Surface condition estimation method and surface condition estimation system

Abstract

To provide a surface state estimation method capable of accurately estimating the surface state of a workpiece.SOLUTION: A surface state estimation method for estimating a surface state of a processed surface of a tool or a workpiece applied to a machine tool that processes the workpiece with the tool includes: calculating a luminance average value φa(n) using luminance φ of each of a plurality of adjacent pixels among a plurality of pixels forming an image obtained by taking an image of the processed surface; calculating a luminance difference value Φ(n) representing difference between the luminance φ of each of the plurality of adjacent pixels and the luminance average value φa(n); calculating a square root of the sum of squares of the luminance difference Φ(n) as an index value KI; and estimating the surface state of the processed surface that correlates with the index value KI using the index value KI.SELECTED DRAWING: Figure 4

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-162558 [Patent Document 2] Japanese Patent Application Laid-Open No. 2018-40599 Summary of the Invention [Problem to be solved by the invention]

[0004] When correcting a tool, frequent correction can always maintain good machining accuracy for the workpiece. However, correcting a tool requires time and costs. Therefore, appropriately setting the timing for correcting a tool is extremely effective because it can shorten the time required for correction and reduce the cost required for correction. While the above-mentioned conventional technology makes it possible to set the timing for correcting a tool based on the estimated surface roughness, it is desirable to be able to accurately set the timing for correcting a tool by more accurately estimating the surface condition of a workpiece.

[0005] An object of the present invention is to provide a surface condition estimation method and a surface condition estimation system that can accurately estimate the surface condition of a workpiece. [Means for solving the problem]

[0006] (Surface condition estimation method) One aspect of the present invention is It is applied to machine tools that process workpieces using tools, The aforementioned Tools and The aforementioned A surface condition estimation method for estimating a surface condition of a machined surface of a workpiece, comprising: An evaluation area is set in an image of the entire processed surface; Dividing the evaluation region among a plurality of pixels forming the image into a plurality of areas formed by a plurality of adjacent pixels; In each of said areas, Adjacent multiple The aforementioned Calculating the average luminance value using the luminance of each pixel; In each of said areas, Adjacent multiple The aforementioned Each pixel The aforementioned Brightness and The aforementioned A brightness difference value representing a difference from the brightness average value is calculated. The aforementioned The square root of the sum of the squares of the brightness difference values ​​is calculated as an index value. The aforementioned Using the index value, The aforementioned Correlated with index values The aforementioned Estimate the surface condition of the machined surface ,Surface condition estimation method. Another aspect of the present invention is a surface condition estimation method that is applied to a machine tool that machines a workpiece with a tool and estimates a surface condition of a machined surface of the tool and the workpiece, the method comprising: calculating a luminance average value using the luminance of each of a plurality of adjacent pixels among a plurality of pixels forming an image of the processed surface; calculating a brightness difference value representing a difference between the brightness of each of the plurality of adjacent pixels and the average brightness value; calculating a square root of the sum of squares of the brightness difference values ​​as an index value; Using the index value, a surface condition of the processed surface that is correlated with the index value is estimated; The correlation is expressed by a linear approximation formula, 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, The surface condition estimation method is such that the surface condition of the processed surface is estimated using the index value and the linear approximation equation having the slope and intercept determined based on the average brightness.

[0007] (Surface condition estimation system) Another aspect of the present invention is It is applied to machine tools that process workpieces using tools, The aforementioned Tools and The aforementionedA surface condition estimation system for estimating a surface condition of a machined surface of a workpiece, comprising: The aforementioned an imaging device that captures an image of the processing surface and outputs the captured image; an evaluation area setting unit that sets an evaluation area for the image; an area dividing unit that divides the evaluation area into a plurality of areas formed by a plurality of adjacent pixels among a plurality of pixels that form the image; Adjacent multiple The aforementioned a brightness average value calculation unit that calculates a brightness average value using the brightness of each pixel; In each of the areas Calculated by the brightness average value calculation unit The aforementioned The average brightness and adjacent The aforementioned Each pixel The aforementioned a brightness difference value calculation unit that calculates a brightness difference value that represents a difference between the brightness and the image; The aforementioned The brightness difference value calculated by the brightness difference value calculation unit The aforementioned an image evaluation device having an index value calculation unit that calculates a square root of the sum of squares of brightness difference values ​​as an index value; The aforementioned Calculated by an image evaluation device The aforementioned Using the index value, The aforementioned Correlated with index values The aforementioned an estimation calculation device having an estimation unit that estimates the surface condition of the processed surface; Equipped with Also, it is a surface condition estimation system. Another aspect of the present invention is a surface condition estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a surface condition of a machined surface of the tool and the workpiece, the system comprising: an imaging device that captures an image of the processed surface and outputs the captured image; an image evaluation device having: a brightness average value calculation unit that calculates a brightness average value using the brightness of each of a plurality of adjacent pixels among a plurality of pixels that form the image output from the imaging device; a brightness difference value calculation unit that calculates a brightness difference value that represents a difference between the brightness average value calculated by the brightness average value calculation unit and the brightness of each of the plurality of adjacent pixels; and an index value calculation unit that calculates, as an index value, the square root of the sum of squares of the brightness difference value calculated by the brightness difference value calculation unit; an estimation calculation device having an estimation unit that estimates a surface condition of the processed surface that is correlated with the index value calculated by the image evaluation device; Equipped with The correlation is expressed by a linear approximation formula, 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, The surface condition of the processed surface is estimated by a surface condition estimation system using the index value and the linear approximation equation having the slope and intercept determined based on the average brightness.

[0008] According to these, the surface condition of the machined surface of the workpiece can be estimated with high accuracy using index values ​​resulting from the brightness of the machined surface of the workpiece. Furthermore, 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 to correct the tool or change the machining conditions at an appropriate time.

[0009] Therefore, it is possible to prevent excessive correction of the tool of the machine tool and delay in correction, thereby shortening the correction time required to correct the tool and reducing the cost for correcting the tool. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a surface state estimation system. [Figure 2] 2 is a diagram showing the configuration of a grinding device which is an example of the machine tool in FIG. 1. FIG. [Figure 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] FIG. 2 is a diagram showing a functional block configuration of the image evaluation device of FIG. [Figure 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. FIG. [Figure 6] FIG. 5 is a diagram for explaining an area set by the image evaluation device of FIG. 4. [Figure 7] FIG. 10 is a diagram for explaining the numbers of pixels that make up an area. [Figure 8] FIG. 10 is a diagram for explaining a change in brightness in the circumferential direction of a workpiece. [Figure 9] FIG. 10 is a diagram for explaining a brightness difference value. [Figure 10]FIG. 2 is a diagram showing a functional block configuration of the surface state estimation system. [Figure 11] FIG. 10 is a diagram for explaining the correlation between an index value and surface roughness. [Figure 12] FIG. 10 is a diagram illustrating the influence of average luminance on a linear approximation formula according to a first modified example. [Figure 13] 10A and 10B 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. [Figure 14] FIG. 10 is a diagram illustrating acquisition of luminance in a first direction according to a second modified example. [Figure 15] 10A and 10B are diagrams illustrating how to determine the center position of luminance in the first direction according to a second modified example. [Figure 16] FIG. 10 is a diagram illustrating acquisition of luminance in a second direction according to a second modification. [Figure 17] FIG. 10 is a diagram illustrating determination of the center position of luminance in the second direction according to a second modification. [Figure 18] FIG. 10 is a diagram illustrating how to determine the center position of an evaluation area according to a second modification. [Figure 19] FIG. 10 is a diagram illustrating setting of an evaluation area when the center position is shifted according to a second modified example. [Figure 20] 10A and 10B are diagrams illustrating setting of an evaluation area when the center position is shifted according to a second modified example. [Figure 21] FIG. 10 is a diagram illustrating setting of an evaluation area according to a second modified example. [Figure 22] FIG. 10 is a diagram illustrating setting of an evaluation area according to a second modified example. DETAILED DESCRIPTION OF THE INVENTION

[0011] (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. Examples of machine tools that machine workpieces include various cutting machines that perform cutting processes (e.g., gear machining machines and machining centers) and various grinding machines that perform grinding processes (e.g., cylindrical grinders, cam grinders, surface grinders).

[0012] In this example, the machine tool is exemplified as a grinding device that grinds a workpiece through grinding processes such as rough grinding, fine grinding, fine grinding, and spark-out. Examples of workpieces that can be ground by the grinding device include simple shaft-shaped members, crankshafts, camshafts, and flat plates. As the workpiece in this example, a simple shaft-shaped member with a circular cross section is ground by a cylindrical grinding machine, as will be described later.

[0013] (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 arithmetic device (30, 40). The surface condition estimating system 1 may target one grinding machine 10, or, as shown in Fig. 1, may target multiple grinding machines.

[0014] The surface condition estimating system 1 of this example is illustrated as being equipped with a plurality of grinding machines 10. The surface condition estimating system 1 of this example is illustrated as being equipped with an image evaluation device 20 in each of the grinding machines 10, as well as one arithmetic device 30 and a plurality of arithmetic devices 40 each provided in each of the grinding machines 10.

[0015] 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 the grinding surface W1 that can be observed during the grinding process of the workpiece W.

[0016] 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 captured for each grinding operation. 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 overall image by capturing an image of the ground surface W1 of the workpiece W, as with the camera 13, and it is also possible to use, for example, a 3D scanner device or the like that can capture an overall image of the unevenness of the ground surface W1.

[0017] 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 region of the entire image (entire image data) for estimating the surface condition.

[0018] The image evaluation device 20 of this example calculates an area index value for each of a plurality of areas, each of which includes 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 the area index value as 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.

[0019] In this example, the arithmetic devices (30, 40) apply machine learning using the index values ​​output from the image evaluation device 20 to estimate the surface roughness as the surface condition of the ground surface W1 of the workpiece W, which has a correlation with the index values. Note that the correlation obtained by applying machine learning represents the correlation between the index values ​​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.

[0020] 1, the calculation devices (30, 40) are configured with 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 components, they can also be configured as a single device. Also, some or all of the calculation devices (30, 40) can be incorporated into a production line configured to include multiple grinding machines 10.

[0021] In this example, the learning processing device 30 has a so-called server function and is communicably connected to a plurality of estimation calculation devices 40 provided in each of the plurality of grinding machines 10. Furthermore, 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.

[0022] (3. Details of the configuration of surface condition estimation system 1) The configuration of the surface condition estimation system 1 will be described in more detail with reference to Fig. 1. The surface condition estimation 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 that functions as a part of the calculation device, and an estimation calculation device 40 provided in each of the grinding machines 10 that functions as another part of the calculation device.

[0023] The grinding device 10 mainly comprises a grinding machine 11 that uses a grinding wheel T to grind the grinding surface W1 of the workpiece W, a control device 12 that controls the grinding machine 11, a camera 13 as an image acquisition device, and an interface 14.

[0024] In this example, a wheelhead traverse type cylindrical grinding machine 50 is used as the grinding machine 11, as shown in Fig. 2. The grinding machine 11 may also be a table traverse type.

[0025] 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.

[0026] The bed 51 is fixed on an installation surface. The headstock 52 is provided on the upper surface of the bed 51, on the front side in the X-axis direction (the lower side in FIG. 2) and on one end side in the Z-axis direction (the left side in FIG. 2). The headstock 52 supports the workpiece W rotatably around the Z-axis. The workpiece W is rotated by driving a motor 52a provided on the headstock 52.

[0027] The tailstock 53 is provided on the upper surface of the bed 51 in 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.

[0028] 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 driving 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 driving a motor 55a provided on the traverse base 54. The grinding wheel 56 (tool T) is rotatably supported on the grinding wheel head 55. The grinding wheel 56 is rotated by driving a motor 56a provided on the grinding wheel head 55. The grinding wheel 56 is composed of a plurality of abrasive grains fixed with a bond material.

[0029] 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.

[0030] The grinding wheel dressing device 58 corrects the shape of the grinding wheel 56. That is, the grinding wheel dressing device 58 is a device that performs truing and dressing of the grinding wheel 56. Truing is a reshaping process that shapes the grinding wheel 56 to fit the shape of the workpiece W when the grinding wheel 56 has deteriorated due to grinding, for example, wear or abrasion, or the abrasive grains have fallen off or been broken, and removes runout of the grinding wheel 56 caused by uneven wear. Dressing is a dressing (sharpening) process for the grinding wheel 56, adjusting the protrusion amount of the abrasive grains, and creating cutting edges for the abrasive grains. That is, dressing is a process that corrects dullness, clogging, missing grains, etc., and is usually performed after truing.

[0031] 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, machining 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 when the operation command data is input.

[0032] As a result, the control device 12 controls the operation of the cylindrical grinding machine 50 (grinder 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 dimensions (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, spark out, etc.

[0033] The control device 12 is also 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 example controls the motors 52a, 54a, 55a, 56a and the grinding wheel dressing device 58, etc., at the timing when it acquires a correction signal R output from the estimation calculation device 40 as the timing for correcting the grinding wheel 56, thereby performing correction (truing and / or dressing) of the grinding wheel 56. Here, the estimation calculation device 40 of this example 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 will be described later.

[0034] As shown in Fig. 2, the camera 13 is mounted on the traverse base 54, and captures an image of the ground surface W1 of the workpiece W with imaging light irradiated onto the ground surface W1. For this purpose, the camera 13 is provided with, for example, an illumination device 13a that is arranged concentrically with the lens (not shown) of the camera 13 and irradiates annular imaging light, as shown in Fig. 3. The camera 13 then outputs overall image data Z representing an overall image of the ground surface W1 reflecting the imaging light to the image evaluation device 20, for example, via the interface 14 of the control device 12 (see Fig. 1).

[0035] As shown in FIG. 4, the image evaluation device 20 includes an evaluation region setting unit 21, an area dividing unit 22, a brightness acquisition unit 23, and an index value calculation 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. As described above, the camera 13 emits an annular imaging light. Therefore, the overall image data Z capturing the ground surface W1 of the workpiece W includes a first portion (shown in white in FIG. 5) that strongly reflects light and a second portion (shown in matte finish in FIG. 5) that weakly reflects light. In this example, a case is illustrated in which two first portions that strongly reflect light exist in the overall image data Z.

[0036] 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 that it includes two first portions.

[0037] As shown in FIG. 6, the area dividing unit 22 sets four adjacent pixels (shown as small squares in FIG. 6) that make up the evaluation area HR as an 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 that make up 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 An example will be given in which the image is composed of pixels.

[0038] In this case, each pixel is assigned a number for identification. For example, as shown in FIG. 6, the pixel at the top left of the evaluation area HR is assigned "0," the pixel immediately adjacent to the right of pixel "0" is assigned "1," and the last pixel in the first column of the evaluation area HR is assigned "2." N Similarly, the pixel immediately below and adjacent to pixel "0" in the evaluation area HR is assigned "2 N " is assigned, and pixel "2 N The pixel to the right of " is "2 N +1" is assigned to the last pixel of the second column of the evaluation area HR, and "2x2 N Similarly, pixel "2" in the evaluation area HR is assigned N The pixel immediately below it is marked with "2x2 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.

[0039] As a result, the numbers assigned to the four pixels that make up the m-th area Am shown in FIG. 6 are as follows: the first pixel P1 located at the top left of the area Am is "2i+2j×2" as shown in FIG. 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 "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" is a value that varies from "0" to "2 M-1 It is a value that varies from -1.

[0040] Then, the area dividing unit 22 outputs brightness data SP(n) including the brightness φ of each of the four pixels constituting the area A, a first pixel P1, a second pixel P2, a third pixel P3, and a fourth pixel P4, 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, to the brightness acquiring unit 23. Here, in this example, the brightness data SP(n) is data that represents each brightness φ in 256 stages, for example, from 0 to 255.

[0041] Note that the brightness φ approaches "0" as it becomes lower (darker), and approaches "255" as it 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 indicated 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, for pixels corresponding to the first portion of the overall image data Z, the brightness φ becomes constant at "255." In other words, the pixels corresponding to the first portion become so-called "blown out" highlights.

[0042] Here, the distribution of brightness φ in the evaluation region HR changes depending on the surface condition of the ground surface W1, specifically, the surface roughness. That is, when 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. However, as the surface roughness increases, a microscopically steep change occurs, resulting in the creation of areas with large differences in brightness. As a result, the change in the distribution of brightness φ tends to increase as the surface roughness (actual roughness value JS) of the ground surface W1 becomes rougher (increases), and decrease as the surface roughness (actual roughness value JS) becomes smoother (decreases).

[0043] 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.

[0044] The brightness average value calculation unit 231 calculates a brightness average value φ using the brightnesses (φ1, φ2, φ3, φ4) of the first pixel P1, the second pixel P2, the third pixel P3, and the fourth pixel P4 that make up the area A. a That is, the brightness average value calculation unit 231 calculates the brightness average value φ (n) according to the following formula 1. aCalculate (n).

number

[0045] The brightness difference value calculation unit 232 calculates the brightness (φ1, φ2, φ3, φ4) of each of the first pixel P1, the second pixel P2, the third pixel P3, and the fourth pixel P4 that make up the area A, and the brightness average value φ calculated by the brightness average value calculation unit 231. a That is, the brightness difference value calculation unit 232 calculates the brightness difference value Δφ(n) from φ. a The luminance difference values ​​Δφ1 and φ between (n) and the luminance φ1 of the first pixel P1 a The luminance difference values ​​Δφ2 and φ between (n) and the luminance φ2 of the second pixel P2 a The difference in brightness Δφ3, φ between (n) and the brightness φ3 of the third pixel P3 a Using a brightness difference value Δφ4 between (n) and the brightness φ4 of the fourth pixel P4, the brightness difference value Δφ(n) is expressed as (Δφ1, Δφ2, Δφ3, Δφ4).

number

number

number

number

[0046] Here, with regard to 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." In other words, a pixel is overexposed when it has a brightness that exceeds the upper brightness limit (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), the influence of the overexposed pixel on the estimation of the surface condition (surface roughness) can be eliminated, and as a result, it becomes possible to accurately estimate the surface condition (surface roughness) of the ground surface W1, which will be described later.

[0047] In this example, once 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 the brightness difference values ​​(Δφ1, Δφ2, Δφ3, Δφ4) for 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 equation 6.

number

[0048] The index value calculation unit 24 calculates the index value KI by calculating the average sum of squares using the brightness difference square sums Δφg(n) (or brightness difference values ​​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 average sum of squares of the brightness difference square sums Δφg(n) (or brightness difference values ​​Δφ(n)) of all areas A as the index value KI according to the following equation 7.

number

[0049] 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 connected to be able to communicate with an image evaluation device 20 provided on each of a plurality of grinding machines 10, i.e., grinding machines 11, that are cylindrical grinding machines 50 (or including an image evaluation device 20 provided on a cylindrical grinding machine 50 operating in another remote factory).

[0050] 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 the interface 33. Then, the learning processing device 30 generates a learned model for estimating the surface condition, i.e., surface roughness, of the ground surface W1 of the workpiece W.

[0051] 1, each estimation calculation device 40 is configured to include a processor 41, a storage device 42, an interface 43, etc. Furthermore, 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 them.

[0052] The estimation calculation device 40 is installed so as to be able to communicate with the control device 12 of each grinding machine 10, and functions as an edge computer. Using the trained 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 (or after) grinding of the workpiece W.

[0053] (4. Functional block configuration of surface condition estimation system 1) The functional blocks of the surface condition estimating system 1 will be described with reference to Fig. 10. As described above, the surface condition estimating system 1 includes a camera provided in the grinding device 10, an image evaluation device 20, a learning processing device 30, and an estimation calculation device 40.

[0054] The learning processing device 30 generates a trained 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 dataset acquisition unit 61, a training dataset storage unit 62, and a model generation unit 63.

[0055] 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 index values ​​KI from the image evaluation device 20 as the training dataset. The actual roughness value acquisition unit 61b acquires actual roughness values ​​JS from the roughness meter 2 as the training dataset.

[0056] The training data set storage unit 62 associates the index value KI and the actual roughness value JS acquired by the training data set acquisition unit 61 and stores them as a training data set. That is, the training data set storage unit 62 associates and stores the index value KI, which varies depending on the surface condition, i.e., 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.

[0057] 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 association with each other in the training dataset storage unit 62 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).

[0058] 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 surfaces W1 of multiple workpieces W. The surface roughness JS is measured at multiple grinding points (e.g., seven locations) on the ground surface W1 of one workpiece W. In this case, as shown in FIG. 11, the index value KI and surface roughness JS corresponding to multiple grinding points can be plotted for each of eight workpieces W, i.e., workpieces Wa, Wb, Wc, Wd, We, Wf, Wg, and Wh. It is assumed that the workpieces W are ground first, and the workpieces Wh are ground last.

[0059] According to this, the workpiece Wa (shown by a white circle in FIG. 11) that was ground first has a small index value KI and surface roughness JS, while the workpiece Wh (shown by a black diamond in FIG. 11) that was ground last has a large index value KI and surface roughness JS. That is, the index value KI of the workpiece Wb (shown by a black circle in FIG. 11) 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. 11) 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 Figure 11) tends to be larger than workpiece Wc, workpiece We (shown by a white square in Figure 11) tends to be larger than workpiece Wd, workpiece Wf (shown by a black square in Figure 11) tends to be larger than workpiece We, and workpiece Wg (shown by a white diamond in Figure 11) tends to be larger than workpiece Wf and smaller than workpiece Wh.

[0060] Furthermore, with respect to the surface roughness JS, the workpiece Wb tends to have a larger surface roughness than the workpiece Wa, and the workpiece We tends to have a larger surface roughness than the workpiece Wb. Similarly, with respect to the surface roughness JS, the workpiece Wd tends to have a larger surface roughness than the workpiece Wc, the workpiece We tends to have a larger surface roughness than the workpiece Wd, the workpiece Wf tends to have a larger surface roughness than the workpiece We, and the workpiece Wg tends to have a larger surface roughness than the workpiece Wf and a smaller surface roughness than the workpiece Wh.

[0061] When the average values ​​(shown by double circles in FIG. 11) of each workpiece W are plotted with respect to the tendency of change in these index values ​​KI and surface roughness JS, the average values ​​can be approximated by a linear approximation formula, as shown by the thick solid line in FIG. 11. That is, in this example, the correlation between the index value KI and 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 represents the correlation between the index value KI and surface roughness JS as a trained model.

[0062] 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. 10 .

[0063] The model storage unit 71 stores the trained model (for example, a linear approximation formula 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 processing as the index value acquisition unit 61a of the training dataset acquisition unit 61.

[0064] 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, the index value acquiring unit 61a of the training data set acquiring unit 61 can also serve as the index value acquiring unit 72. In other words, the function of the element 61a in the learning processing device 30 is also used as part of the function of the estimation calculation device 40.

[0065] The surface roughness estimation unit 73 acquires the trained model stored in the model storage unit 71. The surface roughness estimation unit 73 also 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.

[0066] 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 a 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 so that the ground surface W1 cannot be properly ground, i.e., adjustment 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 so that the ground surface W1 can be properly ground, i.e., adjustment is not required.

[0067] The output unit 75 outputs a correction signal R to the control device 12 in accordance with the determination result 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.

[0068] As a result, when the grinding wheel 56 (tool T) deteriorates, i.e., when the grinding wheel 56 (tool T) needs to be corrected, the control device 12 of the cylindrical grinding machine 50 (grinding machine 11) 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 correcting device 58 to correct the grinding wheel 56. That is, the grinding wheel correcting device 58 performs truing and / or dressing on the grinding wheel 56. This allows the grinding wheel 56 to 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.

[0069] As can be understood from the above explanation, the surface condition estimation system 1 of this example 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 apparatus 10 that grinds the ground surface W1, and output a correction signal R. As a result, the control device 12 of the grinding apparatus 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.

[0070] Therefore, it is possible to prevent excessive correction or delay in correction of the grinding wheel 56 (tool T) of the cylindrical grinding machine 50 (grinding machine 11), thereby shortening the correction time required to correct the grinding wheel 56 and reducing the cost for correcting the grinding wheel 56.

[0071] (5. First alternative example) In the above-described example, the estimation calculation device 40 estimates the surface condition, i.e., 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. As described above, the index value KI is a value related to the brightness φ, which varies due to the surface roughness of the ground surface W1. Therefore, when estimating the surface roughness SS using the index value KI, it is necessary to make the average brightness uniform across the entire ground surface W1, i.e., across the entire evaluation area HR. In other words, if the average brightness is the same or the influence of changes in the average brightness can be eliminated, it is possible to convert the index value KI to the surface roughness SS.

[0072] 12, for example, the trained model used by the estimation calculation device 40 in the present example described above, i.e., the linear approximation equation representing the correlation (correlation function) between the index value KI and the surface roughness SS, tends to have a larger slope value and a smaller intercept value as the average luminance increases (becomes brighter). Conversely, the linear approximation equation tends to have a smaller slope value and a larger intercept value as the average luminance decreases (becomes darker).

[0073] That is, the linear approximation relationship of the slope k(φi) of the linear approximation equation to the change in the average brightness 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 the average brightness φi are as shown in FIG. 13. In FIG. 13, 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 brightness φi (=2 (N+M) The sum of the brightness φ of the pixels is the number of pixels 2 N ×2 M By obtaining the value obtained by dividing the average brightness φi by the number of pixels, the slope k(φi) and intercept b(φi) corresponding to the average brightness φi can be determined.

[0074] Therefore, when the estimation calculation device 40 calculates and estimates the surface roughness SS using the index value KI and a linear approximation formula (trained model), KI = k(φi) × SS + b(φi) holds, and therefore 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.

[0075] Therefore, in the first modified example, it is possible to more easily estimate the surface roughness SS by calculation from the index value KI. Furthermore, in the first modified example, normalization can eliminate the influence of differences in the average brightness φi on the ground surface W1 (evaluation area HR), allowing for more accurate calculation and estimation of the surface roughness SS.

[0076] (6. Second alternative example) In the above-described present example and first modified example, the image evaluation device 20 sets the evaluation area HR so as to include, for example, the first and second portions present on the ground surface W1. Meanwhile, the grinding device 10 performs grinding on the ground surfaces W1 of multiple workpieces W that are sequentially supplied. In this case, the condition of the ground surface W1 of each workpiece W changes due to, for example, a processing position error or a difference in processing time between the first and second halves of a grinding lot. In this case, if the evaluation area HR is set based on the same predetermined position, deviations will occur in the evaluation area HR set for each workpiece W, which may result in a decrease in the accuracy of estimating the surface roughness SS.

[0077] Therefore, in the second modified example, the evaluation area setting unit 21 of the image evaluation device 20 is configured to set the evaluation area HR so as not to cause deviation between different workpieces W. Specifically, the evaluation area setting unit 21 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.

[0078] Then, the intersection of a line passing through the center position in the first direction and a line passing through the center position in the second direction is set as the center of the evaluation area HR, and an evaluation area HR of a predetermined size is set. This makes it possible to appropriately set the evaluation area HR even for different workpieces W. This second modification will be described in detail below.

[0079] In the second modified example, the evaluation area setting unit 21 acquires the overall image data Z output from the camera 13. As shown by the arrow in FIG. 14, the evaluation area setting unit 21 acquires the brightness φ of multiple pixels in the overall image data Z along a first direction corresponding to the circumferential direction of the workpiece W (ground surface W1), and averages the acquired multiple brightnesses φ to calculate the average brightness summation value φsa1. The evaluation area setting unit 21 then calculates the average brightness summation value φsa1 in the first direction for multiple columns along the axial direction (i.e., the second direction) of the workpiece W (ground surface W1). This makes it possible to acquire the change in the average brightness summation value φsa1 in the first direction, as shown in FIG. 15.

[0080] The evaluation area setting unit 21 sets, in the change in the acquired luminance sum average value φsa1, a point at which the luminance sum average value φsa1 increases from a small value to exceed a predetermined threshold value L1 (shown by a dashed line in FIG. 15) as a lower limit point Q11, and sets a point at which the luminance sum average value φsa1 decreases from a large value to exceed the threshold value L1 as an upper limit point Q12. Then, the evaluation area setting unit 21 sets 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. 15.

[0081] 16, the evaluation area setting unit 21 acquires the brightness φ of multiple pixels in the overall image data Z along a second direction corresponding to the axial direction of the workpiece W (ground surface W1), and calculates the average brightness sum φsa2 by averaging the acquired multiple brightness φ.The evaluation area setting unit 21 then calculates the average brightness sum φsa2 in the second direction for multiple columns along the circumferential direction (i.e., the first direction) of the workpiece W (ground surface W1).This makes it possible to acquire the change in the average brightness sum φsa2 in the second direction, as shown in FIG.

[0082] The evaluation area setting unit 21 sets, in the change in the acquired luminance sum average value φsa2, the point at which the luminance sum average value φsa2 increases from a small value to exceed a predetermined threshold value L2 (shown by a dashed line in FIG. 17) as a lower limit point Q21, and sets, as an upper limit point Q22, the point at which the luminance sum average value φsa2 decreases from a large value to exceed the threshold value L2. Then, the evaluation area setting unit 21 sets the median value between the lower limit point Q21 and the upper limit point Q22 as a median position Q23 in the second direction, as shown by a dashed line in FIG. 17.

[0083] Next, the evaluation area setting unit 21 sets the intersection of a line passing through the center position Q13 in the first direction and a line passing through the center position Q23 in the second direction as the center RC of the evaluation area HR, as shown in Fig. 18. Then, the evaluation area setting unit 21 finally determines the evaluation area HR using points RP1 and RP2 (in Fig. 18, 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 have been set in advance to determine the evaluation area HR.

[0084] As a result, for example, as shown in Fig. 19, even if the grinding surface W1 is displaced from the center of the image in the overall image data Z (Fig. 19 shows an example in which the grinding surface W1 is displaced to the left), the evaluation area setting unit 21 can appropriately determine the evaluation area HR in response to this displacement. Similarly, for example, as shown in Fig. 20, 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 (Fig. 20 shows an example in which the apex of the grinding surface W1 is displaced upward), the evaluation area setting unit 21 can appropriately determine the evaluation area HR in response to this displacement.

[0085] 21, even when the entire image data Z is the ground surface W1, the evaluation area setting unit 21 can determine the evaluation area HR by determining the lower limit point Q11 and the upper limit point Q12, and setting the intersection of a line passing through the center position Q13 in the first direction and a line passing through the center position Q13 in the second direction as the center RC of the evaluation area HR, as in the above-described case. Also, as shown in FIG. 22, even when there are two first portions, as in the above-described example, the evaluation area setting unit 21 can determine the evaluation area HR by determining the lower limit point Q11 and the upper limit point Q12, and setting the intersection of a line passing through the center position Q13 in the first direction and a line passing through the center position Q13 in the second direction as the center RC of the evaluation area HR, as in the above-described case.

[0086] (7. Other Examples) In the present example, the first modified example, and the second modified example 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.

[0087] However, the surface condition estimation system 1 does not necessarily need to include a learning processing device 30 that performs machine learning. In this case, the estimation calculation device 40 can perform, for example, 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 previously obtained from the roughness meter 2, to determine the 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, the first modified example, and the second modified example described above can be obtained.

[0088] In the present example, the first modified example, and the second modified example described above, the surface condition estimation system 1 is configured so that the grinding device 10 is equipped with 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.

[0089] 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 be equipped with 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.

[0090] 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 machined surface of a workpiece (e.g., a gear) machined by a cutting device. In this case, as in the present example, the first modified example, and the second modified example described above, the surface condition of the machined surface, for example, surface roughness, can be estimated using the index value KI acquired from the image evaluation device 20. Therefore, in this case, too, the same effects as in the present example, the first modified example, and the second modified example described above can be obtained.

[0091] Furthermore, in the above-described present example, first modified example, and second modified example, the estimation calculation device 40 is configured to output 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. In this way, by acquiring the output signal, the machine tool can appropriately change the machining conditions and accurately machine the workpiece W.

[0092] Furthermore, in the above-described present example, first modified example, and second 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 also accurately estimate the shape of the ground surface as the surface condition, for example, the shape (external line, internal line, steps, etc.) of the ground surface formed by the tool T coming into contact with the workpiece W, using the index value KI acquired from the image evaluation device 20. [Explanation of symbols]

[0093] 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 dividing unit, 23...Brightness acquisition unit, 231...Brightness average value calculation unit, 232...Brightness 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 adjustment device, 61... Training dataset acquisition unit, 61a... Index value acquisition unit, 61b ...actual roughness value acquisition unit, 62...training dataset storage unit, 63...model generation unit, 71...model storage unit, 72...index value acquisition unit, 73...estimation unit, 74...determination 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 point, Q22...upper limit point, Q23...center position, R...corrected signal, RC...center, RP1,RP2...point, SP...brightness data, SS...(estimated) surface roughness (surface condition), T...tool, W...workpiece, W1...ground surface, Z...overall image data, b...intercept, k...slope, Δφ(n)...brightness difference value, Δφg(n)...brightness difference squared sum, φ...brightness, φa...brightness average value, φi...average brightness, φsa1...brightness sum average value, φsa2...brightness sum average value

Claims

1. 1. A surface condition estimation method that is applied to a machine tool that machines a workpiece with a tool and estimates a surface condition of a machined surface of the tool and the workpiece, comprising: An evaluation area is set for an image of the entire processing surface; Dividing the evaluation region into a plurality of areas formed by adjacent pixels among a plurality of pixels forming the image; Calculating an average luminance value using the luminance values ​​of adjacent pixels in each of the areas; calculating a luminance difference value representing a difference between the luminance of each of a plurality of adjacent pixels in each of the areas and the average luminance value; 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. 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 evaluation area based on the first center position and the second center position; The surface condition estimating method according to claim 1 , further comprising: setting the evaluation area based on the center.

3. When the first direction and the second direction are perpendicular to each other, The surface condition estimating method according to claim 2 , 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.

4. The first central position and the second central position are 4. The surface condition estimating method according to claim 2, 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.

5. 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 4, wherein the surface condition of the processed surface is estimated using the index value and the trained model.

6. 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.

7. A surface condition estimation method applied to a machine tool that machines a workpiece with a tool, estimating a surface condition of the tool and a machined surface of the workpiece, comprising: Calculating a luminance average value using the luminance of each of a plurality of adjacent pixels among a plurality of pixels forming an image obtained by capturing the processed surface; calculating a luminance difference value representing a difference between the luminance of each of a plurality of adjacent pixels and the average luminance value; Calculating the square root of the sum of the squares of the brightness difference values ​​as an index value; Using the index value, a surface condition of the processed surface that is correlated with the index value is estimated; 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, a surface condition estimating method, 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.

8. determining a linear approximation of the slope with respect to the average luminance and a linear approximation of the intercept with respect to the average luminance based on the slope and the intercept of the linear approximation equation for each of the average luminances; 8. The surface condition estimating method according to claim 6, wherein the surface condition of the processed surface is estimated using the index value, the slope obtained from a linear approximation of the slope corresponding to the average luminance in the image, and the intercept obtained from a linear approximation of the intercept corresponding to the average luminance in the image.

9. The surface condition estimation method according to any one of claims 1 to 8, 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.

10. The surface state estimating method according to any one of claims 1 to 9, wherein the adjacent pixels are four or more of the pixels adjacent to each other.

11. The surface condition estimating method according to any one of claims 1 to 10, wherein the machine tool is a grinding device having a grinding wheel as the tool and grinding the workpiece with the grinding wheel.

12. The surface condition estimating method according to any one of claims 1 to 11, wherein the surface condition of the processed surface is surface roughness.

13. A surface condition estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a surface condition of a machined surface of the tool and the workpiece, comprising: an imaging device that images the processing surface and outputs the captured image; an image evaluation device having: an evaluation area setting unit that sets an evaluation area for the image; an area dividing unit that divides the evaluation area into a plurality of areas formed by a plurality of adjacent pixels among a plurality of pixels that form the image; a brightness average value calculation unit that calculates a brightness average value using the brightness of each of the adjacent pixels in each of the areas; a brightness difference value calculation unit that calculates a brightness difference value representing a difference between the average brightness value calculated by the brightness average value calculation unit in 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 the squares of the brightness difference value calculated by the brightness difference value calculation unit as an index value; an estimation calculation device having an estimation unit that estimates a surface state of the processed surface that is correlated with the index value calculated by the image evaluation device; A surface condition estimation system comprising:

14. A surface condition estimation system that is applied to a machine tool that machines a workpiece with a tool and estimates a surface condition of a machined surface of the tool and the workpiece, comprising: an imaging device that images the processing surface and outputs the captured image; an image evaluation device having: a luminance average calculation unit that calculates a luminance average value using the luminance of each of a plurality of adjacent pixels among a plurality of pixels forming the image output from the imaging device; a luminance difference value calculation unit that calculates a luminance difference value representing a difference between the luminance average value calculated by the luminance average calculation unit and the luminance of each of the plurality of adjacent pixels; and an index value calculation unit that calculates, as an index value, a square root of the sum of squares of the luminance difference value calculated by the luminance difference value calculation unit; an estimation calculation device having an estimation unit that estimates a surface state of the processed surface that is correlated with the index value calculated by the image evaluation device; Equipped with 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, a surface condition estimating system, 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.

Images