A method for quantifying the evaluation index of an imprint formed on an object by laser marking, a method for evaluating an imprint formed on an object by laser marking, and a method for predicting the optimal laser irradiation conditions for forming an imprint on an object by laser marking.

Abstract

The present invention provides a method for quantifying an evaluation index for an imprint formed on an object by laser marking. According to one aspect of the present invention, a method is provided for quantifying an evaluation index for an imprint formed on an object by laser marking, wherein the evaluation index includes at least contrast, color unevenness, hue, and imprint density, and the method includes the steps of: calculating the absolute value of the difference between the mode of the laser-irradiated area and the mode of the non-laser-irradiated area as contrast; calculating the standard deviation of the laser-irradiated area as color unevenness; calculating the ratio of the mode of the laser-irradiated area of ​​each color to the sum of the modes of the laser-irradiated areas of each color as hue; and calculating the ratio of the area of ​​the good laser-marked area to the total area of ​​the good laser-marked area and the poor laser-marked area in the laser-irradiated area as imprint density.

Description

Cross-reference to Related Applications

[0001] This application claims the benefit of priority of Japanese Patent Application No. 2024-148384 (filing date: August 30, 2024), and the entire disclosure content thereof is incorporated herein by reference and made part of this specification.

Technical Field

[0002] The present invention relates to a method for quantifying an evaluation index of an engraving formed by laser marking on an object, a method for evaluating an engraving formed by laser marking on an object, and a method for predicting optimal laser irradiation conditions for forming an engraving by laser marking on an object.

Background Art

[0003] In recent years, in the field of market product management and the like, various codes and symbols such as barcodes and two-dimensional codes are being used. With the spread of traceability, optical information reading devices called barcode readers or code readers are installed in factories, logistics bases, etc., and identification codes, symbols, etc. are printed or engraved on products (product coding), and systems for reading the information of this product coding with optical information reading devices are adopted in many industries.

[0004] Laser marking is used as a processing technology for irradiating an object (product) such as metal or resin with laser light and changing the surface state to perform engraving (labeling). Compared with printing and the like, laser marking has the feature that the engraved characters, logos, codes, etc. are difficult to disappear because it directly changes the object. In addition, it has good visibility, is easy to correspond with QR codes (registered trademarks) and serial numbers from the perspective of traceability management, is not easily limited in material because it can also correspond to metal, resin, and glass, and has advantages such as stable quality because it is processed by automatic control.

[0005] However, even if a user visually determines that there are no problems with the characters or designs (code printing) engraved by laser marking, it may be difficult for a reader (such as a QR code reader or barcode reader) to read them. Furthermore, even if there are multiple code prints and they appear so similar that a user can visually determine which one to choose without issue, some barcode readers may only be able to reliably read a specific code print.

[0006] To address these issues, a technology has been developed that improves the reading stability of the reading device that reads laser-processed symbols by adjusting contrast and other factors using a computer (see, for example, Patent Document 1). In addition, a technology has been developed that calculates the difference in chromaticity between the laser-marked area and the nearby unprinted area (unprocessed area) as a color difference ΔE, and determines the clarity of the print based on the magnitude of the color difference ΔE (see, for example, Patent Document 2). [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Patent No. 5635917 [Patent Document 2] Japanese Patent Publication No. 7290000 [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] However, the evaluation metrics for markings formed on objects by laser marking have not yet been quantified.

[0009] This invention was made to solve the above problems. Specifically, it aims to provide a method for quantifying the evaluation index of an imprint formed on an object by laser marking. It also provides a method for evaluating an imprint formed on an object by laser marking, and a method for predicting the optimal laser irradiation conditions for forming an imprint on an object by laser marking. [Means for solving the problem]

[0010] [1] A method for quantifying an evaluation index for an imprint formed on an object by laser marking, wherein the evaluation index includes at least contrast, color unevenness, hue, and imprint density, and comprises the steps of: capturing a region of the object including the imprint with an imaging device and obtaining a color image; converting the original image to a grayscale image and obtaining the pixel values ​​of the grayscale image; obtaining a first pixel value distribution graph from the pixel values ​​of the grayscale image; calculating the contrast as the absolute value of the difference between the mode of the pixel values ​​in the laser-irradiated region, which is a region of the object irradiated with a laser, and the mode of the pixel values ​​in the non-laser-irradiated region, which is a region of the object irradiated with a laser, based on the first pixel value distribution graph; and calculating the color unevenness as the standard deviation of the pixel values ​​in the laser-irradiated region based on the first pixel value distribution graph. A method comprising: a calculation step; a step of obtaining pixel values ​​of red, green, and blue from the original image; a step of obtaining a second pixel value distribution graph for each color from the pixel values ​​of each color obtained from the original image; and a step of calculating the color tone as the ratio of the mode of the pixel values ​​of the laser-irradiated area of ​​each color to the sum of the modes of the pixel values ​​of the laser-irradiated area of ​​each color based on each of the second pixel value distribution graphs; and a step of extracting an image of at least a portion of the laser-irradiated area from the grayscale image and obtaining the pixel values ​​of the extracted image; a step of obtaining a third pixel value distribution graph from the pixel values ​​of the laser-irradiated area; and a step of calculating the marking density as the ratio of the area of ​​the laser-marked area to the total area of ​​the laser-marked area and the laser-marked area in the laser-irradiated area based on the third pixel value distribution graph.

[0011] [2] The method according to [1] above, wherein the marking is an information code, a letter, a number, a symbol, a picture, a figure, or a combination thereof.

[0012] [3] The method according to [2] above, wherein the information code is a one-dimensional code or a two-dimensional code.

[0013] [4] The method according to any one of the above [1] to [3], wherein the contrast is 90 or more, the color unevenness is 10 or less, the hue is 0.30 or more and 0.40 or less, and the engraving density is 0.60 or more.

[0014] [5] The method according to any one of [1] to [4] above, wherein the step of calculating the contrast as the absolute value of the difference between the mode of the pixel values ​​in the laser-irradiated area and the mode of the pixel values ​​in the non-laser-irradiated area includes a distribution curve acquisition step of obtaining a first distribution curve in the laser-irradiated area and a second distribution curve in the non-laser-irradiated area by performing normal distribution fitting on the laser-irradiated area and the non-laser-irradiated area of ​​the first pixel value distribution graph, respectively; a mode extraction step of extracting the mode of the pixel values ​​in the laser-irradiated area based on the first distribution curve and the mode of the pixel values ​​in the non-laser-irradiated area based on the second distribution curve, respectively; and a mode difference calculation step of calculating the absolute value of the difference between the respective modes.

[0015] [6] The method according to [5], wherein at least one of the first distribution curve acquisition step, the mode extraction step, and the mode difference calculation step is performed by a computer-executable program.

[0016] [7] The method according to any one of the above [1] to [6], wherein the step of calculating the standard deviation of the laser irradiation area as the color unevenness is performed by a computer-executable program.

[0017] [8] The method according to any one of [1] to [7] above, wherein the step of calculating the color tone is the ratio of the mode of the pixel values ​​in the laser-irradiated area of ​​each color to the sum of the modes of the pixel values ​​in the laser-irradiated area of ​​each color, the step of extracting the mode of the pixel values ​​in the laser-irradiated area of ​​each color based on the second pixel value distribution graph of each color, and the step of calculating the mode ratio of the mode of the pixel values ​​in the laser-irradiated area of ​​each color to the sum of the modes of each color.

[0018] [9] The method according to [8] above, wherein at least one of the steps of obtaining the second distribution curve, extracting the mode, and calculating the mode percentage is performed by a computer-executable program.

[0019]

[10] The method according to any one of [1] to [9] above, wherein the step of calculating the marking density as the ratio of the area of ​​the laser marking good region to the total area of ​​the laser marking good region and the laser marking poor region in the laser irradiation region includes a third distribution curve acquisition step of performing a normal distribution fitting on the third pixel value distribution graph to obtain a third distribution curve, and an area ratio calculation step of calculating the area of ​​the laser marking good region and the area of ​​the laser marking poor region, respectively, based on the third distribution curve, and determining the ratio of the total area of ​​the laser marking good region to the total area of ​​the laser marking good region and the laser marking poor region.

[0020]

[11] The method according to

[10] , wherein at least one of the third distribution curve acquisition step and the area ratio calculation step is performed by a computer executable program.

[0021]

[12] A method for evaluating an imprint formed on an object by laser marking using the contrast, color unevenness, color tone, and imprint density quantified by the method described in any one of the above items [1] to

[11] .

[0022]

[13] A method for predicting optimal laser irradiation conditions for forming an engraving on an object by laser marking, comprising inputting at least first information regarding the laser irradiation conditions and second information regarding the contrast, color unevenness, color tone, and engraving density defined in claim 1 associated with the first information as input data, and setting the optimal laser irradiation conditions as output data, and predicting the optimal laser irradiation conditions that satisfy the input second information by inputting at least the second information into a trained model generated by machine learning.

[0023]

[14] The method according to

[13] above, wherein the input data further includes third information regarding the material of the object associated with the first information and the second information, and predicting the optimal laser irradiation conditions that satisfy the input second information and third information by inputting the second information and the third information into the trained model. [Advantages of the Invention]

[0024] According to one aspect of the present invention, it is possible to quantify an evaluation index of an engraving formed on an object by laser marking. Further, according to another aspect of the present invention, it is possible to evaluate an engraving formed on an object by laser marking. Furthermore, according to another aspect of the present invention, it is possible to predict optimal laser irradiation conditions for forming an engraving on an object by laser marking. [Brief Description of the Drawings]

[0025] [Figure 1] FIG. 1A is a system for quantifying an evaluation index of an engraving formed on an object by laser marking according to an embodiment, and FIG. 1B is a plan view of an object having an engraving used in the system shown in FIG. 1A. [Figure 2] FIG. 2 is a diagram showing the configuration of an image processing apparatus according to an embodiment. [Figure 3]Figure 3 is a flowchart of the contrast calculation step according to the embodiment. [Figure 4] Figure 4 is an illustrative diagram of the first pixel value distribution graph. [Figure 5] Figure 5 is an illustrative diagram of the first distribution curve obtained by performing a normal distribution fitting on the laser irradiation region of the first pixel value distribution graph. [Figure 6] Figure 6 is an illustrative diagram of the second distribution curve obtained by performing a normal distribution fitting on the laser-unirradiated region of the first pixel value distribution graph. [Figure 7] Figure 7 is a flowchart of the color calculation step according to the embodiment. [Figure 8] Figure 8 is an illustrative diagram of the second pixel value distribution graph. [Figure 9] Figure 9 is a flowchart of the marking density calculation step according to the embodiment. [Figure 10] Figures 10A and 10B are illustrative diagrams of the third distribution curve obtained by performing a normal distribution fitting on the third pixel value distribution graph. [Figure 11] Figure 11 shows the configuration of the optimal laser irradiation condition prediction device according to the embodiment. [Figure 12] Figure 12A is a photograph showing the marking state of sample 1, Figure 12B is a photograph showing the marking state of sample 2, Figure 12C is a photograph showing the marking state of sample 3, and Figure 12D is a photograph showing the marking state of sample 4. [Figure 13] Figure 13 is a graph showing the distribution of the first pixel value for sample 1. [Figure 14] Figure 14 is a graph of the first distribution curve obtained by performing a normal distribution fitting on the laser irradiation region of the first pixel value distribution graph shown in Figure 13. [Figure 15] Figure 15 is a graph of the second distribution curve obtained by performing a normal distribution fitting on the laser-unirradiated region of the first pixel value distribution graph shown in Figure 13. [Figure 16]Figure 16 is a graph showing the distribution of the first pixel value for sample 2. [Figure 17] Figure 17 is a graph of the first distribution curve obtained by performing a normal distribution fitting on the laser irradiation region of the first pixel value distribution graph shown in Figure 16. [Figure 18] Figure 18 is a graph of the second distribution curve obtained by performing a normal distribution fitting on the laser-unirradiated region of the first pixel value distribution graph shown in Figure 16. [Figure 19] Figure 19 shows the distribution graph of the second pixel values ​​in red, green, and blue for Sample 1. [Figure 20] Figure 20 shows the distribution graph of the second pixel values ​​in red, green, and blue for Sample 2. [Figure 21] Figure 21 is an image extracted from the image of Sample 1, showing a portion of the laser-irradiated area. [Figure 22] Figure 22 is an image extracted from the image of Sample 2, showing a portion of the laser-irradiated area. [Figure 23] Figure 23 shows the third distribution curve obtained by performing a normal distribution fitting on the third pixel value distribution graph for sample 1. [Figure 24] Figure 24 shows the third distribution curve obtained by performing a normal distribution fitting on the third pixel value distribution graph related to sample 2. [Modes for carrying out the invention]

[0026] The following describes a method for quantifying an evaluation index of an imprint formed on an object by laser marking according to an embodiment of the present invention, a method for evaluating an imprint formed on an object by laser marking, and a method for predicting the optimal laser irradiation conditions for forming an imprint on an object by laser marking. Figure 1A is a system for quantifying an evaluation index of an imprint formed on an object by laser marking according to this embodiment, Figure 1B is a plan view of an object having an imprint used in the system shown in Figure 1A, Figure 2 is a diagram showing the configuration of an image processing apparatus according to this embodiment, and Figure 3 is a flowchart of the contrast calculation step according to this embodiment. Figure 4 is a first pixel value distribution graph. Figure 5 is an image diagram of the first distribution curve obtained by performing normal distribution fitting on the laser irradiation area of ​​the first pixel value distribution graph, and Figure 6 is an image diagram of the second distribution curve obtained by performing normal distribution fitting on the laser non-irradiated area of ​​the first pixel value distribution graph. Figure 7 is a flowchart of the color calculation step according to this embodiment, and Figure 8 is an image diagram of the second pixel value distribution graph. Figure 9 is a flowchart of the marking density calculation step according to this embodiment, Figures 10A and 10B are illustrative diagrams of the third distribution curve obtained by performing normal distribution fitting on the third pixel value distribution graph, and Figure 11 is a diagram showing the configuration of the optimal laser irradiation condition prediction device according to this embodiment.

[0027] <<Method for quantifying evaluation indicators of markings formed by laser marking>> First, an object 10 having an engraving 11 formed by laser marking is prepared, as shown in Figures 1A and 1B. In this specification, "engraving" is a concept that includes information codes, letters, numbers, symbols, pictures, figures, or combinations thereof. The information code may be one that can be read by an information reading device such as a barcode reader, for example, a one-dimensional code such as a one-dimensional barcode, or a two-dimensional code such as a QR code (registered trademark) or a data matrix. The engraving 11 shown in Figure 1B is a two-dimensional code.

[0028] The material of the object 10 is not particularly limited, but examples include resin materials, metal materials, glass, etc. If the material of the object 10 is a resin material, the resin is not particularly limited. Resin mixtures, which are blends of multiple resins, are also included in the above resin. Furthermore, resin materials to which desired properties have been imparted are also included, which are obtained by adding additives such as inorganic fillers such as glass fibers and carbon fibers, nucleating agents, pigments such as carbon black and inorganic calcined pigments, antioxidants, stabilizers, plasticizers, lubricants, mold release agents, and flame retardants to the resin.

[0029] Thermoplastic resins can be used as the resin material. Examples of thermoplastic resins include polyolefin resins, polyester resins, polyacetal resins, polyphenylene sulfide resins, and polyamide resins.

[0030] The evaluation metrics for such markings 11 include at least contrast, color uniformity, hue, and marking density. In addition to contrast, color uniformity, hue, and marking density, the evaluation metrics may also include other metrics.

[0031] <Contrast> The contrast is determined by the contrast calculation step (steps S1-1 to S1-4 shown in Figure 3). In the contrast calculation step, first, an original image including the markings on the laser-marked object is acquired by the imaging device 20 (see Figure 2) (step S1-1). The original image is a color image. Then, the acquired original image is processed by the image processing device 30 to convert it into, for example, a 256-level grayscale image, and the pixel values ​​of the grayscale image are acquired (step S1-2). When the grayscale image has 256 levels, the pixel values ​​are in the range of 0 to 255. In this case, black is 0 and white is 255.

[0032] The image processing apparatus 30 shown in Figure 2 includes, for example, a processing unit 31, a storage unit 32, an input unit 33, an output unit 34, and a communication unit 35. The processing unit 31 includes means for performing arithmetic processing such as a CPU. The processing unit 31 processes the original image obtained by the imaging device 20. The storage unit 32 includes storage means such as a semiconductor memory or a memory card. The storage unit 32 stores a program that can be executed by a computer and is electrically connected to the processing unit 31 in an accessible manner. The input unit 33 includes means for inputting information. The input unit 33 includes, for example, a keyboard, touch panel, buttons, etc., for receiving input from an administrator. The output unit 34 includes means for outputting processing results. The output unit 34 includes, for example, a display. The communication unit 35 is configured to include an interface for communicating with external devices via an information communication network such as the Internet or a LAN. Communication by the communication unit 35 can be wired or wireless.

[0033] Next, after obtaining pixel values ​​from the grayscale image, a first pixel value distribution graph is obtained from the pixel values ​​(step S1-3, see Figure 4). In this specification, a "pixel value distribution graph" is a graph that shows the relationship when the horizontal axis is pixel value and the vertical axis is the number of pixels (frequency). The pixel value distribution graph may be a histogram or a scatter plot.

[0034] Subsequently, based on the first pixel value distribution graph, the difference between the most frequent pixel value in the laser-irradiated region (the area of ​​the object 10 irradiated with a laser) and the most frequent pixel value in the non-laser-irradiated region (the area not irradiated with a laser) is calculated, and the absolute value of this difference in most frequent values ​​is defined as the contrast (Step S1-4). Here, in the non-laser-irradiated region, the frequency of pixel values ​​based on the color of the object increases, and in the laser-irradiated region, the frequency of pixel values ​​based on the markings increases, so different peaks appear in the first pixel value distribution graph. The tops of these peaks become the most frequent values.

[0035] Step S1-4 may be performed by the following steps. First, a normal distribution fitting is performed on the laser-irradiated region and the non-laser-irradiated region of the first pixel value distribution graph, respectively, to obtain the first distribution curve (see Figure 5), which is the distribution curve of the laser-irradiated region, and the second distribution curve (see Figure 6), which is the distribution curve of the non-laser-irradiated region (distribution curve acquisition step). Next, the mode of the peak in the laser-irradiated region is extracted from the first distribution curve and the mode of the peak in the non-laser-irradiated region is extracted from the second distribution curve (mode extraction step). Then, the absolute value of the difference between the respective modes is calculated (mode difference calculation step).

[0036] The contrast calculation step may be performed manually, but it is preferable to automate at least some of the steps (e.g., steps S1-4) by executing a computer-executable program (e.g., a program written in a programming language such as Python®) or image processing software (e.g., Image J) stored in the memory unit 32. By automating at least some of the steps in the contrast calculation step, the time required for analyzing a large number of conditions can be reduced.

[0037] When steps S1-4 are performed by a computer-executable program or image processing software, it is preferable to perform at least one of the steps, for example, the first distribution curve acquisition step, the mode extraction step, and the mode difference calculation step, by the program, and it is more preferable to perform all steps by the program or image processing software.

[0038] For example, when using a program written in Python, running this program automatically performs the acquisition of a grayscale image, the acquisition of the first pixel value distribution graph, the acquisition of the first and second distribution curves (performing normal distribution fitting), the extraction of the mode, and the calculation of the mode difference, thus automating steps S1-2 to S1-4. Alternatively, when using Image J, this software can automatically acquire a grayscale image, the acquisition of the first pixel value distribution graph, and the acquisition of the first and second distribution curves (performing normal distribution fitting), but the extraction of the mode and the calculation of the mode difference are performed using other software (for example, Microsoft Excel®).

[0039] The contrast is preferably 90 or higher, from the viewpoint of obtaining good visibility when viewed by the human eye and good readability when read by a barcode reader or other reading device. The lower limit of the contrast is more preferably 95 or higher, or 97 or higher.

[0040] <Uneven coloring> Color unevenness is determined by the color unevenness calculation step. In the color unevenness calculation step, the standard deviation of the pixel values ​​in the laser-irradiated area is determined based on the first distribution curve, and the standard deviation is taken as the color unevenness (step S2-1). The steps up to obtaining the first distribution curve are the same as steps S1-1 to S1-3.

[0041] The color unevenness calculation step may be performed manually, but from the viewpoint of saving time, it is preferable to automate step S2-1 by executing it using a computer-executable program (for example, a program written in Python, etc.) stored in the memory unit 32.

[0042] From the viewpoint of obtaining good visibility and good readability, the color unevenness is preferably 10 or less. More preferably, the upper limit of the color unevenness is 8 or less, or 6 or less.

[0043] <Color> The color is determined by a color calculation step (steps S3-1 to S3-3 shown in Figure 7). First, pixel values ​​for each color, such as red, green, and blue, are obtained from the original color image (step S3-1), for example, 256 levels of color (step S3-1). The step of obtaining the original image is the same as in step S1-1. Next, a second pixel value distribution graph is obtained for each color from the pixel values ​​of each color obtained from the original image (step S3-2, see Figure 8). That is, three types of second pixel value distribution graphs are obtained: a second pixel value distribution graph for red, a second pixel value distribution graph for green, and a second pixel value distribution graph for blue.

[0044] Then, the ratio of the mode of the laser-irradiated area in each second pixel value distribution graph (the mode of the pixel value of the laser-irradiated area in the red second pixel value distribution graph, the mode of the pixel value of the laser-irradiated area in the green second pixel value distribution graph, and the mode of the pixel value of the laser-irradiated area in the blue second pixel value distribution graph) to the sum of the modes of the pixel values ​​of the laser-irradiated area in each second pixel value distribution graph (the sum of the mode of the pixel value of the laser-irradiated area in the red second pixel value distribution graph, the mode of the pixel value of the laser-irradiated area in the green second pixel value distribution graph, and the mode of the pixel value of the laser-irradiated area in the blue second pixel value distribution graph) is calculated, and this ratio is defined as the color (Step S3-3). There are three types of colors: red, green, and blue.

[0045] Step S3-3 may be performed by the following steps: First, extract the mode of the peak in the laser-irradiated area in the second pixel value distribution graph for each color (mode extraction step). Then, calculate the ratio of the mode of the pixel values ​​in the laser-irradiated area for each color to the sum of the modes for each color (mode ratio calculation step).

[0046] The color calculation step may be performed manually, but from the viewpoint of saving time, it is preferable to automate at least some of the steps, for example, steps S3-3, by executing them using a computer-executable program (for example, a program written in Python, etc.) or image processing software (for example, ImageJ, etc.) stored in the memory unit 32.

[0047] When step S3-3 is performed by a computer-executable program, it is preferable, for example, to perform at least one of the steps, such as the mode extraction step and the mode ratio calculation step, by the program, and more preferably to perform all steps by the program or image processing software.

[0048] For example, when using a program written in Python, running this program automatically performs the acquisition of pixel values, the acquisition of a second pixel value distribution graph, the extraction of the mode, and the calculation of the mode's proportion, thus automating steps S3-1 to S3-3. Alternatively, when using Image J, this software can automatically acquire pixel values ​​and the acquisition of a second pixel value distribution graph, but the extraction of the mode and the calculation of the mode's proportion are performed using other software (for example, Microsoft Excel®).

[0049] From the viewpoint of obtaining white laser marking and good visibility, the value of each color is preferably 0.30 to 0.40. The lower limit of each color is preferably 0.31 or 0.32, and the upper limit is preferably 0.39 or 0.38.

[0050] <Engraving density> The engraving density is determined by the engraving density calculation step (steps S4-1 to S4-3 shown in Figure 9). First, at least a portion of the image of the laser-irradiated area is extracted from the grayscale image and the pixel values ​​are obtained (step S4-1). The steps up to obtaining the grayscale image are the same as in steps S1-1 and S1-2.

[0051] Subsequently, a third pixel value distribution graph is obtained from the pixel values ​​of the laser-irradiated area (step S4-2).

[0052] After obtaining the third pixel value distribution graph, the ratio of the good laser marking area to the total area of ​​the good laser marking area and the bad laser marking area in the laser irradiation region is determined based on the third pixel value distribution graph, and this ratio is defined as the marking density (Step S4-3). Specifically, the marking density is calculated by the following equation (1). Engraving density = Area of ​​good laser marking region / (Area of ​​good laser marking region + Area of ​​poor laser marking region) ...Equation (1)

[0053] Even within a laser-irradiated area, there may be areas that are not laser-marked and / or insufficiently laser-marked (laser-marked areas) in addition to areas that are sufficiently laser-marked (good laser-marked areas). Therefore, the marking density is determined to evaluate the extent to which good laser-marked areas exist within the laser-irradiated area.

[0054] Step S4-3 may be performed by the following steps. First, a third distribution curve, as shown in Figures 10A and 10B, is obtained by performing a normal distribution fitting on the third pixel value distribution graph (third distribution curve acquisition step). Next, the area of ​​the good laser marking region and the area of ​​the bad laser marking region are calculated, and the ratio of the total area of ​​the good laser marking region to the total area of ​​the good laser marking region and the bad laser marking region is determined (area ratio calculation step).

[0055] The determination of good and bad laser marking regions is as follows: As shown in Figure 10A, if the tail of the peak in the third distribution curve is not broad, then there are no bad laser marking regions. As shown in Figure 10B, if the tail of the peak in the third distribution curve is broad, a predetermined lower tail probability (e.g., the lower 1% point) is used as the threshold. The region including the peak top above the threshold (in Figure 10B, the region with pixel values ​​greater than or equal to the threshold) is considered a good laser marking region, and the region not including the peak top below the threshold (in Figure 10B, the region with pixel values ​​less than the threshold) is considered a bad laser marking region.

[0056] The marking density calculation step may be performed manually, but from the viewpoint of saving time, it is preferable to automate at least some of the steps (for example, steps S4-3) by performing them using a computer-executable program (for example, a program written in Python, etc.) or image processing software (for example, ImageJ, etc.).

[0057] When step S4-3 is performed by a computer-executable program or image processing software, it is preferable, for example, to perform at least one of the steps of the third distribution curve acquisition step and the area ratio calculation step by program, and more preferably to perform all steps by program.

[0058] For example, when using a program written in Python, running this program automatically performs the acquisition of a grayscale image, the acquisition of a third pixel value distribution graph, the acquisition of a third distribution curve (performing normal distribution fitting), the calculation of area, and the calculation of area ratio, thus automating steps S4-1 to S4-3. Alternatively, when using Image J, this software can automatically acquire a grayscale image, the acquisition of a third pixel value distribution graph, and the acquisition of a third distribution curve (performing normal distribution fitting), but the calculation of area and area ratio is performed using other software (for example, Microsoft Excel®).

[0059] From the viewpoint of obtaining good visibility, the engraving density is preferably 0.60 or higher. The lower limit of the engraving density is preferably 0.70 or higher, 0.80 or higher, or 0.90 or higher.

[0060] According to this embodiment, contrast, color unevenness, color tone, and marking density are used as evaluation indicators for markings formed on an object by laser marking, and these are expressed as numerical values, so that the evaluation indicators for markings formed on an object by laser marking can be quantified.

[0061] Engravings with a contrast of 90 or higher, color unevenness of 10 or less, color tone between 0.30 and 0.40, and an engraving density of 0.60 or higher can be read and seen by barcode readers. To obtain such uniform engravings by laser marking, the laser irradiation conditions are important. For example, regarding the printing speed (scanning speed), which is the speed at which the marking head engraves characters and symbols, the laser light spreads evenly across the area that makes up the line, so if the printing speed doubles, the laser irradiation area also doubles. In other words, if the printing speed is too fast, the power density received by the printing surface is halved, so it is preferable not to make the printing speed too fast. For this reason, the printing speed is preferably, for example, between 1000 mm / sec and 2000 mm / sec. Furthermore, regarding the frequency, which is the number of waves repeated per second (number of points irradiated), it is preferable to set it higher, as a higher frequency increases the density of laser dots and makes the marked spot appear smoother. Therefore, the laser frequency is preferably, for example, between 20 kHz and 40 kHz.

[0062] <<Method for evaluating markings formed by laser marking>> The markings formed on an object by laser marking are evaluated using the contrast, color unevenness, hue, and marking density quantified by the above evaluation index quantification method. For example, if the contrast of the marking is 90 or higher, the color unevenness is 10 or lower, the hue is between 0.30 and 0.40, and the marking density is 0.60 or higher, the marking can be considered to be formed evenly on the object, and the marking can be evaluated as "good". On the other hand, if at least one of the contrast, color unevenness, hue, or marking density of the marking does not meet the above range, the marking can be considered to be uneven on the object, and can be judged as "bad". This makes it easy to determine whether the marking is good or bad.

[0063] <<Method for predicting laser irradiation conditions>> Since contrast, color unevenness, hue, and engraving density change depending on the laser irradiation conditions when irradiating the object with a laser, the optimal laser irradiation conditions are predicted by the optimal laser irradiation condition prediction device 40 shown in Figure 11.

[0064] The optimal laser irradiation condition prediction device 40 can be configured to include a control unit 41, a storage unit 42 storing a learned model 42A, an input unit 43, and an output unit 44. The control unit 41 includes means for performing calculation processing such as a CPU. The control unit 41 may have the function of storing at least first information and second information, described later, which are input, in the storage unit 42, and the function of generating a learned model 42A using at least the first information and second information and storing it in the storage unit 42. The storage unit 42 includes storage means such as a semiconductor memory or a memory card. The storage unit 42 is connected to the processing unit 41 in an accessible manner and stores learned parameters and other information necessary for processing in the optimal laser irradiation condition prediction device 40. The input unit 43 includes means for inputting information including input data. The input unit 43 includes, for example, a keyboard, touch panel, buttons, etc., for receiving input from an administrator. The output unit 44 includes means for outputting processing results. The output unit 36 ​​includes, for example, a display for outputting processing results.

[0065] In the optimal laser irradiation condition prediction device 40, the optimal laser irradiation conditions that satisfy the input second information are predicted by inputting the second information into a trained model 42A generated by machine learning, which takes as input data at least first information relating to the laser irradiation conditions and second information relating to the contrast, color unevenness, color tone, and engraving density associated with the first information, and outputs the optimal laser irradiation conditions. Alternatively, the input data may further include third information relating to the material of the object associated with the first and second information, and the optimal laser irradiation conditions that satisfy the input second and third information may be predicted by inputting the second and third information into the trained model.

[0066] The first piece of information includes at least one of the following: scan speed, laser frequency, focal point, number of scans, line spacing, and laser irradiation energy, but it is preferable to use all of these.

[0067] Third-party information includes the type of material that makes up the object (for example, the type of resin if the object is made of resin), the type and amount of additives added to the material, etc. If the object is made of resin, examples of additives include inorganic fillers such as glass fibers and carbon fibers, nucleating agents, pigments such as carbon black and inorganic calcined pigments, antioxidants, stabilizers, plasticizers, lubricants, mold release agents, and flame retardants.

[0068] The machine learning method used to generate the trained model 42A is not particularly limited as long as it performs supervised learning, but for example, neural networks and deep learning can be used.

[0069] According to this embodiment, by inputting the second information into a pre-trained model generated by machine learning, which takes at least first information regarding laser irradiation conditions and second information regarding contrast, color unevenness, hue, and engraving density associated with the first information as input data and outputs the optimal laser irradiation conditions, the optimal laser irradiation conditions that satisfy the input second information are predicted, making it easy to predict the optimal laser irradiation conditions. [Examples]

[0070] To illustrate the present invention in detail, examples are given below, but the present invention is not limited to these descriptions. Figure 12A is a photograph showing the marking state of sample 1, Figure 12B is a photograph showing the marking state of sample 2, Figure 12C is a photograph showing the marking state of sample 3, and Figure 12D is a photograph showing the marking state of sample 4. Figure 13 is a first pixel value distribution graph for sample 1, Figure 14 is a graph of the first distribution curve obtained by performing normal distribution fitting on the laser-irradiated region of the first pixel value distribution graph shown in Figure 13, and Figure 15 is a graph of the second distribution curve obtained by performing normal distribution fitting on the laser-non-irradiated region of the first pixel value distribution graph shown in Figure 13. Figure 16 is the first pixel value distribution graph for sample 2, Figure 17 is the first distribution curve obtained by performing a normal distribution fitting on the laser-irradiated region of the first pixel value distribution graph shown in Figure 16, and Figure 18 is the second distribution curve obtained by performing a normal distribution fitting on the non-laser-irradiated region of the pixel value distribution graph for sample 2. Figure 19 is the second pixel value distribution graph in red, green, and blue for sample 1, and Figure 20 is the second pixel value distribution graph in red, green, and blue for sample 2. Figure 21 is an image extracted from the image of sample 1 showing a portion of the laser-irradiated region, and Figure 22 is an image extracted from the image of sample 2 showing a portion of the laser-irradiated region. Figure 23 is the third distribution curve obtained by performing a normal distribution fitting on the third pixel value distribution graph for sample 1, and Figure 24 is the third distribution curve obtained by performing a normal distribution fitting on the third pixel value distribution graph for sample 2.

[0071] <Sample preparation> First, four black test pieces made of flat polyacetal resin, measuring 100 mm in length, 100 mm in width, and 3 mm in thickness, were prepared as the objects to be laser-marked. Next, the surface of each test piece was irradiated with a laser under different processing conditions shown in Table 1 to imprint a data matrix onto the test piece, yielding samples 1 to 4 with the markings shown in Figures 12A to 12D. Sample 1 was marked under processing condition A, sample 2 under processing condition B, sample 3 under processing condition C, and sample 4 under processing condition D.

[0072] [Table 1]

[0073] <Contrast Calculation> In samples 1-4, contrast was determined. Specifically, images containing the two-dimensional codes of samples 1-4 were first captured using an imaging device (Canon LiDE210 manufactured by Canon Inc.) to obtain the original images. The original images were color images. Then, the acquired original images were converted to 256-level grayscale images, and pixel values ​​were obtained from the grayscale images. The pixel values ​​were set from 0 to 255, with black being 0 and white being 255.

[0074] After obtaining pixel values ​​from a grayscale image, a first pixel value distribution graph was obtained from the pixel values ​​(Figures 13 and 16). After obtaining the first pixel value distribution graph, a first distribution curve and a second distribution curve were obtained by performing normal distribution fitting on the laser-irradiated and non-laser-irradiated regions of the first pixel value distribution graph, respectively (Figures 14, 15, 17, and 18). Next, the mode of pixel values ​​in the laser-irradiated region was extracted based on the first distribution curve, and the mode of pixel values ​​in the non-laser-irradiated region was extracted based on the second distribution curve. Then, the absolute value of the difference between the respective modes was calculated, and this absolute value of the difference between the modes was defined as the contrast.

[0075] The acquisition of the grayscale image, the acquisition of the first pixel value distribution graph, the acquisition of the first and second distribution curves (performing normal distribution fitting), the extraction of the mode, and the calculation of the mode difference were all performed using a program written in Python.

[0076] <Color unevenness calculation> In samples 1-4, color unevenness was determined. Specifically, the standard deviation of pixel values ​​was calculated from the first distribution curve of the laser-irradiated area obtained in the contrast column, and this was defined as color unevenness. The standard deviation was calculated using a program written in Python.

[0077] <Color Calculation> In Samples 1-4, we determined the color. Specifically, we first obtained 256-level pixel values ​​for red, green, and blue from the original color image obtained in the contrast column, and then obtained a distribution graph of the second pixel value for each color from the pixel values ​​of each color (Figures 19 and 20).

[0078] After obtaining the second pixel value distribution graph, the mode of the peak in the laser-irradiated area for each color was extracted from the second pixel value distribution graph for each color. Then, the ratio of the mode of the pixel value in the laser-irradiated area for each color to the sum of the modes for each color was calculated, and this ratio was defined as the color.

[0079] The acquisition of the second pixel value distribution graph, extraction of the mode, and calculation of the mode percentage were performed using a program written in Python.

[0080] <Engraving density> In samples 1-4, the engraving density was determined. Specifically, as shown in Figures 21 and 22, at least a portion of the image of the laser-irradiated area was extracted from the grayscale image obtained in the contrast column, and the pixel values ​​were obtained. Subsequently, a third pixel value distribution graph was obtained from the pixel values ​​of the laser-irradiated area.

[0081] After obtaining the third pixel value distribution graph, a third distribution curve was obtained by performing a normal distribution fitting on the third pixel value distribution graph. Next, the area of ​​the laser marking good region and the laser marking bad region were determined based on the third distribution curve. Here, in Sample 1, as shown in Figure 23, the tail of the peak in the third distribution curve was not broad, so it was assumed that there was no laser marking bad region. Similarly, in Sample 4, there was no laser marking bad region. In Sample 2, as shown in Figure 24, the tail of the peak in the third distribution curve was broad, so the lower 1% point of the third distribution curve was used as a threshold, and pixel values ​​below this threshold were considered the laser marking bad region, and pixel values ​​above this threshold were considered the laser marking good region. In Sample 3, similar to Sample 2, the tail of the peak in the third distribution curve was broad, so the lower 1% point of the third distribution curve was used as a threshold, and pixel values ​​below this threshold were considered the laser marking bad region, and pixel values ​​above this threshold were considered the laser marking good region.

[0082] Then, the area of ​​the separated laser-marked good area and the area of ​​the laser-marked bad area were calculated, and from equation (1) above, the ratio of the total area of ​​the laser-marked good area to the total area of ​​the laser-marked good area and laser-marked bad area was determined, and this ratio was defined as the marking density.

[0083] The acquisition of the grayscale image, the acquisition of the third pixel value distribution graph, the acquisition of the third distribution curve (performing normal distribution fitting), the calculation of the area of ​​the good and bad laser marking regions, and the calculation of the area ratio were all performed using a program written in Python.

[0084] <Evaluation results for samples 1-4> Table 2 shows the contrast, color unevenness, hue, and engraving density for samples 1-4. [Table 2]

[0085] <Readability Evaluation> The two-dimensional codes engraved on samples 1, 2, and 4 were read using a barcode reader with a judgment function (Cognex Corporation's "DM282-9D97EE") in Standard-Based Grading (SBG) verification mode, and their readability was determined. The following inspection items were performed in accordance with ISO 15416, and the imaging method was based on ISO 15416, with a fixed focal distance of approximately 30 cm. The optimal conditions for illumination angle, fixed focal distance, and readability were set on the barcode reader with the judgment function through calibration. (Inspection items) • Unused Error Correction (UEC): This is the percentage of error correction capability available for inaccurate modules. • Module (MOD) and Reflectance Margin (RM): These are grades based on the degree of variation in the reflectance of the module. • Axis Non-Uniformity (ANU): A measure of how much a module "deviates from a square," i.e., the overall aspect ratio of the symbol. • Grid non-uniformity (GNU): The worst-case distance between the center of a calculated module and the ideal position of the center of a module based on modules arranged at perfectly equal intervals. • Fixed Pattern Damage (FPD): This is the overall score for all fixed pattern components. • Left 'L' side (LLS): This is a grade based on defects on the left 'L' side of the finder pattern. • Bottom 'L' side (BLS): This is a grade based on defects on the bottom 'L' side of the finder pattern. • Left Quiet Zone (LQZ): This rating is based on defects in the quiet zone area of ​​one module located on the left side of the left 'L' side. • Bottom Quiet Zone (BQZ): This is a grade based on defects in the quiet zone area of ​​one module located on the lower side of the bottom 'L' side. • Top Quiet Zone (TQZ): This rating is based on defects in the quiet zone, which is an area equivalent to one module above the top clock track. • Right Quiet Zone (RQZ): This rating is based on defects in the quiet zone area of ​​one module located to the right of the right clock track. • Top Conversion Ratio (TTR): This is a rating based on defects in the top clock track in relation to adjacent quiet zones. • Right-side conversion ratio (RTR): This is the conversion ratio of the right clock track to the right quiet zone. • Top Clock Track (TCT): This is a rating based on defects in the top clock track. • Right Clock Track (RCT): This rating is based on defects in the right clock track. • Average Grade (AG): This grade takes into account the cumulative effect of damage from several parts of the finder pattern. • Decode: Reports whether the two-dimensional symbol was decoded according to the standard decoding algorithm at the specified aperture.

[0086] Table 3 shows the evaluation results. In the evaluation results, A is the best, and the results get worse as you move closer to F. [Table 3]

[0087] As shown in Table 3, in Sample 2, the decoding result was "F," and in Sample 4, there were no "F" results, but some items had a "C" result. In contrast, in Sample 1, all items were either "A" or "B."

[0088] From the results in Table 2 and the reading evaluation results in Table 3, it was confirmed that Sample 1, which met all of the following criteria—contrast of 90 or higher, color unevenness of 10 or lower, hue between 0.30 and 0.40, and engraving density of 0.60 or higher—had superior readability compared to Samples 2 and 4, which did not meet any of these criteria. Therefore, it was confirmed that using contrast, color unevenness, hue, and engraving density as evaluation indicators is appropriate.

[0089] 10...Object 11…Engraving 20…Imaging device 30…Image processing device 42A... Pre-trained model

Claims

1. A method for quantifying the evaluation index of an imprint formed on an object by laser marking, The evaluation index includes at least contrast, color unevenness, hue, and engraving density. A contrast calculation step comprising the steps of: capturing a region of the object including the markings using an imaging device to obtain a color image; converting the original image to a grayscale image and obtaining the pixel values ​​of the grayscale image; obtaining a first pixel value distribution graph from the pixel values ​​of the grayscale image; and calculating the contrast as the absolute value of the difference between the mode of the pixel values ​​in the laser-irradiated region, which is the region of the object irradiated with a laser, and the mode of the pixel values ​​in the non-laser-irradiated region, which is the region of the object not irradiated with a laser, based on the first pixel value distribution graph. A color unevenness calculation step includes the step of calculating the standard deviation of the pixel values ​​in the laser irradiation area as the color unevenness based on the first pixel value distribution graph, A color calculation step including the steps of: obtaining pixel values ​​of red, green, and blue from the original image; obtaining a second pixel value distribution graph for each color from the pixel values ​​of each color obtained from the original image; and calculating the color as the ratio of the mode of the pixel values ​​of the laser-irradiated area of ​​each color to the sum of the modes of the pixel values ​​of the laser-irradiated area of ​​each color based on each of the second pixel value distribution graphs. A marking density calculation step includes the steps of: extracting an image of at least a portion of the laser irradiation area from the grayscale image and obtaining the pixel values ​​of the extracted image; obtaining a third pixel value distribution graph from the pixel values ​​of the laser irradiation area; and calculating the marking density as the ratio of the area of ​​the laser marking good region to the total area of ​​the laser marking good region and the laser marking poor region in the laser irradiation area based on the third pixel value distribution graph. Methods that include...

2. The method according to claim 1, wherein the marking is an information code, a character, a number, a symbol, a picture, a figure, or a combination thereof.

3. The method according to claim 2, wherein the information code is a one-dimensional code or a two-dimensional code.

4. The method according to claim 1, wherein the contrast is 90 or more, the color unevenness is 10 or less, the hue is 0.30 or more and 0.40 or less, and the engraving density is 0.60 or more.

5. The method according to claim 1, wherein the step of calculating the contrast as the absolute value of the difference between the mode of the pixel values ​​in the laser-irradiated area and the mode of the pixel values ​​in the non-laser-irradiated area includes a distribution curve acquisition step of obtaining a first distribution curve for the laser-irradiated area and a second distribution curve for the non-laser-irradiated area by performing normal distribution fitting on the laser-irradiated area and the non-laser-irradiated area of ​​the first pixel value distribution graph, respectively; a mode extraction step of extracting the mode of the pixel values ​​in the laser-irradiated area based on the first distribution curve and the mode of the pixel values ​​in the non-laser-irradiated area based on the second distribution curve, respectively; and a mode difference calculation step of calculating the absolute value of the difference between the respective modes.

6. The method according to claim 5, wherein at least one of the steps of obtaining the distribution curve, extracting the mode, and calculating the mode difference is performed by a computer-executable program.

7. The method according to claim 1, wherein the step of calculating the standard deviation of the pixel values ​​in the laser irradiation area as the color unevenness is performed by a computer-executable program.

8. The method according to claim 1, wherein the step of calculating the color tone as the ratio of the mode of the pixel values ​​in the laser-irradiated area of ​​each color to the sum of the modes of the pixel values ​​in the laser-irradiated area of ​​each color includes a mode extraction step of extracting the mode of the pixel values ​​in the laser-irradiated area of ​​each color based on a second pixel value distribution graph of each color, and a mode ratio calculation step of calculating the ratio of the mode of the pixel values ​​in the laser-irradiated area of ​​each color to the sum of the modes of each color.

9. The method according to claim 8, wherein at least one of the mode extraction step and the mode ratio calculation step is performed by a computer-executable program.

10. The method according to claim 1, wherein the step of calculating the marking density as the ratio of the area of ​​the laser marking good region to the total area of ​​the laser marking good region and the laser marking poor region in the laser irradiation region includes a third distribution curve acquisition step of performing a normal distribution fitting on the third pixel value distribution graph to obtain a third distribution curve, and an area ratio calculation step of calculating the area of ​​the laser marking good region and the area of ​​the laser marking poor region, respectively, based on the third distribution curve, and determining the ratio of the total area of ​​the laser marking good region to the total area of ​​the laser marking good region and the laser marking poor region.

11. The method according to claim 10, wherein at least one of the third distribution curve acquisition step and the area ratio calculation step is performed by a computer-executable program.

12. A method for evaluating an imprint formed on an object by laser marking, using the contrast, color unevenness, hue, and imprint density quantified by the method of claim 1.

13. A method for predicting the optimal laser irradiation conditions for forming an imprint on an object by laser marking, A method for predicting the optimal laser irradiation conditions that satisfy the input second information, by inputting at least the second information into a trained model generated by machine learning, which takes first information relating to laser irradiation conditions and second information relating to the contrast, color unevenness, color tone, and engraving density as defined in claim 1 associated with the first information as input data, and outputs the optimal laser irradiation conditions.

14. The method according to claim 13, wherein the input data further includes third information relating to the material of the object associated with the first information and the second information, and the optimal laser irradiation conditions that satisfy the input second information and the third information are predicted by inputting the second information and the third information into the trained model.