JP6255289B2 - Tool inspection method and tool inspection apparatus - Google Patents

Description

  The present invention relates to a tool inspection method and a tool inspection apparatus.

  Patent Document 1 discloses a method for detecting a shape defect of a tool by scanning a line entering a minute amount from the outer peripheral shape drawn by a ridge line with a laser focus displacement meter and detecting irregularities on the scanning line. Yes.

  Further, in Patent Document 2, as a method of inspecting the state of the cutting tool, when the cutting tool is first positioned at the inspection position, the first position and the first tip position of the cutting tool are stored, and the cutting tool is stored. When the second positioning is performed at the inspection position, the second position and the second tip position of the cutting tool are memorized, and the difference between the first tip position and the second tip position and between the first position and the second position are stored. It is disclosed that the amount of wear of a cutting tool is obtained based on the amount of displacement.

JP 9-2108030 A JP 2010-162671 A

  However, since the method described in Patent Document 1 is for inspecting a tool defect by scanning with a laser beam, the apparatus becomes large and the time required for the inspection also becomes long.

  Moreover, since the method described in Patent Document 2 needs to position the cutting tool at a specific position, the inspection accuracy is affected by the positioning accuracy of the cutting tool. In addition, the time required for positioning of the cutting tool becomes longer.

  The present invention has been made in view of the above problems, and an object thereof is to inspect a tool with high accuracy by a simple method.

The tool inspection method according to the present invention captures a target tool to be inspected, generates a grayscale image, and generates a histogram indicating the distribution of the number of pixels with respect to luminance values for the grayscale image. And a determination step of determining the state of the target tool based on the histogram , wherein the determination step calculates a total number of pixels having a predetermined brightness value or more based on the histogram. The state of the target tool is determined based on the total number, and the predetermined brightness value generates a histogram indicating the distribution of the number of pixels with respect to the brightness value for the gray scale image of the unused target tool, from the maximum brightness value The histogram is integrated toward the luminance value 0, and the luminance value when the integrated value becomes a predetermined numerical value is obtained. Characterized in that it is a constant.

A tool inspection apparatus according to the present invention images a target tool to be inspected, generates a grayscale image, and generates a histogram indicating a distribution of the number of pixels with respect to luminance values for the grayscale image. A determination unit that determines a state of the target tool based on the histogram, and the determination unit calculates a total number of pixels that are equal to or higher than a predetermined luminance value based on the histogram. The state of the target tool is determined based on the total number, and the predetermined brightness value generates a histogram indicating the distribution of the number of pixels with respect to the brightness value for the gray scale image of the unused target tool, from the maximum brightness value The histogram is integrated toward the luminance value 0, and the luminance value when the integrated value becomes a predetermined numerical value is set. It is characterized in.

  According to the present invention, since the state of the target tool is determined based on the histogram indicating the distribution of the number of pixels with respect to the luminance value, the tool can be inspected with high accuracy by a simple method.

It is a schematic block diagram of the tool inspection apparatus which concerns on embodiment of this invention. It is a flowchart which shows the procedure of the tool inspection method which concerns on embodiment of this invention. A gray scale image of an unused throw-away chip with zero use is shown. The histogram of FIG. 3A is shown. A gray scale image of a throw-away chip with 100 uses is shown. The histogram of FIG. 4A is shown. The histogram for every use count of a throw away tip is shown. 6 is a graph showing a change in a wear index E. It is a graph which shows the change of the area of the wear field extracted manually. It is a flowchart which shows the procedure of the determination method of threshold value (epsilon). The histogram of the gray scale image of an unused throw away tip is shown. The histogram of the gray scale image of an unused throw away tip is shown. The histogram of the gray scale image of the throw away chip | tip 100 times of use is shown. It is a graph which shows the change of the abrasion degree parameter | index E calculated as threshold value (epsilon) 177,165,158.

  Embodiments of the present invention will be described below with reference to the drawings.

  A tool inspection apparatus 100 according to an embodiment of the present invention is an apparatus that inspects the state of a tool. In this embodiment, a case where the inspection target is a throw-away tip of various tools will be described.

  As shown in FIG. 1, the tool inspection apparatus 100 includes a camera 1 as an image acquisition unit that acquires an image to be inspected by imaging a cutting edge of a throw-away tip mounted on a processing apparatus, and an image acquired by the camera 1. And a computer 2 that performs image processing of data and determines the state of the throw-away chip. The camera 1 is attached to a processing apparatus. The computer 2 may be provided adjacent to the processing device, or may be provided at a location away from the processing device.

  The computer 2 includes a display 21 as a display unit capable of displaying image data, and a keyboard 22 and a mouse 23 as input units capable of inputting an instruction from a user.

  Next, with reference to FIG. 2, the throw-away tip inspection method will be described in detail.

  In step 11, the cutting edge of the throw-away tip mounted on the processing apparatus is imaged using the camera 1, and an inspection target image is acquired (image acquisition process). Specifically, the flank of the throw-away tip is imaged. The wear area formed on the flank surface tends to increase in proportion to the number of times used for machining compared to the wear area formed on the rake face. Therefore, the wear degree can be appropriately evaluated by imaging the flank. The inspection target image is acquired as a color image.

  In step 12, a gray scale image is generated from the inspection target image acquired in step 11 (gray scale image generation step). Since the throw-away tip is manufactured by sintering a hard material, the surface thereof is often colored in various ways. For this reason, the method of detecting the wear of the throw-away tip using the color information of the inspection target image cannot detect the wear with high accuracy. Therefore, in this embodiment, a grayscale image is generated from the inspection target image. Instead of generating a grayscale image from the color inspection target image acquired in step 11, the grayscale image may be generated directly by the camera 11.

  In step 13, a histogram indicating the distribution of the number of pixels with respect to the luminance value is generated for the grayscale image acquired in step 12 (histogram generation step). FIG. 3A shows a gray scale image of an unused throw-away chip with zero use, FIG. 3B shows a histogram of the gray scale image, FIG. 4A shows a gray scale image of a throw-away chip with 100 uses, and FIG. 2 shows a histogram of a grayscale image. 3B and 4B, the horizontal axis represents the luminance value (density value), and the vertical axis represents the number of pixels. In this embodiment, the luminance value is represented by 256 gradations, the luminance value 0 represents black, and the luminance value 255 represents white.

  As shown in FIG. 3B, the histogram of the gray scale image of the unused throw-away chip is concentrated in the low luminance value area (left side in the figure), whereas, as shown in FIG. The histogram of the gray scale image of each throwaway chip has a large number of pixels in an area with a high luminance value (right side in the figure) and spreads. This is because, as shown in FIG. 4A, the wear area is displayed with a high luminance value (white) in the grayscale image.

  FIG. 5 shows a histogram for each use count of the throw-away chip. Also in each histogram shown in FIG. 5, the horizontal axis represents the luminance value (density value) and the vertical axis represents the number of pixels, as in FIGS. 3B and 4B. As can be seen from FIG. 5, the number of pixels having a high luminance value increases as the number of uses increases. From this, it can be said that the number of pixels having a high luminance value tends to be proportional to the area of the wear region. Therefore, the wear area can be evaluated by quantitatively measuring the number of pixels having a high luminance value.

  In step 14, the state of the throw-away chip is determined based on the histogram acquired in step 13 (determination step). Specifically, the total number of pixels equal to or higher than a predetermined luminance value is calculated, and the state of the throw-away chip is determined based on the total number. This will be described in detail below.

  A dotted line in each histogram shown in FIG. 5 indicates a luminance value (tone value) 165. The histogram with 0 use counts hardly includes pixels with a luminance value of 165 or more, but the number of pixels with a brightness value of 165 or more increases as the use count increases, while the number of pixels with a brightness value of 165 or less decreases. doing. Therefore, in the present embodiment, the total number of pixels having a luminance value of 165 or more is calculated, and the state of the throw-away chip is determined based on the total number.

  More specifically, the area of the high brightness value region of the histogram h (x) (x: brightness value) is defined as the wear index E as shown in the following formula (1).

  In the formula (1), ε is a threshold value corresponding to the predetermined luminance value, and is set to 165 in the present embodiment as described above. That is, in the histogram, the area of the region having a luminance value of 165 or more and 255 or less is calculated as the wear index E. The predetermined luminance value is not limited to 165, and is arbitrarily set according to the inspection object, the inspection environment, and the like.

  Based on the obtained wear index E, the state of the throw-away tip is determined. For example, when the wear index E reaches a predetermined value, the throw-away tip is determined to have a lifetime. Further, deterioration levels 1 to 5 are set in advance according to the magnitude of the wear index E, and when the deterioration level 5 is reached, the throw-away tip is determined to have a life. Further, a map or the like in which the correlation between the wear index E and the number of times the throw-away tip is used is set in advance based on experiments and empirical rules, and the wear index E calculated by Equation (1) is Use the map to estimate the number of times of use and determine the state of the throw-away chip.

  The processes in steps 12 to 14 described above are automatically executed by software stored in the computer 2. As a result, if it is determined that the throw-away tip has reached the end of its life, a notification that prompts replacement is issued.

<Example>
Next, examples will be described.

  Each time the three throwaway chips 1, 2, 3 are used once, a histogram is generated in the manner of steps 11 to 13 described above, and the wear index E is calculated for the histogram using equation (1). As a comparative example, every time the throwaway tips 1, 2, and 3 are used 10 times, the flank of the throwaway tip blade edge is imaged, the wear area is manually extracted, and the area of the extracted wear area (pixels) Number).

  FIG. 6 is a graph showing changes in the wear index E of the throwaway tips 1, 2, 3, and FIG. 7 is a graph showing changes in the area of the wear region manually extracted from the throwaway tips 1, 2, 3. . 6 and 7, the values of the throw-away tips 1, 2, and 3 are indicated by a solid line, a broken line, and a one-dot chain line, respectively. As can be seen from FIGS. 6 and 7, the two were almost the same. From this, according to this embodiment, it has confirmed that a wear area | region could be extracted with high precision. Therefore, according to the present embodiment, it can be said that the state of the throw-away tip can be determined with high accuracy.

  Next, a method for determining the threshold value ε, which is the predetermined luminance value, will be described with reference to FIG.

  When the throw-away chip is not used, the high brightness value area should basically not exist, but a part shows a high brightness value as noise due to the illumination conditions at the time of shooting an image and the reflection of the chip. In order to reduce the influence, the threshold ε is determined based on an image of an unused throw-away chip. This will be described in detail below.

  In step 21, the cutting edge of an unused throw-away tip is imaged using the camera 1, and a reference image is acquired.

  In step 22, a grayscale image is generated from the reference image acquired in step 21.

  In step 23, as in step 13, for the grayscale image acquired in step 22, a histogram (see FIG. 9) indicating the distribution of the number of pixels with respect to the luminance value is generated.

  In step 24, the threshold ε is determined using the histogram acquired in step 23. The determination method will be described in detail below.

  First, using the following equation (2), the histogram h (x) is integrated from the maximum luminance value toward the luminance value 0 as indicated by an arrow in FIG. P in the equation (2) is an integrated area, and the luminance value when P becomes a predetermined numerical value is determined as the threshold ε. Here, the predetermined numerical value is set to 2% of the total number of pixels. The luminance value when P is 2% of the total number of pixels is calculated as 165 from Expression (2). Therefore, the threshold ε is determined to be 165. Thus, in the present embodiment, the threshold ε is determined to be 165, which is the luminance value when P is 2% of the total number of pixels.

  It was verified as follows that the value of P was set to 2% of the total number of pixels in determining the threshold value ε.

  The brightness value when P is 1%, 2%, and 3% of the total number of pixels is calculated using Equation (2), and the calculated brightness value is set as a threshold value ε and the wear index E is calculated using Equation (1). Is calculated. The used image size is 1024 × 960 pixels, and the total number of pixels is 983040. The luminance values when P is 1%, 2%, and 3% of the total number of pixels are calculated as 177, 165, and 158, respectively, using Equation (2).

  FIG. 10A and FIG. 10B show histograms of gray scale images of unused throw-away chips used for verification and throw-away chips with a usage count of 100 times. Further, FIG. 11 shows the calculation result of the wear degree index E calculated with the threshold ε as 177, 165, and 158. In FIG. 11, the wear index E having threshold values ε of 177, 165, and 158 is indicated by a solid line, a broken line, and a one-dot chain line, respectively.

  In FIG. 10A, pixels having a luminance value of 165 or higher are hardly included, whereas in FIG. 10B, pixels having a luminance value of 165 or higher are increased and pixels having a luminance value of less than 165 are decreased. From this, it can be seen that the boundary between the surface of the unused throwaway tip that is not worn and the surface of the used throwaway tip that exhibits a high luminance value is the luminance value 165. That is, it can be seen from FIGS. 10A and 10B that the threshold ε is about 165.

  Also, as shown in FIG. 11, it can be seen that even if P changes to 1%, 2%, and 3% of the total number of pixels, there is no significant effect on the time-series fluctuation of the wear index E.

  10A and 10B, the threshold ε is about 165 as described above, and 2% of the luminance values 177, 165, and 158 when P is 1%, 2%, and 3% of the total number of pixels. The brightness value 165 at the time of is the closest. Therefore, it can be seen that it is reasonable to set the value of P to 2% of the total number of pixels in determining the threshold value ε.

  As described above, by setting the integral area P to about 2% of the total number of pixels of the image, the calculation of the area of about 2%, which is the noise of the entire image, is omitted, and the luminance value is calculated from the remaining 98% of the image. Since the transition can be analyzed, the analysis accuracy of wear can be improved.

  In the present embodiment, the value of P is set to 2% of the total number of pixels in determining the threshold value ε, but the set value is not limited to 2% and is arbitrary according to the inspection object, the inspection environment, and the like. Set to

  The processes in steps 21 to 24 described above are automatically executed by software stored in the computer 2, and the threshold ε is automatically calculated. When this automatic calculation of the threshold value ε is incorporated in the flowchart shown in FIG. 2, the processes in steps 21 to 24 may be performed before the processes in steps 11 to 14. That is, the threshold value ε is determined in the processing of steps 21 to 24, and the wear-away index E is determined using the threshold value ε in step 14 to determine the state of the throwaway tip.

  According to the above embodiment, there exist the effects shown below.

  In this embodiment, since the state of the target tool is automatically determined based on a histogram indicating the distribution of the number of pixels with respect to the luminance value, the tool can be inspected with high accuracy by a simple method.

  In the past, skilled techniques were required to determine tool life. When not relying on skilled technology, tools were changed at a safe number of uses. However, according to the present embodiment, the state of the tool can be automatically determined just by photographing the cutting edge of the tool, so that a skillful technique is unnecessary and the tool can be used up to the real life. And cost can be reduced.

  Further, since the wear index E used for determining the state of the throw-away tip is calculated only by the equation (1), the calculation is very simple and can be performed at high speed.

  Further, since the present embodiment uses a histogram, the state of the throw-away tip can be determined as long as the wear area is imaged. That is, it suffices if the wear target area is included in the inspection target image, and high positioning accuracy of the target tool and the camera 1 is not required.

  The embodiment of the present invention has been described above. However, the above embodiment only shows a part of application examples of the present invention, and the technical scope of the present invention is limited to the specific configuration of the above embodiment. Absent.

1 Camera (image acquisition unit)
2 Computer 100 Tool inspection device

Claims (2)

A gray scale image generation step of imaging a target tool to be inspected and generating a gray scale image;
For the grayscale image, a histogram generation step of generating a histogram indicating the distribution of the number of pixels with respect to the luminance value;
A determination step of determining a state of the target tool based on the histogram ,
The determination step calculates the total number of pixels equal to or higher than a predetermined brightness value based on the histogram, determines the state of the target tool based on the total number,
The predetermined brightness value generates a histogram indicating the distribution of the number of pixels with respect to the brightness value for the gray scale image of the unused target tool, integrates the histogram from the maximum brightness value toward the brightness value 0, and A tool inspection method characterized in that is set to a luminance value when becomes a predetermined numerical value . A grayscale image generation unit that images a target tool to be inspected and generates a grayscale image;
For the grayscale image, a histogram generation unit that generates a histogram indicating the distribution of the number of pixels with respect to the luminance value;
A determination unit that determines the state of the target tool based on the histogram ,
The determination unit calculates the total number of pixels equal to or higher than a predetermined luminance value based on the histogram, determines the state of the target tool based on the total number,
The predetermined brightness value generates a histogram indicating the distribution of the number of pixels with respect to the brightness value for the gray scale image of the unused target tool, integrates the histogram from the maximum brightness value toward the brightness value 0, and A tool inspection apparatus characterized in that is set to a luminance value when becomes a predetermined numerical value .

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