A tool wear monitoring method and device based on an operation and maintenance database, an electronic device, and a medium

Abstract

The application relates to a tool wear monitoring method and device based on an operation and maintenance database, electronic equipment and a medium, and relates to the field of tool monitoring. The method comprises the following steps: acquiring a first image of multiple finished products machined by a cutting machine tool in a preset time period and a vibration change graph of the tool in a machining state; determining the profile and roughness of the tool cutting surface of each finished product based on the first image; determining the quality score of the tool machined finished product based on the profile and roughness; determining the fastening value between the tool and the machine tool spindle based on the vibration change graph; determining the health value of the tool based on the quality score and the fastening value; and determining the wear degree of the tool based on the health value. The application has the effect of monitoring the wear of the tool in real time and accurately.

Description

Technical Field

[0001] This application relates to the field of tool monitoring, and in particular to a tool wear monitoring method, device, electronic device and medium based on an operation and maintenance database. Background Technology

[0002] Cutting is one of the most commonly used processes for machining metal materials. It has high production efficiency, high processing quality, and low processing cost. However, as a consumable part, the cutting tool will rub against the workpiece when cutting it, which will cause the cutting tool to wear. When it wears down to the point of being dull, its cutting effect will decrease and it will need to be replaced.

[0003] Existing detection technologies are insufficient for real-time monitoring of the working status of cutting tools, and the wear degree of cutting tools is determined by manual measurement, which is greatly affected by human subjectivity and has low accuracy. Therefore, how to monitor the wear of cutting tools in real time and accurately has become a problem. Summary of the Invention

[0004] To monitor tool wear in real time and accurately, this application provides a tool wear monitoring method, device, electronic device, and medium based on an operation and maintenance database.

[0005] Firstly, this application provides a tool wear monitoring method based on an operation and maintenance database, employing the following technical solution:

[0006] A tool wear monitoring method based on an operations and maintenance database includes:

[0007] Acquire first images of multiple finished products processed by a cutting machine tool within a preset time period and vibration variation diagrams of the cutting tool under processing conditions;

[0008] Based on the first image, the contour and roughness of the cutting surface of each finished product are determined, and the quality score of the finished product processed by the tool is determined based on the contour and roughness.

[0009] The clamping value between the cutting tool and the machine tool spindle is determined based on the vibration variation diagram.

[0010] The tool's health value is determined based on the quality score and firmness value;

[0011] The degree of tool wear is determined based on the health value.

[0012] By employing the above technical solution, first images of multiple finished products machined by a cutting machine tool within a preset time period and vibration variation diagrams of the cutting tool under machining conditions are obtained. The first images reflect the cutting situation of the machine tool, and the vibration variation diagrams reflect the vibration variation of the cutting tool under machining conditions. Based on the first images, the contour and roughness of the cutting surface of each finished product are determined. The greater the roughness, the rougher the cutting surface of the finished product, and the greater the wear of the cutting tool. Based on the contour and roughness, a quality score of the machined finished product is determined. The quality score characterizes the quality level of the machined finished product. In addition, resonance occurs between the cutting tool and the workpiece during the cutting process. If the tool is worn, its vibration frequency may increase. Repeated large-amplitude vibrations may cause the tool to loosen between itself and the machine tool spindle, leading to more severe tool wear. Therefore, the firmness value between the tool and the machine tool spindle is determined based on the vibration change graph. The worse the firmness value, the looser the tool, the more severe the tool wear, and the worse the quality of the machined product. This reflects the more severe the tool wear. Therefore, the tool health value is determined based on the quality score and the firmness value. Finally, the degree of tool wear is determined based on the health value. This allows for real-time monitoring of tool wear and more accurate monitoring of the degree of tool wear.

[0013] In another possible implementation, determining the profile and roughness of the tool-cutting surface of each finished product based on the first image includes:

[0014] The first image is used for feature recognition to determine the contour of the cutting surface of each finished product.

[0015] The first image is extracted based on the contour, and a grayscale image is obtained by grayscale transformation.

[0016] The pixel values ​​of the grayscale image are statistically analyzed to obtain a grayscale histogram;

[0017] The roughness of the cutting surface of each finished product is determined based on the grayscale histogram.

[0018] In another possible implementation, determining the quality score of the machined product based on the profile and roughness includes:

[0019] Determine the similarity between each contour and a preset contour, and determine the variance of all similarities;

[0020] The number of items with a roughness higher than a preset roughness threshold is determined, and the ratio of this number to the total number of finished products is determined.

[0021] The quality score of the finished product processed by the tool is determined based on the variance and ratio.

[0022] In another possible implementation, the method further includes:

[0023] Obtain the real-time health value of the cutting tool after each finished product is machined by the cutting machine tool;

[0024] The wear rate of the cutting tool is determined based on the real-time health value;

[0025] The amount of machining completed by the tool is determined, and the remaining amount of machining is determined based on the wear rate and the amount of machining completed by the tool.

[0026] In another possible implementation, determining the tool wear rate based on the real-time health value includes:

[0027] Determine the first difference between the real-time health value of the next finished product and the real-time health value of the previous finished product for each pair of adjacent finished products.

[0028] Determine the sum of all the first differences;

[0029] Obtain the current first health value of the tool and the second health value before the preset time period, and determine the second difference between the first health value and the second health value;

[0030] The wear rate of the tool is determined based on the sum and the second difference.

[0031] In another possible implementation, the method further includes:

[0032] Acquire audio information during the machining process of a cutting machine tool, and filter out environmental audio information from the audio information to obtain target audio information;

[0033] Determine whether there is a target audio segment in the target audio information that is the same as a preset audio segment;

[0034] If they exist, determine the number of target audio segments and the percentage of total duration of all target audio segments in the target audio information;

[0035] The health value is adjusted based on the quantity and the percentage of total duration.

[0036] In another possible implementation, the method further includes:

[0037] Acquire a second image of the machine tool in the machining state, wherein the second image includes the cutting tool;

[0038] The second image is used to identify the tool, and the tool identification result is used to determine whether the tool is broken.

[0039] If a break occurs, the machine tool will be powered off.

[0040] Secondly, this application provides a tool wear monitoring device based on an operation and maintenance database, which adopts the following technical solution:

[0041] A tool wear monitoring device based on an operation and maintenance database includes:

[0042] The first acquisition module is used to acquire first images of multiple finished products processed by a cutting machine tool within a preset time period and vibration change diagrams of the cutting tool under processing conditions.

[0043] The first determining module is used to determine the contour and roughness of the cutting surface of each finished product based on the first image, and to determine the quality score of the finished product processed by the tool based on the contour and roughness.

[0044] A stability value determination module is used to determine the stability value between the cutting tool and the machine tool spindle based on the vibration change diagram.

[0045] A health value determination module is used to determine the health value of the cutting tool based on the quality score and the firmness value.

[0046] The wear degree determination module is used to determine the wear degree of the tool based on the health value.

[0047] By adopting the above technical solution, the first acquisition module acquires first images of multiple finished products processed by a cutting machine tool within a preset time period and vibration variation diagrams of the cutting tool under processing conditions. The first images reflect the cutting situation of the machine tool, and the vibration variation diagrams reflect the vibration variation of the cutting tool under processing conditions. The first determination module determines the contour and roughness of the cutting surface of each finished product based on the first images. The greater the roughness, the rougher the cutting surface of the finished product, and the greater the wear of the cutting tool. Based on the contour and roughness, the quality score of the finished product processed by the cutting tool is determined. The quality score characterizes the quality level of the finished product processed by the cutting tool. In addition, during the cutting process of the workpiece, resonance occurs between the cutting tool and the workpiece. If the cutting tool has... Wear can increase the vibration frequency of the tool. Repeated large-amplitude vibrations may cause the tool to loosen between itself and the machine tool spindle, leading to more severe tool wear. Therefore, the firmness value determination module determines the firmness value between the tool and the machine tool spindle based on the vibration change graph. The worse the firmness value, the looser the tool, the more severe the wear on the tool, and the worse the quality of the machined product. Thus, the health value determination module determines the health value of the tool based on the quality score and the firmness value. Finally, the wear degree determination module determines the wear degree of the tool based on the health value. This allows for real-time monitoring of tool wear and provides more accurate monitoring of the wear degree.

[0048] In another possible implementation, when the first determining module determines the contour and roughness of the tool cutting surface of each finished product based on the first image, it is specifically used for:

[0049] The first image is used for feature recognition to determine the contour of the cutting surface of each finished product.

[0050] The first image is extracted based on the contour, and a grayscale image is obtained by grayscale transformation.

[0051] The pixel values ​​of the grayscale image are statistically analyzed to obtain a grayscale histogram;

[0052] The roughness of the cutting surface of each finished product is determined based on the grayscale histogram.

[0053] In another possible implementation, when determining the quality score of the machined product based on the contour and roughness, the first determining module is specifically used for:

[0054] Determine the similarity between each contour and a preset contour, and determine the variance of all similarities;

[0055] The number of items with a roughness higher than a preset roughness threshold is determined, and the ratio of this number to the total number of finished products is determined.

[0056] The quality score of the finished product processed by the tool is determined based on the variance and ratio.

[0057] In another possible implementation, the device further includes:

[0058] The second acquisition module is used to acquire the real-time health value of the cutting tool after the cutting machine tool has completed the processing of each finished product;

[0059] The second determining module is used to determine the wear rate of the cutting tool based on the real-time health value;

[0060] The third determining module is used to determine the amount of machining completed by the tool, and to determine the remaining amount of machining based on the wear rate and the amount of machining completed by the tool.

[0061] In another possible implementation, when the second determining module determines the wear rate of the tool based on the real-time health value, it is specifically used for:

[0062] Determine the first difference between the real-time health value of the next finished product and the real-time health value of the previous finished product for each pair of adjacent finished products.

[0063] Determine the sum of all the first differences;

[0064] Obtain the current first health value of the tool and the second health value before the preset time period, and determine the second difference between the first health value and the second health value;

[0065] The wear rate of the tool is determined based on the sum and the second difference.

[0066] In another possible implementation, the device further includes:

[0067] The third acquisition module is used to acquire audio information under the machining state of the cutting machine tool, and filter out the environmental audio information in the audio information to obtain the target audio information;

[0068] The judgment module is used to determine whether there is a target audio segment in the target audio information that is the same as a preset audio segment;

[0069] The fourth determination module is used to determine, if it exists, the number of target audio segments and the percentage of the total duration of all target audio segments in the target audio information;

[0070] The adjustment module is used to adjust the health value based on the quantity and the percentage of total duration.

[0071] In another possible implementation, the device further includes:

[0072] The fourth acquisition module is used to acquire a second image of the machine tool in the machining state, wherein the second image includes the cutting tool;

[0073] The identification module is used to identify the tool in the second image and determine whether the tool is broken based on the obtained tool identification result;

[0074] A control module is used to disconnect the power supply to the machine tool in the event of a breakage.

[0075] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0076] An electronic device comprising:

[0077] At least one processor;

[0078] Memory;

[0079] At least one application, wherein the application is stored in memory and configured to be executed by at least one processor, and at least one configuration is for: executing a tool wear monitoring method based on an operation and maintenance database as shown in any possible implementation of the first aspect.

[0080] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0081] A computer-readable storage medium, when the computer program is executed in a computer, causes the computer to perform a tool wear monitoring method based on an operation and maintenance database as described in any of the first aspects.

[0082] In summary, this application includes at least one of the following beneficial technical effects:

[0083] This method acquires first images of multiple finished products machined on a cutting machine tool within a preset time period, along with vibration variation diagrams of the cutting tool during the machining process. The first images reflect the cutting conditions of the machine tool, while the vibration variation diagrams reflect the vibration variations of the cutting tool during machining. Based on the first images, the contour and roughness of the cutting surface of each finished product are determined. Higher roughness indicates a rougher cutting surface and greater tool wear. A quality score for the machined finished product is determined based on the contour and roughness, representing the quality level of the machined product. Furthermore, during the cutting process, resonance occurs between the tool and the workpiece. If the tool is worn, its vibration frequency may increase. Multiple large-amplitude vibrations may loosen the connection between the tool and the machine spindle, leading to more severe tool wear. Therefore, the firmness value between the tool and the machine spindle is determined based on the vibration variation diagram. A lower firmness value indicates a looser tool, resulting in more severe tool wear and lower finished product quality. Thus, a tool health value is determined based on the quality score and firmness value. Finally, the degree of tool wear is determined based on the health value. This method enables real-time monitoring of tool wear and provides more accurate monitoring of tool wear levels. Attached Figure Description

[0084] Figure 1 This is a flowchart illustrating a tool wear monitoring method based on an operation and maintenance database according to an embodiment of this application.

[0085] Figure 2 This is a schematic diagram of a tool wear monitoring device based on an operation and maintenance database according to an embodiment of this application.

[0086] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0087] The present application will be further described in detail below with reference to the accompanying drawings.

[0088] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0089] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0090] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0091] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0092] This application provides a tool wear monitoring method based on an operation and maintenance database, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this. Figure 1 As shown, the method includes steps S101, S102, S103, S104, and S105, wherein,

[0093] S101, acquire the first image of multiple finished products processed by the cutting machine tool within a preset time period and the vibration change diagram of the tool under the processing state.

[0094] In the embodiments of this application, turning is one of the more common cutting methods in the machining process of machine tools. The cutting tool is used continuously and is generally installed on the movable transmission device of the machine tool. Multiple finished products can be processed. A camera device can be fixed near the machining position of the machine tool in advance by the operator, and a vibration sensor can be installed on the transmission device. The installation positions of the camera device and the vibration sensor do not affect the normal use of the machine tool, and the camera device faces the cutting position of the tool. Both the camera device and the vibration sensor are wired or wirelessly connected to the electronic equipment. The camera device is used to collect images during the machining process, and the vibration sensor can collect the vibration changes of the tool. The electronic equipment obtains the first image of multiple finished products processed by the machine tool within a preset time period and the vibration change diagram of the tool under the machining state through the camera device.

[0095] S102, determine the profile and roughness of the tool cutting surface of each finished product based on the first image, and determine the quality score of the finished product processed by the tool based on the profile and roughness.

[0096] In the embodiments of this application, the first image can reflect the cutting situation of the machine tool. The electronic device determines the contour and roughness of the cutting surface of each finished product based on the first image, and determines the quality score of the finished product processed by the tool based on the contour and roughness. The quality score characterizes the quality level of the finished product.

[0097] S103, determine the clamping value between the tool and the machine tool spindle based on the vibration variation diagram.

[0098] In the embodiments of this application, resonance occurs between the tool and the workpiece during the cutting process. If the tool is worn, the vibration frequency of the tool may increase. Multiple large-amplitude vibrations may cause the tool to become loose between itself and the machine tool spindle, which will lead to more severe tool wear. Therefore, the electronic device determines the firmness value between the tool and the machine tool spindle based on the vibration change graph. Specifically, the firmness value between the tool and the machine tool spindle can be determined by the percentage of vibrations exceeding the preset amplitude, the difference between the maximum amplitude and the preset amplitude, and their respective first coefficients in the vibration change graph. The firmness value characterizes the looseness between the tool and the machine tool spindle.

[0099] S104 determines the tool's health value based on the quality score and firmness value.

[0100] In the embodiments of this application, the quality score characterizes the finished products of different qualities produced by tools with different degrees of wear. The lower the quality score, the worse the quality of the finished product, which reflects the more severe the wear of the tool. The firmness value characterizes the looseness between the tool and the machine tool spindle. The worse the firmness value, the looser the tool, and the more severe the wear on the tool. The electronic device determines the health value of the tool through the quality score and the firmness value. The health value reflects the wear of the tool during continuous cutting.

[0101] S105 determines the degree of tool wear based on health values.

[0102] In this embodiment, different preset health value ranges correspond to different degrees of wear. The electronic device determines the target health value range to which the health value belongs and the degree of wear corresponding to the target health value range. The lower the health value, the higher the degree of wear. The quality score of the finished product is determined based on the first image of the finished product, and the firmness value between the tool and the machine tool spindle is determined based on the vibration change diagram of the tool during processing. Based on the quality score and the firmness value, the health value of the tool is determined, and finally, the degree of tool wear is determined. This allows for real-time monitoring of tool wear and provides more accurate monitoring of the degree of tool wear.

[0103] One possible implementation of this application embodiment is that step S102, which determines the contour and roughness of the cutting surface of each finished product based on the first image, specifically includes steps S1021 (not shown in the figure), S1022 (not shown in the figure), S1023 (not shown in the figure), and S1024 (not shown in the figure), wherein,

[0104] Step S1021: Perform feature recognition on the first image to determine the contour of the cutting surface of each finished product.

[0105] In this embodiment of the application, the electronic device inputs the first image into the trained network model for feature recognition to determine the contour of the cutting surface of each finished product. Finished products processed by tools with different wear may have inconsistent contours, so the contour reflects the wear condition of the tool.

[0106] Step S1022: Extract the first image based on the contour and perform grayscale transformation to obtain a grayscale image.

[0107] In this embodiment of the application, the electronic device extracts the first image based on the contour and performs grayscale transformation to obtain a grayscale image, which can improve the image quality of the first image and make the image display effect clearer.

[0108] Step S1023: Statistical analysis of pixel values ​​in the grayscale image to obtain a grayscale histogram.

[0109] In the embodiments of this application, a grayscale image represents the number of pixels at each grayscale level (or brightness level) in the image. Electronic devices can obtain a grayscale histogram by statistically analyzing the pixel values ​​of a grayscale image, thereby facilitating the analysis of the grayscale distribution characteristics of the image.

[0110] Step S1024: Determine the roughness of the tool cutting surface of each finished product based on the grayscale histogram.

[0111] In this embodiment of the application, the electronic device determines the roughness of the cutting surface of each finished product based on a grayscale histogram. The lower the roughness, the smoother the cutting surface and the less wear on the cutting tool. The roughness can be calculated using the following formula:

[0112] Ra = |f(x)| dx / l

[0113] Where Ra represents roughness, |f(x)|dx represents the absolute integral of the area between the curve and the center line in the gray-scale histogram, and l is the sampling length.

[0114] One possible implementation of this application embodiment is that step S102, which determines the quality score of the machined product based on the contour and roughness, specifically includes steps S1025 (not shown in the figure), S1026 (not shown in the figure), and S1027 (not shown in the figure), wherein,

[0115] Step S1025: Determine the similarity between each contour and the preset contour, and determine the variance of all similarities.

[0116] In the embodiments of this application, the electronic device determines the similarity between each contour and the preset contour, and calculates the variance of all similarities. The lower the similarity, the greater the deviation between the finished product and the standard finished product. The magnitude of the variance characterizes the degree of dispersion of the similarity between multiple contours and the preset contour. The larger the variance, the greater the difference in similarity between multiple contours, indicating that the contours of the finished products processed by the tool are inconsistent and the quality of the processed finished products is worse.

[0117] Step S1026: Determine the number of items with roughness higher than the preset roughness threshold, and determine the ratio of the number of items to the total number of finished products.

[0118] In the embodiments of this application, the electronic device determines the number of items with roughness higher than a preset roughness threshold, and determines the ratio of the number of items to the total number of finished products. The larger the ratio, the greater the proportion of less smooth finished products, indicating that the quality of the processed finished products is worse.

[0119] Step S1027: Determine the quality score of the finished product processed by the tool based on variance and proportion.

[0120] In the embodiments of this application, the electronic device determines the quality score of the finished product processed by the tool based on the variance, the ratio, and their respective second coefficients. The quality score characterizes the processing quality of the tool.

[0121] One possible implementation of this application embodiment includes steps one, two, and three, wherein...

[0122] Step 1: Obtain the real-time health value of the cutting tool after each finished product is machined.

[0123] In the embodiments of this application, during the processing of each finished product, the electronic device calculates the real-time health value of the cutting tool. Therefore, the electronic device obtains the real-time health value of the cutting tool after the cutting machine tool has processed each finished product.

[0124] Step 2: Determine the tool wear rate based on real-time health values.

[0125] In this embodiment of the application, the electronic device determines the wear rate of the tool based on real-time health values. The faster the wear rate, the shorter the tool's lifespan.

[0126] Step 3: Determine the amount of machining already completed by the tool, and determine the remaining amount of machining based on the wear rate and the amount of machining already completed by the tool.

[0127] In the embodiments of this application, the electronic device determines the amount of machining completed by the cutting tool, and determines the remaining amount of machining based on the grinding speed and the amount of machining completed by the cutting tool. When the cutting tool has been machined to the remaining amount of machining, it is convenient for the staff to replace the cutting tool in a timely manner.

[0128] One possible implementation of this application embodiment is that step two, which determines the tool wear rate based on real-time health values, specifically includes steps S1 (not shown in the figure), S2 (not shown in the figure), S3 (not shown in the figure), and S4 (not shown in the figure), wherein,

[0129] Step S1: Determine the first difference between the real-time health value of the next finished product and the real-time health value of the previous finished product for every two adjacent finished products.

[0130] In the embodiments of this application, the electronic device determines a first difference between the real-time health value of the latter finished product and the real-time health value of the former finished product in every two adjacent finished products.

[0131] Step S2: Determine the sum of all first differences.

[0132] In the embodiments of this application, the electronic device determines the sum of all first differences. The first differences obtained between adjacent finished products may be positive or negative. Calculating the sum more accurately characterizes the severity of tool wear.

[0133] Step S3: Obtain the current first health value of the tool and the second health value before a preset time period, and determine the second difference between the first health value and the second health value.

[0134] In the embodiments of this application, the electronic device obtains the current first health value of the tool and the second health value before a preset time period, and determines the second difference between the first health value and the second health value. The second difference characterizes the wear condition of the tool after it has processed multiple finished products.

[0135] Step S4: Determine the tool wear rate based on the sum and the second difference.

[0136] In the embodiments of this application, the electronic device determines a first score based on the sum, the second difference, and their respective third coefficients, and determines the wear rate of the tool corresponding to the first score. The wear rate characterizes how fast or slow the tool wears.

[0137] One possible implementation of this application embodiment includes steps four, five, six, and seven, wherein...

[0138] Step 4: Obtain the audio information under the machining state of the cutting machine tool, and filter out the environmental audio information in the audio information to obtain the target audio information.

[0139] In the embodiments of this application, during the machining process, there will be some metal friction sounds between the tool and the workpiece. The electronic device acquires the audio information of the cutting machine tool in the machining state, and filters out the environmental audio information in the audio information to obtain the target audio information. The target audio information represents the sound generated by the machine tool and the tool in the machining state.

[0140] Step 5: Determine whether there is a target audio segment in the target audio information that is the same as the preset audio segment.

[0141] In the embodiments of this application, when the cutting tool wears down, the sound may become irregular or intermittent, or it may produce a piercing scream or obvious vibration. Therefore, the electronic device determines whether there is a target audio segment in the target audio information that is the same as the preset audio segment.

[0142] The preset audio segments are abnormal audio segments left over from the wear process of historically replaced tools, and the preset audio segments are stored in electronic devices.

[0143] Step 6: If it exists, determine the number of target audio segments and the percentage of the total duration of all target audio segments in the target audio information.

[0144] For the embodiments of this application, if they exist, the number of target audio segments and the percentage of the total duration of all target audio segments in the target audio information are determined. The more the number or the higher the percentage of the total duration, the more severe the tool wear.

[0145] Step 7: Adjust the health value based on the quantity and the percentage of total time.

[0146] In this embodiment of the application, the electronic device determines the second score based on the quantity, the proportion of total time and their respective fourth coefficients, and determines the preset adjustment value corresponding to the second score. Based on the health value, the preset adjustment value is reduced to obtain the final tool health value.

[0147] One possible implementation of this application embodiment includes steps eight, nine, and ten, wherein...

[0148] Step 8: Obtain the second image under the machine tool processing status.

[0149] The second image includes the knife.

[0150] In this embodiment of the application, the electronic device acquires a second image in the machining state of the machine tool. The second image includes the cutting tool and can reflect the state of the cutting tool.

[0151] Step nine: Perform tool identification on the second image and determine whether the tool is broken based on the obtained tool identification results.

[0152] In this embodiment of the application, the electronic device performs tool recognition in the network model trained with the second image input value, and determines whether the tool is broken based on the tool recognition result.

[0153] Step 10: If a break occurs, control the machine tool to disconnect the power supply.

[0154] In this embodiment of the application, if the tool breaks, the electronic device controls the machine tool to disconnect the power supply in time, so that the staff can replace the tool with a new one.

[0155] The above embodiments describe a tool wear monitoring method based on an operation and maintenance database from the perspective of method flow. The following embodiments describe a tool wear monitoring device based on an operation and maintenance database from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0156] This application provides a tool wear monitoring device 20 based on an operation and maintenance database, such as... Figure 2 As shown, the tool wear monitoring device 20 based on an operation and maintenance database may specifically include:

[0157] The first acquisition module 201 is used to acquire first images of multiple finished products processed by a cutting machine tool within a preset time period and vibration change diagrams of the cutting tool under processing conditions.

[0158] The first determining module 202 is used to determine the profile and roughness of the cutting surface of each finished product based on the first image, and to determine the quality score of the finished product processed by the tool based on the profile and roughness.

[0159] The adhesion value determination module 203 is used to determine the adhesion value between the tool and the machine tool spindle based on the vibration change diagram.

[0160] Health value determination module 204 is used to determine the health value of the tool based on the quality score and the firmness value;

[0161] Wear degree determination module 205 is used to determine the wear degree of the tool based on the health value.

[0162] This application discloses a tool wear monitoring device 20 based on an operation and maintenance database. The first acquisition module 201 acquires first images of multiple finished products machined by a cutting machine tool within a preset time period and vibration variation diagrams of the tool under machining conditions. The first images reflect the cutting situation of the machine tool, and the vibration variation diagrams reflect the vibration variation of the tool under machining conditions. The first determination module 202 determines the contour and roughness of the tool cutting surface of each finished product based on the first images. Higher roughness indicates a rougher cutting surface and greater tool wear. Based on the contour and roughness, a quality score is determined for the machined finished product. The quality score characterizes the quality level of the machined finished product. Furthermore, during the cutting process, the wear between the tool and the workpiece... If resonance occurs and the tool is worn, the vibration frequency of the tool may increase. Repeated large-amplitude vibrations may cause the tool to become loose between itself and the machine tool spindle, leading to more severe tool wear. Therefore, the firmness value determination module 203 determines the firmness value between the tool and the machine tool spindle based on the vibration change graph. The worse the firmness value, the looser the tool, the more severe the wear on the tool, and the worse the quality of the finished product. Thus, the health value determination module 204 determines the health value of the tool based on the quality score and the firmness value. Finally, the wear degree determination module 205 determines the wear degree of the tool based on the health value. This allows for real-time monitoring of tool wear and provides more accurate monitoring of the wear degree.

[0163] In one possible implementation of this application embodiment, when the first determining module 202 determines the contour and roughness of the cutting surface of each finished product based on the first image, it is specifically used for:

[0164] Feature recognition is performed on the first image to determine the contour of the cutting surface of each finished product.

[0165] The first image is extracted based on its contours, and then grayscale transformation is performed to obtain a grayscale image.

[0166] The pixel values ​​of a grayscale image are statistically analyzed to obtain a grayscale histogram.

[0167] The roughness of the cutting surface of each finished product is determined based on the grayscale histogram.

[0168] In one possible implementation of this application embodiment, when the first determining module 202 determines the quality score of the finished product machined by the tool based on the contour and roughness, it is specifically used for:

[0169] Determine the similarity between each contour and a preset contour, and determine the variance of all similarities;

[0170] Determine the number of items with a roughness higher than a preset roughness threshold, and determine the ratio of this number to the total number of finished products;

[0171] The quality score of the finished product is determined based on variance and ratio.

[0172] In one possible implementation of this application embodiment, the apparatus 20 further includes:

[0173] The second acquisition module is used to acquire the real-time health value of the cutting tool after each finished product is machined by the cutting machine tool;

[0174] The second determination module is used to determine the wear rate of the tool based on real-time health values;

[0175] The third determining module is used to determine the amount of machining completed by the tool, and to determine the remaining amount of machining based on the wear rate and the amount of machining completed by the tool.

[0176] In one possible implementation of this application embodiment, when the second determining module determines the tool wear rate based on real-time health values, it is specifically used for:

[0177] Determine the first difference between the real-time health value of the next finished product and the real-time health value of the previous finished product for each pair of adjacent finished products.

[0178] Determine the sum of all the first differences;

[0179] Obtain the current first health value of the tool and the second health value before a preset time period, and determine the second difference between the first health value and the second health value;

[0180] The tool wear rate is determined based on the sum and the second difference.

[0181] In one possible implementation of this application embodiment, the apparatus 20 further includes:

[0182] The third acquisition module is used to acquire audio information under the machining state of the cutting machine tool, and filter out the environmental audio information in the audio information to obtain the target audio information;

[0183] The judgment module is used to determine whether there is a target audio segment in the target audio information that is the same as the preset audio segment;

[0184] The fourth determination module is used to determine, if it exists, the number of target audio segments and the percentage of the total duration of all target audio segments in the target audio information;

[0185] The adjustment module is used to adjust the health value based on the quantity and the percentage of total time.

[0186] In one possible implementation of this application embodiment, the apparatus 20 further includes:

[0187] The fourth acquisition module is used to acquire a second image under the machine tool processing state, the second image being an image including the cutting tool;

[0188] The identification module is used to identify the tool in the second image and determine whether the tool is broken based on the obtained tool identification result;

[0189] The control module is used to disconnect the power supply to the machine tool in case of failure.

[0190] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 30 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 30 does not constitute a limitation on the embodiments of this application.

[0191] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0192] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0193] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0194] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0195] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0196] This application provides a computer-readable storage medium storing a computer program, which, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments. Compared with related technologies, this application acquires a first image of multiple finished products processed by a cutting machine tool within a preset time period and a vibration variation diagram of the cutting tool under processing conditions. The first image reflects the cutting situation of the machine tool, and the vibration variation diagram reflects the vibration variation of the cutting tool under processing conditions. Based on the first image, the contour and roughness of the cutting surface of each finished product are determined. The greater the roughness, the rougher the cutting surface of the finished product, and the greater the wear of the cutting tool. Based on the contour and roughness, a quality score of the finished product processed by the cutting tool is determined. The quality score characterizes the quality level of the finished product processed by the cutting tool. In addition, during the cutting process of the workpiece, the cutting tool and the workpiece undergo resonance. Vibration, if the tool is worn, may increase the tool vibration frequency. Repeated large-amplitude vibrations may cause the tool to loosen between itself and the machine tool spindle, leading to more severe tool wear. Therefore, the firmness value between the tool and the machine tool spindle is determined based on the vibration change graph. The worse the firmness value, the looser the tool, the more severe the tool wear, and the worse the quality of the machined product. Thus, the tool health value is determined based on the quality score and the firmness value. Finally, the degree of tool wear is determined based on the health value. This allows for real-time monitoring of tool wear and more accurate monitoring of the degree of tool wear.

[0197] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0198] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A tool wear monitoring method based on an operation and maintenance database, characterized by, The method comprises the following steps: acquiring a first image of a plurality of finished products machined by a cutting machine tool in a preset time period and a vibration change graph of the tool in a machining state; determining a profile and roughness of a tool cutting surface of each finished product based on the first image, determining a quality score of the tool machined finished product based on the profile and roughness; determining a fastness value between the tool and a machine tool spindle based on the vibration change graph; determining a health value of the tool based on the quality score and fastness value; determining a degree of wear of the tool based on the health value; the step of determining the profile and roughness of the tool cutting surface of each finished product based on the first image comprises the following steps: performing feature recognition on the first image to determine the profile of the tool cutting surface of each finished product; performing extraction on the first image based on the profile and performing gray scale conversion to obtain a gray scale image; performing statistics on pixel values of the gray scale image to obtain a gray scale histogram; determining the roughness of the tool cutting surface of each finished product based on the gray scale histogram; the step of determining the quality score of the tool machined finished product based on the profile and roughness comprises the following steps: determining the similarity of each profile to a preset profile and determining the variance of all similarities; determining the number of roughnesses higher than a preset roughness threshold and determining the ratio of the number to the total number of finished products; determining the quality score of the tool machined finished product based on the variance and the ratio; the method further comprises the following steps: acquiring a real-time health value of the tool after each finished product is machined by the cutting machine tool; determining a wear rate of the tool based on the real-time health value; determining the completed machining amount of the tool and determining the remaining machining amount based on the wear rate and the completed machining amount of the tool; the step of determining the wear rate of the tool based on the real-time health value comprises the following steps: determining a first difference value between the real-time health value of a latter finished product and the real-time health value of a former finished product of each adjacent two finished products; determining the sum of all first difference values; acquiring a first health value of the tool at present and a second health value before the preset time period and determining a second difference value between the first health value and the second health value; determining the wear rate of the tool based on the sum and the second difference value.

2. The tool wear monitoring method based on the operation and maintenance database according to claim 1, characterized in that, The method further comprises the following steps: acquiring audio information in a machining state of the cutting machine tool and filtering out environmental audio information in the audio information to obtain target audio information; determining whether there is a target audio segment identical to a preset audio segment in the target audio information; if there is, determining the number of target audio segments and the total time length proportion of all target audio segments in the target audio information; adjusting the health value based on the number and the total time length proportion.

3. The tool wear monitoring method based on the operation and maintenance database according to claim 1, characterized in that, The method further comprises the following steps: acquiring a second image in the machining state of the machine tool, the second image being an image including the tool; performing tool recognition on the second image and determining whether the tool is broken based on the obtained tool recognition result; if the tool is broken, controlling the machine tool to disconnect the power supply.

4. A device for tool wear monitoring based on the method according to any one of claims 1 to 3, characterized in that The method comprises the following steps: The first acquisition module is configured to acquire a first image of multiple finished products machined by a cutting machine tool in a preset time period and a vibration change graph of the tool in a machining state; the first determination module is configured to determine a profile and roughness of a tool cutting surface of each finished product based on the first image, and determine a quality score of the tool machining finished product based on the profile and roughness; The firmness value determination module is configured to determine a firmness value between the tool and the machine tool spindle based on the vibration change graph; The health value determination module is configured to determine a health value of the tool based on the quality score and the firmness value; The wear degree determination module is configured to determine a wear degree of the tool based on the health value.

5. An electronic device, comprising: It comprises: At least one processor; Memory; At least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the tool wear monitoring method based on the operation and maintenance database according to any one of claims 1-3.

6. A computer readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed in the computer, the computer is enabled to execute the tool wear monitoring method based on the operation and maintenance database according to any one of claims 1-3.

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