Steel hot stamping equipment damage monitoring system and method based on machine vision

Abstract

The invention discloses a steel hot stamping equipment damage monitoring system and method based on machine vision, and belongs to the field of machine vision. Images of stamped products and size data of all positions of the stamped products are imported into a stamping production anomaly analysis model for stamping production anomaly analysis; and the obtained stamping equipment operation abnormity analysis result and the obtained stamping production abnormity analysis result are imported into a stamping process abnormity analysis strategy for stamping process abnormity analysis, stamping process abnormity early warning is conducted according to the obtained stamping process abnormity analysis result, non-contact and high-precision damage detection is achieved through the machine vision technology, and the stamping process abnormity early warning efficiency is improved. The system has the advantages that subjectivity and errors of manual detection are avoided, the running state of equipment is monitored in real time, potential damage is found and early warned in time, safety accidents and production loss caused by equipment faults are avoided, the frequency and time of manual inspection are reduced through automatic monitoring, and the production efficiency and the equipment utilization rate are improved.

Description

Technical Field

[0001] The invention belongs to the field of machine vision, and in particular relates to a damage monitoring system and method for steel hot stamping equipment based on machine vision. Background Art

[0002] With the increasing requirements for automobile lightweighting and corrosion resistance, automobile steel plates are gradually developing towards high-strength steel plates and galvanized steel plates. As a key link in the automobile manufacturing process, the stability and safety of steel hot stamping equipment directly affect the quality and efficiency of automobile production. However, during long-term operation, steel hot stamping equipment is prone to various damages such as mold wear, cracks in equipment components, and surface corrosion due to extreme working environments such as high temperature, high pressure, and high load. These damages not only affect the normal operation of the equipment, but may also cause safety accidents. Therefore, it is particularly important to develop an efficient and accurate damage monitoring system for steel hot stamping equipment;

[0003] In the existing steel hot stamping equipment damage monitoring system, it is impossible to comprehensively and accurately analyze the operation data of the hot stamping equipment during the stamping process and the production data of the stamped products after stamping in real time, resulting in the inability to accurately and quickly warn of safety accidents and production losses caused by equipment failures. Most of the existing technologies have the above problems;

[0004] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a steel hot stamping equipment damage monitoring system and method based on machine vision. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a machine vision-based damage monitoring system and method for hot stamping equipment. The present invention collects the operation data of the hot stamping equipment, the image of the stamped product after stamping, and the dimensional data of each position of the stamped product through a data acquisition terminal, imports the operation data of the hot stamping equipment into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis, imports the image of the stamped product after stamping and the dimensional data of each position of the stamped product into the stamping production abnormality analysis model to perform stamping production abnormality analysis, imports the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis, and performs stamping process abnormality warning according to the obtained stamping process abnormality analysis results. Through machine vision technology, non-contact and high-precision damage detection is realized, the subjectivity and error of manual detection is avoided, the operation status of the equipment is monitored in real time, potential damage is discovered and warned in time, and safety accidents and production losses caused by equipment failure are avoided. Through automated monitoring, the frequency and time of manual inspections are reduced, and production efficiency and equipment utilization are improved.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for monitoring damage of hot stamping equipment based on machine vision, which comprises the following specific steps:

[0007] Collect the hot stamping equipment operation data, the image of the stamped product after stamping and the dimensional data of each position of the stamped product through the data acquisition terminal;

[0008] Import the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis;

[0009] Importing the image of the stamped product after stamping and the dimension data of each position of the stamped product into the stamping production abnormality analysis model to perform stamping production abnormality analysis;

[0010] Importing the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis;

[0011] An abnormality warning of the stamping process is issued based on the abnormality analysis results of the stamping process.

[0012] It should be noted here that as a preferred technical solution for the damage monitoring method of steel hot stamping equipment based on machine vision, the specific steps of collecting the operation data of the hot stamping equipment, the image of the stamped product after stamping, and the dimensional data of each position of the stamped product through the data acquisition terminal are:

[0013] S11, collecting real-time temperature, pressure, output length and shaking amplitude data of the stamping end of the hot stamping equipment through the equipment data collection terminal, and storing them in the first storage component;

[0014] S12, collecting image data of the stamped product after stamping through the image acquisition terminal, importing the image data into the image processing software to obtain pixel data of each pixel point of the stamped product and data of length and maximum width of cracks on the surface of the stamped product, and storing them in the second storage component;

[0015] S13. Collect thickness dimension data of each position of the stamping product through the measurement and collection terminal, and store it in the third storage component.

[0016] It should be noted here that as a preferred technical solution for the damage monitoring method of hot stamping equipment based on machine vision, the method of importing the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis includes the following specific steps:

[0017] S21, obtaining the real-time temperature, pressure, output length and shaking amplitude data of the stamping end of the hot stamping equipment, and importing the obtained output length and shaking amplitude data of the stamping end of the hot stamping equipment into the first abnormal value calculation formula of the stamping equipment to calculate the first abnormal value of the stamping equipment, wherein the first abnormal value calculation formula of the stamping equipment is: Wherein, T is the stamping process time, Lt is the output length of the stamping end at time t, Ltm is the median value of the output length safety range of the stamping end at time t, Ltmax is the maximum value of the output length safety range of the stamping end at time t, Ltmin is the minimum value of the output length safety range of the stamping end at time t, a1 is the output length ratio, Ht is the shaking frequency of the stamping end at time t, Dt is the maximum shaking amplitude of the stamping end at time t, H is the standard value of the shaking frequency of the stamping end, D is the standard value of the shaking amplitude of the stamping end, and dt is the time integral;

[0018] S22, importing the acquired real-time temperature and pressure of the stamping end of the hot stamping equipment into the second abnormal value calculation formula of the stamping equipment to calculate the second abnormal value of the stamping equipment, wherein the second abnormal value calculation formula of the stamping equipment is: Wherein, Tt is the real-time temperature of the stamping end at time t, Ttm is the median value of the safe temperature range of the stamping end, Ttmax is the maximum value of the safe temperature range of the stamping end, Ttmin is the minimum value of the safe temperature range of the stamping end, a2 is the temperature ratio coefficient, Ft is the real-time pressure of the stamping end at time t, and Ftm is the standard pressure of the stamping end at time t;

[0019] S23. Obtain the first abnormal value of the stamping equipment and the second abnormal value of the stamping equipment during the stamping process, and substitute them into the operation abnormal value calculation formula to calculate the operation abnormal value, wherein the operation abnormal value calculation formula is: Yc=λS+(1-λ)Z, wherein λ is the stamping ratio coefficient.

[0020] It should be noted here that as a preferred technical solution for the damage monitoring method of steel hot stamping equipment based on machine vision, the importing of the image of the stamped product after stamping and the dimensional data of each position of the stamped product into the stamping production abnormality analysis model for stamping production abnormality analysis includes the following specific steps:

[0021] S31, obtaining pixel data of each pixel point of the stamping product and data of the length and maximum width of the cracks on the surface of the stamping product, and simultaneously obtaining thickness dimension data of each position of the stamping product;

[0022] S32, the surface of the stamped product is evenly divided into several areas, the average thickness data of each area, the pixel data of each pixel point, and the surface crack length and maximum width data are obtained, and the data are substituted into the calculation formula of the abnormal analysis value of the stamped product to calculate the abnormal analysis value of the stamped product, wherein the calculation formula of the abnormal analysis value of the stamped product is: Among them, N is the number of regions, exp() is the power of the natural constant e, yi is the number of cracks in the i-th region, cj is the length of the j-th crack in the i-th region, Lj is the maximum width of the j-th crack in the i-th region, C is the standard value of the crack length, L is the standard value of the crack width, ki is the number of pixels in the i-th region, mc is the pixel value of the c-th pixel in the i-th region, mz is the pixel standard value, di is the average thickness of the i-th region, and Dm is the thickness standard value.

[0023] It should be noted here that as a preferred technical solution for the damage monitoring method of hot stamping equipment based on machine vision, the importation of the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy for stamping process abnormality analysis includes the following specific contents:

[0024] Obtain the calculated stamping product abnormality analysis value and the calculated operation abnormality value and substitute them into the stamping process abnormality analysis formula to calculate the stamping process abnormality analysis value, wherein the stamping process abnormality analysis formula is:

[0025] Vm=(λS+(1-λ)Z)P.

[0026] It should be noted that, as a preferred technical solution for the damage monitoring method of steel hot stamping equipment based on machine vision, the abnormal warning of the stamping process according to the obtained abnormal analysis results of the stamping process includes the following specific contents:

[0027] The calculated stamping process abnormality analysis value is compared with the set stamping process abnormality analysis threshold. If the obtained stamping process abnormality analysis value is greater than or equal to the set stamping process abnormality analysis threshold, a stamping process abnormality warning is issued. If the obtained stamping process abnormality analysis value is less than the set stamping process abnormality analysis threshold, no stamping process abnormality warning is issued.

[0028] A machine vision-based steel hot stamping equipment damage monitoring system, which is implemented based on the above-mentioned machine vision-based steel hot stamping equipment damage monitoring method, and specifically includes a data acquisition module, a stamping equipment operation abnormality analysis module, a stamping production abnormality analysis module, a stamping process abnormality analysis module, a stamping process abnormality warning module and a control module, wherein the data acquisition module is used to collect hot stamping equipment operation data, images of stamped products after stamping, and dimensional data of each position of the stamped products through a data acquisition terminal;

[0029] The stamping equipment operation abnormality analysis module is used to import the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis;

[0030] The stamping production anomaly analysis module is used to import the image of the stamping product after stamping and the dimensional data of each position of the stamping product into the stamping production anomaly analysis model to perform stamping production anomaly analysis;

[0031] The stamping process abnormality analysis module is used to import the acquired stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis;

[0032] The stamping process abnormality warning module is used to issue a stamping process abnormality warning according to the stamping process abnormality analysis result obtained;

[0033] The control module is used for controlling the operation of the data acquisition module, the stamping equipment operation abnormality analysis module, the stamping production abnormality analysis module, the stamping process abnormality analysis module and the stamping process abnormality early warning module.

[0034] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0035] The processor executes the above-mentioned machine vision-based steel hot stamping equipment damage monitoring method by calling the computer program stored in the memory.

[0036] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned machine vision-based steel hot stamping equipment damage monitoring method.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention collects the operation data of hot stamping equipment, the image of the stamped product after stamping and the dimensional data of each position of the stamped product through the data acquisition terminal, imports the operation data of the hot stamping equipment into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis, imports the image of the stamped product after stamping and the dimensional data of each position of the stamped product into the stamping production abnormality analysis model to perform stamping production abnormality analysis, imports the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis, and performs stamping process abnormality warning according to the obtained stamping process abnormality analysis results. Through machine vision technology, non-contact and high-precision damage detection is realized, the subjectivity and error of manual detection is avoided, the operation status of the equipment is monitored in real time, potential damage is discovered and warned in time, safety accidents and production losses caused by equipment failure are avoided, and the frequency and time of manual inspections are reduced through automated monitoring, thereby improving production efficiency and equipment utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall process of the steel hot stamping equipment damage monitoring method based on machine vision of the present invention;

[0040] Figure 2 It is a schematic diagram of step S2 of the damage monitoring method for steel hot stamping equipment based on machine vision of the present invention;

[0041] Figure 3 It is a schematic diagram of the overall framework of the steel hot stamping equipment damage monitoring system based on machine vision of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0043] Example 1

[0044] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown, the damage monitoring method of steel hot stamping equipment based on machine vision includes the following specific steps:

[0045] S1. Collecting the operation data of the hot stamping equipment, the image of the stamped product after stamping, and the dimension data of each position of the stamped product through the data acquisition terminal;

[0046] In this embodiment, the specific steps of collecting the hot stamping equipment operation data, the image of the stamped product after stamping, and the size data of each position of the stamped product through the data acquisition terminal are:

[0047] S11, collecting real-time temperature, pressure, output length and shaking amplitude data of the stamping end of the hot stamping equipment through the equipment data collection terminal, and storing them in the first storage component;

[0048] S12, collecting image data of the stamped product after stamping through the image acquisition terminal, importing the image data into the image processing software to obtain pixel data of each pixel point of the stamped product and data of length and maximum width of cracks on the surface of the stamped product, and storing them in the second storage component;

[0049] The specific contents are as follows: Use an image acquisition terminal (such as an industrial camera) to take photos or videos of the stamped products, ensure that the light is sufficient and uniform, so as to obtain high-quality image data, and import the collected image data into the image processing software. Common image processing software includes MATLAB, OpenCV, ImageJ, etc. Preprocess the image, such as grayscale, filter denoising, contrast enhancement, etc., to improve the image quality, and use threshold segmentation, edge detection and other methods to separate the stamped products from the background. If necessary, the crack area can be further segmented, and the segmented image is analyzed to extract the pixel data of the crack area. Use image processing algorithms (such as contour detection, regional growth, etc.) to determine the length and maximum width of the crack. According to the extracted pixel data, measure the length and maximum width of the crack, convert the pixel value into the actual physical size (the resolution and scale factor of the image need to be known), and output the crack length and maximum width data as a report or directly display it on the software interface;

[0050] S13, collecting thickness dimension data of each position of the stamping product through the measurement and collection terminal, and storing it in the third storage component;

[0051] S2. Importing the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis;

[0052] In this embodiment, the operation data of the hot stamping equipment is imported into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis, which includes the following specific steps:

[0053] S21, obtaining the real-time temperature, pressure, output length and shaking amplitude data of the stamping end of the hot stamping equipment, and importing the obtained output length and shaking amplitude data of the stamping end of the hot stamping equipment into the first abnormal value calculation formula of the stamping equipment to calculate the first abnormal value of the stamping equipment, wherein the first abnormal value calculation formula of the stamping equipment is: Wherein, T is the stamping process time, Lt is the output length of the stamping end at time t, Ltm is the median value of the output length safety range of the stamping end at time t, Ltmax is the maximum value of the output length safety range of the stamping end at time t, Ltmin is the minimum value of the output length safety range of the stamping end at time t, a1 is the output length ratio, Ht is the shaking frequency of the stamping end at time t, Dt is the maximum shaking amplitude of the stamping end at time t, H is the standard value of the shaking frequency of the stamping end, D is the standard value of the shaking amplitude of the stamping end, and dt is the time integral;

[0054] It should be noted here that the comprehensive analysis of the output length and shaking amplitude data of the stamping end of the hot stamping equipment improves the efficiency and accuracy of identifying faults at the stamping end;

[0055] S22, importing the acquired real-time temperature and pressure of the stamping end of the hot stamping equipment into the second abnormal value calculation formula of the stamping equipment to calculate the second abnormal value of the stamping equipment, wherein the second abnormal value calculation formula of the stamping equipment is: Wherein, Tt is the real-time temperature of the stamping end at time t, Ttm is the median value of the safe temperature range of the stamping end, Ttmax is the maximum value of the safe temperature range of the stamping end, Ttmin is the minimum value of the safe temperature range of the stamping end, a2 is the temperature ratio coefficient, Ft is the real-time pressure of the stamping end at time t, and Ftm is the standard pressure of the stamping end at time t;

[0056] S23, obtaining the first abnormal value of the stamping equipment and the second abnormal value of the stamping equipment in the stamping process obtained by calculation and substituting them into the operation abnormal value calculation formula to calculate the operation abnormal value, wherein the operation abnormal value calculation formula is: Yc=λS+(1-λ)Z, wherein λ is the stamping proportion coefficient;

[0057] S3, importing the image of the stamped product after stamping and the dimension data of each position of the stamped product into the stamping production abnormality analysis model to perform stamping production abnormality analysis;

[0058] In this embodiment, the image of the stamped product after stamping and the dimension data of each position of the stamped product are imported into the stamping production abnormality analysis model to perform stamping production abnormality analysis, which includes the following specific steps:

[0059] S31, obtaining pixel data of each pixel point of the stamping product and data of the length and maximum width of the cracks on the surface of the stamping product, and simultaneously obtaining thickness dimension data of each position of the stamping product;

[0060] S32, the surface of the stamped product is evenly divided into several areas, the average thickness data of each area, the pixel data of each pixel point, and the surface crack length and maximum width data are obtained, and the data are substituted into the calculation formula of the abnormal analysis value of the stamped product to calculate the abnormal analysis value of the stamped product, wherein the calculation formula of the abnormal analysis value of the stamped product is: Wherein, N is the number of regions, exp() is the power of the natural constant e, yi is the number of cracks in the ith region, cj is the length of the jth crack in the ith region, Lj is the maximum width of the jth crack in the ith region, C is the standard value of the length of the crack, L is the standard value of the width of the crack, ki is the number of pixels in the ith region, mc is the pixel value of the cth pixel in the ith region, mz is the pixel standard value, di is the average thickness of the ith region, and Dm is the thickness standard value;

[0061] It should be noted that the surface of the stamped product is evenly divided into several areas. Since the thickness of each position of the stamped product is different, the damage of cracks in different thickness places has different effects on the product. Therefore, dividing it into several areas is conducive to accurate analysis of the abnormality of the surface of the stamped product. At the same time, the area here is at least 50 blocks. For example, the surface area of ​​the largest surface of the stamped product is 50 square centimeters, and the area is 50 blocks, so the area is a square grid of 1 cm*1 cm;

[0062] At the same time, the cracks in the area and the pixel data of each pixel point are distinguished and analyzed, and the abnormalities on the surface of the stamping product are accurately analyzed;

[0063] S4, importing the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis;

[0064] In this embodiment, the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results are imported into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis, including the following specific contents:

[0065] Obtain the calculated stamping product abnormality analysis value and the calculated operation abnormality value and substitute them into the stamping process abnormality analysis formula to calculate the stamping process abnormality analysis value, wherein the stamping process abnormality analysis formula is:

[0066] Vm=(λS+(1-λ)Z)P;

[0067] S5. Perform an abnormality warning for the stamping process according to the obtained abnormality analysis results of the stamping process;

[0068] In this embodiment, the stamping process abnormality warning according to the obtained stamping process abnormality analysis result includes the following specific contents:

[0069] The calculated stamping process abnormality analysis value is compared with the set stamping process abnormality analysis threshold. If the obtained stamping process abnormality analysis value is greater than or equal to the set stamping process abnormality analysis threshold, a stamping process abnormality warning is issued. If the obtained stamping process abnormality analysis value is less than the set stamping process abnormality analysis threshold, no stamping process abnormality warning is issued.

[0070] In this embodiment, the value-taking method of setting parameters such as the output length ratio coefficient, the temperature ratio coefficient, the stamping ratio coefficient and the set stamping process abnormality analysis threshold in this embodiment is: obtained according to experiments by technical personnel in this field, for example: 500 sets of hot stamping equipment operation data, images of stamped products after stamping and dimensional data of each position of the stamping products are imported into the stamping process abnormality analysis formula to calculate the stamping process abnormality analysis value, and at the same time, experts are hired to determine whether the hot stamping equipment is faulty, and the fault judgment results and the calculated stamping process abnormality analysis values ​​are substituted into the fitting software to output the values ​​of setting parameters such as the output length ratio coefficient, the temperature ratio coefficient, the stamping ratio coefficient and the set stamping process abnormality analysis threshold that meet the highest judgment accuracy.

[0071] It should be noted in the present embodiment that the advantages of the present embodiment over the prior art are as follows: the operation data of the hot stamping equipment, the image of the stamped product after stamping and the dimensional data of each position of the stamped product are collected through the data acquisition terminal, the operation data of the hot stamping equipment is imported into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis, the image of the stamping product after stamping and the dimensional data of each position of the stamping product are imported into the stamping production abnormality analysis model to perform stamping production abnormality analysis, the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results are imported into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis, and a stamping process abnormality warning is performed based on the obtained stamping process abnormality analysis results. Through machine vision technology, non-contact, high-precision damage detection is realized, the subjectivity and error of manual detection is avoided, the operation status of the equipment is monitored in real time, potential damage is discovered and warned in time, safety accidents and production losses caused by equipment failure are avoided, and the frequency and time of manual inspections are reduced through automated monitoring, thereby improving production efficiency and equipment utilization.

[0072] Example 2

[0073] like Figure 3 As shown, the steel hot stamping equipment damage monitoring system based on machine vision is implemented based on the above-mentioned steel hot stamping equipment damage monitoring method based on machine vision, which specifically includes a data acquisition module, a stamping equipment operation abnormality analysis module, a stamping production abnormality analysis module, a stamping process abnormality analysis module, a stamping process abnormality warning module and a control module, wherein the data acquisition module is used to collect the hot stamping equipment operation data, the image of the stamped product after stamping and the size data of each position of the stamped product through the data acquisition terminal;

[0074] Stamping equipment operation abnormality analysis module, used to import hot stamping equipment operation data into stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis;

[0075] A stamping production anomaly analysis module, which is used to import the image of the stamped product after stamping and the dimension data of each position of the stamped product into the stamping production anomaly analysis model to perform stamping production anomaly analysis;

[0076] A stamping process abnormality analysis module is used to import the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis;

[0077] A stamping process abnormality warning module is used to issue a stamping process abnormality warning based on the stamping process abnormality analysis results;

[0078] The control module is used for controlling the operation of the data acquisition module, the stamping equipment operation abnormality analysis module, the stamping production abnormality analysis module, the stamping process abnormality analysis module and the stamping process abnormality early warning module.

[0079] Example 3

[0080] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0081] The processor executes the above-mentioned machine vision-based steel hot stamping equipment damage monitoring method by calling the computer program stored in the memory.

[0082] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the machine vision-based steel hot stamping equipment damage monitoring method provided in the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0083] Example 4

[0084] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0085] When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned machine vision-based steel hot stamping equipment damage monitoring method.

[0086] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0087] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A damage monitoring method for steel hot stamping equipment based on machine vision, characterized in that: It includes the following specific steps: Collect the hot stamping equipment operation data, the image of the stamped product after stamping and the dimensional data of each position of the stamped product through the data acquisition terminal; Import the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis; Importing the image of the stamped product after stamping and the dimension data of each position of the stamped product into the stamping production abnormality analysis model to perform stamping production abnormality analysis; Importing the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis; An abnormality warning of the stamping process is issued based on the abnormality analysis results of the stamping process.

2. The damage monitoring method for steel hot stamping equipment based on machine vision according to claim 1, characterized in that: The method of importing the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis includes the following specific steps: The real-time temperature, pressure, output length and shaking amplitude data of the stamping end of the hot stamping equipment are obtained, and the output length and shaking amplitude data of the stamping end of the hot stamping equipment are imported into the first abnormal value calculation formula of the stamping equipment to calculate the first abnormal value of the stamping equipment, wherein the first abnormal value calculation formula of the stamping equipment is: Wherein, T is the stamping process time, Lt is the output length of the stamping end at time t, Ltm is the median value of the output length safety range of the stamping end at time t, Ltmax is the maximum value of the output length safety range of the stamping end at time t, Ltmin is the minimum value of the output length safety range of the stamping end at time t, a1 is the output length ratio, Ht is the shaking frequency of the stamping end at time t, Dt is the maximum shaking amplitude of the stamping end at time t, H is the standard value of the shaking frequency of the stamping end, D is the standard value of the shaking amplitude of the stamping end, and dt is the time integral; The acquired real-time temperature and pressure of the stamping end of the hot stamping equipment are introduced into the calculation formula of the second abnormal value of the stamping equipment to calculate the second abnormal value of the stamping equipment, wherein the calculation formula of the second abnormal value of the stamping equipment is: Wherein, Tt is the real-time temperature of the stamping end at time t, Ttm is the median value of the safe temperature range of the stamping end, Ttmax is the maximum value of the safe temperature range of the stamping end, Ttmin is the minimum value of the safe temperature range of the stamping end, a2 is the temperature ratio coefficient, Ft is the real-time pressure of the stamping end at time t, and Ftm is the standard pressure of the stamping end at time t; Obtain the first abnormal value of the stamping equipment and the second abnormal value of the stamping equipment during the stamping process, and substitute them into the operation abnormal value calculation formula to calculate the operation abnormal value, wherein the operation abnormal value calculation formula is: Yc=λS+(1-λ)Z, wherein λ is the stamping ratio coefficient.

3. The machine vision-based steel hot stamping equipment damage monitoring method according to claim 2, characterized in that: The step of importing the image of the stamped product after stamping and the dimension data of each position of the stamped product into the stamping production abnormality analysis model to perform stamping production abnormality analysis includes the following specific steps: Obtain pixel data of each pixel point of the stamped product and the length and maximum width data of the cracks on the surface of the stamped product, and at the same time obtain the thickness dimension data of each position of the stamped product; The surface of the stamped product is evenly divided into several areas, and the average thickness data of each area, the pixel data of each pixel point, the surface crack length, and the maximum width data are obtained. The data are substituted into the calculation formula of the abnormal analysis value of the stamped product to calculate the abnormal analysis value of the stamped product. The calculation formula of the abnormal analysis value of the stamped product is: Among them, N is the number of regions, exp() is the power of the natural constant e, yi is the number of cracks in the i-th region, cj is the length of the j-th crack in the i-th region, Lj is the maximum width of the j-th crack in the i-th region, C is the standard value of the crack length, L is the standard value of the crack width, ki is the number of pixels in the i-th region, mc is the pixel value of the c-th pixel in the i-th region, mz is the pixel standard value, di is the average thickness of the i-th region, and Dm is the thickness standard value.

4. The damage monitoring method for steel hot stamping equipment based on machine vision according to claim 3, characterized in that: The step of importing the obtained stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis includes the following specific contents: Obtain the calculated stamping product abnormality analysis value and the calculated operation abnormality value and substitute them into the stamping process abnormality analysis formula to calculate the stamping process abnormality analysis value, wherein the stamping process abnormality analysis formula is: Vm=(λS+(1-λ)Z)P.

5. The machine vision-based steel hot stamping equipment damage monitoring method according to claim 4, characterized in that: The abnormality warning of the stamping process according to the obtained abnormality analysis result of the stamping process includes the following specific contents: The calculated stamping process abnormality analysis value is compared with the set stamping process abnormality analysis threshold. If the obtained stamping process abnormality analysis value is greater than or equal to the set stamping process abnormality analysis threshold, a stamping process abnormality warning is issued. If the obtained stamping process abnormality analysis value is less than the set stamping process abnormality analysis threshold, no stamping process abnormality warning is issued.

6. The machine vision-based steel hot stamping equipment damage monitoring method according to claim 5, characterized in that: The specific steps of collecting the hot stamping equipment operation data, the image of the stamped product after stamping and the dimension data of each position of the stamped product through the data acquisition terminal are: The real-time temperature, pressure, output length and shaking amplitude data of the stamping end of the hot stamping equipment are collected through the equipment data collection terminal, and stored in the first storage component; The image data of the stamped product after stamping is collected through the image acquisition terminal, and the image data is imported into the image processing software to obtain the pixel data of each pixel point of the stamped product and the length and maximum width data of the cracks on the surface of the stamped product, and stored in the second storage component; The thickness dimension data of each position of the stamping product is collected by the measuring and collecting terminal and stored in the third storage component.

7. A machine vision-based steel hot stamping equipment damage monitoring system, which is implemented based on the machine vision-based steel hot stamping equipment damage monitoring method according to any one of claims 1 to 6, characterized in that: It specifically includes a data acquisition module, a stamping equipment operation abnormality analysis module, a stamping production abnormality analysis module, a stamping process abnormality analysis module, a stamping process abnormality warning module and a control module, wherein the data acquisition module is used to collect hot stamping equipment operation data, images of stamped products after stamping and dimensional data of each position of the stamped products through a data acquisition terminal; The stamping equipment operation abnormality analysis module is used to import the hot stamping equipment operation data into the stamping equipment operation abnormality analysis strategy to perform stamping equipment operation abnormality analysis; The stamping production anomaly analysis module is used to import the image of the stamping product after stamping and the dimensional data of each position of the stamping product into the stamping production anomaly analysis model to perform stamping production anomaly analysis; The stamping process abnormality analysis module is used to import the acquired stamping equipment operation abnormality analysis results and stamping production abnormality analysis results into the stamping process abnormality analysis strategy to perform stamping process abnormality analysis; The stamping process abnormality warning module is used to issue a stamping process abnormality warning according to the stamping process abnormality analysis result obtained; The control module is used for controlling the operation of the data acquisition module, the stamping equipment operation abnormality analysis module, the stamping production abnormality analysis module, the stamping process abnormality analysis module and the stamping process abnormality early warning module.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the machine vision-based steel hot stamping equipment damage monitoring method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the machine vision-based steel hot stamping equipment damage monitoring method as described in any one of claims 1 to 6.

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