 20, for example, the index value KI and surface roughness JS corresponding to multiple grinding points can be plotted for each of eight workpieces W, that is, workpieces Wa, Wb, Wc, Wd, We, Wf, Wg, and Wh. It is assumed that the workpiece Wa is ground first and the workpiece Wh is ground last in terms of the grinding timing of the workpiece W.

[0074] According to this, the workpiece Wa (shown by a white circle in FIG. 20) which is ground first has a small index value KI and surface roughness JS, while the workpiece Wh (shown by a black diamond in FIG. 20) which is ground last has a large index value KI and surface roughness JS. In other words, the index value KI of the workpiece Wb (shown by a black circle in FIG. 20) tends to be larger than that of the workpiece Wa, and the index value KI of the workpiece Wc (shown by a white triangle in FIG. 20) tends to be larger than that of the workpiece Wb. Similarly, with regard to the index value KI, workpiece Wd (shown by a black triangle in FIG. 20) tends to be larger than workpiece Wc, workpiece We (shown by a white square in FIG. 20) tends to be larger than workpiece Wd, workpiece Wf (shown by a black square in FIG. 20) tends to be larger than workpiece We, and workpiece Wg (shown by a white diamond in FIG. 20) tends to be larger than workpiece Wf and smaller than workpiece Wh.

[0075] Furthermore, with respect to the surface roughness JS, the workpiece Wb tends to be greater than the workpiece Wa, and the workpiece Wc tends to be greater than the workpiece Wb. Similarly, with respect to the surface roughness JS, the workpiece Wd tends to be greater than the workpiece Wc, the workpiece We tends to be greater than the workpiece Wd, the workpiece Wf tends to be greater than the workpiece We, and the workpiece Wg tends to be greater than the workpiece Wf and smaller than the workpiece Wh.

[0076] Regarding the tendency of change in the index value KI and the surface roughness JS, when the average values ​​(shown by double circles in Fig. 20) of each workpiece W are plotted, the average values ​​can be approximated by a linear approximation formula, as shown by the thick solid line in Fig. 20. That is, in this example, the correlation between the index value KI and the surface roughness JS can be expressed by a linear approximation formula. Therefore, the model generation unit 63 in this example performs machine learning to generate a linear approximation formula that shows the correlation between the index value KI and the surface roughness JS as a learned model.

[0077] The estimation calculation device 40 estimates the surface roughness SS of the ground surface W1 of the workpiece W using the trained model generated by the learning processing device 30 and the index value KI acquired from the image evaluation device 20, and determines whether or not the grinding wheel 56 (tool T) of the cylindrical grinding machine 50 needs to be corrected based on the estimated surface roughness SS. If the grinding wheel 56 (tool T) needs to be corrected, the estimation calculation device 40 outputs a correction signal R to the control device 12. For this reason, the estimation calculation device 40 includes a model storage unit 71, an index value acquisition unit 72, a surface roughness estimation unit 73, a determination unit 74, and an output unit 75, as shown in FIG.

[0078] The model storage unit 71 stores the trained model (for example, a linear approximation equation representing the correlation between the index value KI and the surface roughness JS) generated by the model generation unit 63. The index value acquisition unit 72 acquires the index value KI from the image evaluation device 20. Here, the index value acquisition unit 72 performs the same process as the index value acquisition unit 61a of the training data set acquisition unit 61.

[0079] In this example, the index value acquiring unit 72 will be described as a separate element from the index value acquiring unit 61a of the training data set acquiring unit 61. However, it is also possible for the index value acquiring unit 61a of the training data set acquiring unit 61 to serve as the index value acquiring unit 72. In other words, the function of the element 61a in the learning processing device 30 is shared with part of the function of the estimation calculation device 40.

[0080] The surface roughness estimation unit 73 acquires the trained model stored in the model storage unit 71. In addition, the surface roughness estimation unit 73 acquires the index value KI acquired by the index value acquisition unit 72. As a result, the surface roughness estimation unit 73 estimates the surface roughness SS of the ground surface W1 of the workpiece W based on the trained model and the index value KI.

[0081] The determination unit 74 determines whether the surface roughness SS of the ground surface W1 estimated by the surface roughness estimation unit 73 is equal to or greater than the reference surface roughness SR preset for the ground surface W1 for each grinding process (i.e., rough grinding, fine grinding, fine grinding, and spark out). If the surface roughness SS is equal to or greater than the reference surface roughness SR, the determination unit 74 determines that the grinding wheel 56 (tool T) of the cylindrical grinding machine 50 (grinding machine 11) has deteriorated and the ground surface W1 cannot be appropriately ground, that is, correction is required. On the other hand, if the surface roughness SS is less than the reference surface roughness SR, the determination unit 74 determines that the grinding wheel 56 (tool T) of the cylindrical grinding machine 50 (grinding machine 11) has not deteriorated and the ground surface W1 can be appropriately ground, that is, correction is not required.

[0082] The output unit 75 outputs a correction signal R to the control device 12 according to the result of the determination by the determination unit 74. That is, when the determination unit 74 determines that the grinding wheel 56 (tool T) needs to be corrected, the output unit 75 outputs the correction signal R to the control device 12. On the other hand, when the determination unit 74 determines that the grinding wheel 56 (tool T) does not need to be corrected, the output unit 75 does not output the correction signal R to the control device 12.

[0083] As a result, in the control device 12 of the cylindrical grinding machine 50 (grinding machine 11), when the deterioration of the grinding wheel 56 (tool T) progresses, that is, when the grinding wheel 56 (tool T) needs to be corrected, the control device 12 acquires a correction signal R from the output unit 75 of the estimation calculation device 40 via the interface 14. Then, the control device 12 controls the grinding wheel correction device 58 to correct the grinding wheel 56. That is, the grinding wheel correction device 58 performs truing and / or dressing on the grinding wheel 56. As a result, the grinding wheel 56 can be corrected at an appropriate timing and an appropriate number of times. Therefore, the time required for correction can be shortened, and the cost required for correction can also be reduced.

[0084] As can be understood from the above description, the surface condition estimation system 1 of this embodiment can accurately estimate the surface roughness SS, which is the surface condition of the ground surface W1 of the workpiece W, using the index value KI resulting from the brightness of the ground surface W1 of the workpiece W. The surface condition estimation system 1 can then use the estimated surface roughness SS to determine the degree of deterioration of the grinding wheel 56 (tool T) of the cylindrical grinding machine 50 (grinding machine 11), which is the grinding device 10 that grinds the ground surface W1, and output a correction signal R. As a result, the control device 12 of the grinding device 10 can control the grinding wheel correction device 58 at the timing when the correction signal R is acquired, and correct the grinding wheel 56 by truing and / or dressing.

[0085] Therefore, it is possible to prevent the grinding wheel 56 (tool T) of the cylindrical grinding machine 50 (grinding machine 11) from being corrected excessively or delayed. This makes it possible to shorten the correction time required to correct the grinding wheel 56 and reduce the cost of correcting the grinding wheel 56.

[0086] (5. First alternative example) In the above-mentioned embodiment, the estimation calculation device 40 estimates the surface condition of the ground surface W1 of the workpiece W, i.e., the surface roughness SS, using the trained model generated by the learning processing device 30 and the index value KI acquired from the image evaluation device 20. As described above, the index value KI is a value related to the brightness φ that changes due to the surface roughness of the ground surface W1. For this reason, when estimating the surface roughness SS using the index value KI, it is necessary to make the average brightness uniform over the entire ground surface W1, i.e., the entire evaluation area HR. In other words, if the average brightness is the same or the effect of changes in the average brightness can be eliminated, it is possible to convert the index value KI into the surface roughness SS.

[0087] 21, for example, the trained model used by the estimation calculation device 40 in the present example described above, i.e., the linear approximation equation expressing the correlation (correlation function) between the index value KI and the surface roughness SS, has a tendency that as the average luminance increases (becomes brighter), the slope value increases while the intercept value decreases. Conversely, the linear approximation equation has a tendency that as the average luminance decreases (becomes darker), the slope value decreases while the intercept value increases.

[0088] That is, the linear approximation relationship of the slope k(φi) of the linear approximation equation to the change in average luminance φi in the ground surface W1 (evaluation area HR) and the linear approximation relationship of the intercept b(φi) of the linear approximation equation to the change in average luminance φi are as shown in FIG. 22. In FIG. 22, the open circles represent the slope of the linear approximation equation (i.e., the square sum average value line), and the open triangles represent the intercepts of the linear approximation equation (i.e., the square sum average value line). According to these linear approximation relationships, for example, when the image evaluation device 20 outputs the average luminance φi (=2 (N+M) The sum of the brightness φ of pixels is the number of pixels 2 N ×2 M By obtaining the value obtained by dividing the average luminance φi by the number of pixels, the slope k(φi) and intercept b(φi) corresponding to the average luminance φi can be determined.

[0089] Therefore, when the estimation calculation device 40 calculates and estimates the surface roughness SS using the index value KI and a linear approximation formula (learned model), KI=k(φi)×SS+b(φi) holds, so the surface roughness SS can be calculated and estimated according to SS=(KI-B(φi)) / k(φi) using the slope k(φi) and intercept b(φi) according to the average brightness φi. That is, in this case, normalization is performed by using the slope k(φi) and intercept b(φi) according to the average brightness φi, and the index value KI can be directly converted to the surface roughness SS.

[0090] Therefore, in the first modified example, it is possible to more easily estimate the surface roughness SS by calculation from the index value KI. Also, in the first modified example, normalization can be performed to eliminate the influence of the difference in the average brightness φi on the ground surface W1 (evaluation area HR), so that the surface roughness SS can be calculated and estimated more accurately.

[0091] (6. Other Examples) In the present embodiment and the first modified embodiment described above, the learning processing device 30 performs machine learning to generate a trained model, i.e., a correlation between the index value KI and the surface roughness SS. As a result, the estimation calculation device 40 uses the trained model generated by the learning processing device 30 to estimate the surface roughness SS of the ground surface W1 of the workpiece W.

[0092] However, the surface condition estimation system 1 does not necessarily have to include a learning processing device 30 that performs machine learning. In this case, the estimation calculation device 40 can perform multiple regression analysis using the index value KI obtained from the image evaluation device 20 and the surface roughness JS of the ground surface W1 obtained in advance from the roughness meter 2 to obtain a correlation used to estimate the surface roughness SS. In this case, the estimation calculation device 40 can accurately estimate the surface roughness SS using the index value KI obtained from the image evaluation device 20 based on the correlation obtained by the multiple regression analysis. Therefore, in this case as well, the same effects as those of the present example and the first modified example described above can be obtained.

[0093] In the present example and the first modified example described above, the surface condition estimation system 1 is configured so that the grinding device 10 includes a cylindrical grinding machine 50 as the grinding machine 11. The surface condition estimation system 1 is configured to estimate the surface condition, i.e., the surface roughness SS, of the ground surface W1 of the workpiece W, which is a shaft-shaped member ground by the cylindrical grinding machine 50.

[0094] However, the surface condition estimation system 1 is not limited to estimating the surface roughness SS of the ground surface W1 ground by the cylindrical grinding machine 50. For example, the grinding device 10 may include a cam lathe or a surface grinding machine as the grinding machine 11, and the surface condition estimation system 1 may estimate the surface condition, i.e., the surface roughness SS, of the ground cam surface or flat surface.

[0095] Furthermore, the surface condition estimation system 1 is not limited to estimating the surface condition of the ground surface W1, but can also estimate, for example, the surface condition of the cut surface of a workpiece (e.g., a gear) cut by a cutting device. In this case, as in the present example and the first variant example described above, the surface condition of the cut surface, for example, surface roughness, can be estimated using the index value KI obtained from the image evaluation device 20. Therefore, in this case, too, the same effects as in the present example and the first variant example described above can be obtained.

[0096] In the above-described present example and first modified example, the estimation calculation device 40 outputs a correction signal R for correcting the tool T based on the estimated surface roughness SS. However, the estimation calculation device 40 can also output a signal for appropriately changing the machining conditions under which the machine tool uses the tool T to machine the workpiece W based on the estimated surface roughness SS. This allows the machine tool to acquire the output signal and appropriately change the machining conditions to machine the workpiece W with high precision.

[0097] Furthermore, in the above-described present example and the first modified example, the surface roughness SS is estimated as the surface condition of the ground surface W1. However, the surface condition that can be estimated is not limited to the surface roughness SS. The surface condition estimation system 1 can accurately estimate the shape of the ground surface as the surface condition, for example, the shape (outer shape, inner shape, steps, etc.) of the ground surface formed by the contact of the tool T with the workpiece W, by using the index value KI acquired from the image evaluation device 20. [Explanation of symbols]

[0098] 1...surface condition estimation system, 2...roughness meter, 10...grinding device, 11...grinding machine, 12...control device, 13...camera, 13a...illumination device, 14...interface, 20...image evaluation device, 21...evaluation area setting unit, 22...area division unit, 23...luminance acquisition unit, 231...luminance average value calculation unit, 232...luminance difference value calculation unit, 24...index value calculation unit, 30...learning processing device (arithmetic device), 31...processor, 32...storage device, 33...interface, 40... estimation calculation device (calculation device), 41... processor, 42... storage device, 43... interface, 50... cylindrical grinding machine, 51... bed, 52... headstock, 52a... motor, 53... tailstock, 54... traverse base, 54a... motor, 55... grinding wheel head, 55a... motor, 56... grinding wheel, 56a... motor, 57... sizing device, 57a... feed mechanism, 58... grinding wheel correction device, 61... training data set acquisition unit, 61a... index value acquisition unit, 61b ...actual roughness value acquisition unit, 62...training data set storage unit, 63...model generation unit, 71...model storage unit, 72...index value acquisition unit, 73...estimation unit, 74...judgment unit, 75...output unit, A...area, Am...area, HR...evaluation area, JS...actual roughness value, KI...index value, P1...first pixel, P2...second pixel, P3...third pixel, P4...fourth pixel, L1...threshold, L2...threshold, Q11...lower limit, Q12...upper limit, Q13...center position, Q2 1...Lower limit, Q22...Upper limit, Q23...Center position, R...Correction signal, RC...Center, RP1,RP2...Point, SP...Brightness data, SS...(Estimated) surface roughness (surface condition), T...Tool, W...Workpiece, W1...Grounded surface, Z...Overall image data, b...Intercept, k...Slope, Δφ(n)...Brightness difference value, Δφg(n)...Brightness squared difference sum, φ...Brightness, φa...Brightness average value, φi...Average brightness, φsa1...Brightness sum average value, φsa2...Brightness sum average value

Claims

1. A surface condition estimation method for estimating a surface condition of a machined surface of a tool of a machine tool that machines a workpiece with a tool, the method comprising: applying a region setting method for setting a region in a captured image; and estimating a surface condition of the tool and a machined surface of the workpiece, the method comprising: The region setting method includes: determining a first central position in a first direction of the image based on a variation in luminance of a plurality of pixels forming the image in the first direction of the image; determining a second center position in a second direction different from the first direction based on the change in luminance of the pixel in the second direction; determining a center of the region based on the first center location and the second center location; Setting the region based on the center; The surface state estimation method includes: setting an evaluation area for the image of the entire processing surface according to the area setting method; Dividing the evaluation area by a plurality of areas formed by a plurality of adjacent pixels among the plurality of pixels forming the evaluation area; Calculating an average luminance value for each of the areas using the luminances of a plurality of adjacent pixels; calculating a luminance difference value representing a difference between the luminance of each of a plurality of adjacent pixels and the average luminance value for each of the areas; Calculating the square root of the sum of the squares of the brightness difference values ​​as an index value; A surface condition estimating method, comprising: using the index value to estimate a surface condition of the machined surface that is correlated with the index value.

2. When the first direction and the second direction are perpendicular to each other, The surface condition estimating method according to claim 1 , wherein the center is an intersection point between a line perpendicular to the first direction and passing through the first central position, and a line perpendicular to the second direction and passing through the second central position.

3. The first central position and the second central position are 3. The surface condition estimating method according to claim 1, wherein the change in luminance is determined based on a median between a lower limit point where the luminance changes from a lower luminance to a higher luminance relative to a preset luminance threshold value and an upper limit point where the luminance changes from a higher luminance to a lower luminance relative to the threshold value.

4. the correlation is a trained model generated by performing machine learning using a training data set including the index value as an explanatory variable, the surface state of the processed surface as an objective variable, and the explanatory variable and the objective variable; The surface condition estimating method according to any one of claims 1 to 3, wherein the surface condition of the processed surface is estimated using the index value and the trained model.

5. The correlation is expressed by a linear approximation equation, a slope of the linear approximation equation increases as an average luminance of the plurality of pixels forming the image increases, and an intercept of the linear approximation equation decreases as the average luminance increases, 5. The surface condition estimating method according to claim 1, wherein the surface condition of the processed surface is estimated using the index value and the linear approximation equation having the slope and the intercept determined based on the average luminance.

6. The surface condition estimating method according to any one of claims 1 to 5, further comprising setting a timing for correcting the tool based on the estimated surface condition of the machined surface, or changing a machining condition under which the machine tool uses the tool to machine the workpiece.

7. The surface state estimating method according to any one of claims 1 to 6, wherein the adjacent pixels are four or more of the pixels adjacent to each other.

8. The surface condition estimating method according to any one of claims 1 to 7, wherein the machine tool is a grinding device having a grinding wheel as the tool and grinding the workpiece with the grinding wheel.

9. The surface condition estimating method according to any one of claims 1 to 8, wherein the surface condition of the processed surface is surface roughness.

10. A surface condition estimation system including an area setting device for setting an area for a captured image, the surface condition estimation system estimating a surface condition of a tool of a machine tool that processes a workpiece with a tool and a machined surface of the workpiece, The area setting device includes: a first center position determiner for determining a first center position in a first direction of the image based on a variation in luminance of a plurality of pixels forming the image in the first direction; a second center position determiner that determines a second center position in a second direction different from the first direction based on a change in the luminance of the pixel in the second direction; a region center determination unit that determines a center of the region based on the first center position and the second center position; a region setting unit that sets the region based on the center, The surface state estimation system includes: an imaging device that images the processing surface and outputs the captured image; an image evaluation device having: an area dividing unit that divides an evaluation area set by the area setting device for the image capturing the entire processing surface output from the imaging device into a plurality of areas formed from a plurality of adjacent pixels; a brightness average value calculation unit that calculates a brightness average value for each of the areas using the brightness of each of the adjacent pixels; a brightness difference value calculation unit that calculates a brightness difference value representing a difference between the brightness average value for each of the areas and the brightness of each of the adjacent pixels; and an index value calculation unit that calculates the square root of the sum of squares of the brightness difference value calculated by the brightness difference value calculation unit as an index value; and an estimation calculation device having an estimation unit that uses the index value calculated by the image evaluation device to estimate a surface state of the processed surface that is correlated with the index value.

Images