Metal sheet optimization processing method and system based on automatic deviation correction

Abstract

The present application provides a metal sheet optimization processing method and system based on automatic deviation correction, which relates to the field of metal sheet processing technology, including: configuring a cutting scale threshold, collecting multi-dimensional images of the sheet, constructing a three-dimensional model of the sheet, performing cutting simulation on the three-dimensional model of the sheet, obtaining a first metal sheet cutting prediction model, obtaining a first deviation scale feature of the first metal sheet cutting prediction model and a metal sheet cutting standard model, and when any one of the first deviation scale features is greater than the cutting scale threshold, performing optimization, obtaining a recommended fixed position mark, and performing automatic deviation correction control of the metal sheet. The present application can solve the technical problem in the prior art that the cutting accuracy does not meet the standard in scenarios with high precision requirements due to the fact that traditional metal sheet cutting does not take into account the tiny displacements and cutting control offsets in actual operations. By fine-tuning the clamping position and re-adjusting the cutting control parameters, the technical effect of achieving high-precision cutting control is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of metal sheet processing, and in particular to a metal sheet optimization processing method and system based on automatic deviation correction. Background Art

[0002] With the development of industrial automation technology, the cutting of metal sheets is increasingly widely used in the manufacturing industry. Metal sheet cutting is an important part of metal processing, and its precision and efficiency directly affect the quality of the final product. In the traditional metal sheet cutting process, fixed clamping positions and preset cutting control parameters are usually used for cutting. This method can meet the needs of general industrial production to a certain extent, but it has obvious limitations in application scenarios with high precision requirements. Traditional cutting methods fail to fully consider the small scale deviations that may be caused by clamping displacement and cutting control offset. These small deviations accumulate during the cutting process, resulting in the dimensional accuracy of the final product not meeting the high precision requirements. Especially in the fields of aerospace, precision instrument manufacturing, etc., these small deviations may have a significant impact on the performance of the product. In order to meet the needs of high-precision cutting, some existing technologies attempt to solve the problem by improving the accuracy of cutting equipment or improving the planning of cutting paths. However, these methods often require expensive equipment upgrades or complex algorithm adjustments, and are still limited in effect for small scale deviations caused by clamping displacement and cutting control offset.

[0003] In summary, there is a technical problem in the prior art that the cutting accuracy does not meet the standard in scenarios with high precision requirements because the traditional metal sheet cutting does not take into account the tiny displacement and cutting control offset in actual operation. Summary of the invention

[0004] The purpose of this application is to provide a metal sheet optimization processing method and system based on automatic deviation correction, so as to solve the technical problem in the prior art that the cutting accuracy does not meet the standard in scenarios with high precision requirements because the traditional metal sheet cutting does not take into account the tiny displacement and cutting control offset in actual operation.

[0005] In view of the above problems, the present application provides a metal sheet optimization processing method and system based on automatic deviation correction.

[0006] In the first aspect, the present application provides a metal sheet optimization processing method based on automatic deviation correction, and the metal sheet optimization processing method based on automatic deviation correction is implemented by a metal sheet optimization processing system based on automatic deviation correction, wherein the metal sheet optimization processing method based on automatic deviation correction includes: configuring a cutting scale threshold through a user end; when the metal sheet starts to be cut, collecting a multi-dimensional image of the sheet through an image sensor; constructing a three-dimensional model of the sheet based on the multi-dimensional image of the sheet, wherein the three-dimensional model of the sheet has a fixed position identifier; according to the sheet cutting control parameters and the fixed position identifier, performing cutting simulation on the three-dimensional model of the sheet in a digital twin model of a metal sheet cutting equipment to obtain a first metal sheet cutting prediction model; obtaining a first deviation scale feature of the first metal sheet cutting prediction model and a metal sheet cutting standard model; when any one of the first deviation scale features is greater than the cutting scale threshold, optimizing the fixed position identifier to obtain a recommended fixed position identifier; and performing automatic deviation correction control of the metal sheet according to the recommended fixed position identifier.

[0007] In a second aspect, the present application further provides a metal sheet optimization processing system based on automatic deviation correction, which is used to execute the metal sheet optimization processing method based on automatic deviation correction as described in the first aspect, wherein the metal sheet optimization processing system based on automatic deviation correction includes: a threshold configuration module, the threshold configuration module is used to configure the cutting scale threshold through the user end; an image acquisition module, the image acquisition module is used to collect a multi-dimensional image of the plate through an image sensor when the metal plate starts to be cut; a three-dimensional modeling module, the three-dimensional modeling module is used to construct a three-dimensional model of the plate according to the multi-dimensional image of the plate, wherein the three-dimensional model of the plate has a fixed position mark; a cutting simulation module, the cutting simulation module The block is used to perform cutting simulation on the three-dimensional model of the plate in the digital twin model of the metal plate cutting equipment according to the plate cutting control parameters and the fixed position identifier, so as to obtain a first metal plate cutting prediction model; a deviation analysis module, the deviation analysis module is used to obtain the first deviation scale characteristics of the first metal plate cutting prediction model and the metal plate cutting standard model; an optimization module, the optimization module is used to optimize the fixed position identifier when any one of the first deviation scale characteristics is greater than the cutting scale threshold, so as to obtain a recommended fixed position identifier; an automatic deviation correction control module, the automatic deviation correction control module is used to perform automatic deviation correction control of the metal plate according to the recommended fixed position identifier.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] Through the user end, configure the cutting scale threshold; when the metal sheet starts to be cut, collect the multi-dimensional image of the sheet through the image sensor; construct a three-dimensional model of the sheet according to the multi-dimensional image of the sheet, wherein the three-dimensional model of the sheet has a fixed position mark; according to the sheet cutting control parameters and the fixed position mark, perform cutting simulation on the three-dimensional model of the sheet in the digital twin model of the metal sheet cutting equipment to obtain a first metal sheet cutting prediction model; obtain the first deviation scale feature of the first metal sheet cutting prediction model and the metal sheet cutting standard model; when any one of the first deviation scale features is greater than the cutting scale threshold, optimize the fixed position mark to obtain a recommended fixed position mark; perform automatic deviation correction control on the metal sheet according to the recommended fixed position mark. In other words, by fine-tuning the clamping position and re-adjusting the cutting control parameters, the technical effect of achieving high-precision cutting control is achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 This is a schematic diagram of the process flow of the metal sheet optimization processing method based on automatic deviation correction in this application.

[0013] Figure 2 This is a schematic diagram of the structure of the metal sheet optimization processing system based on automatic deviation correction in this application.

[0014] Explanation of the accompanying drawings: threshold configuration module 11, image acquisition module 12, three-dimensional modeling module 13, cutting simulation module 14, deviation analysis module 15, optimization module 16, automatic deviation correction control module 17. DETAILED DESCRIPTION

[0015] This application provides a metal sheet optimization processing method and system based on automatic deviation correction, which solves the technical problem in the prior art that the cutting accuracy does not meet the standard in scenes with high precision requirements because the traditional metal sheet cutting does not take into account the tiny displacement and cutting control deviation in actual operation. By fine-tuning the clamping position and re-adjusting the cutting control parameters, the technical effect of achieving high-precision cutting control is achieved.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a metal sheet optimization processing method based on automatic deviation correction, wherein the metal sheet optimization processing method based on automatic deviation correction is applied to a metal sheet optimization processing system based on automatic deviation correction, and the metal sheet optimization processing method based on automatic deviation correction specifically includes the following steps:

[0018] Step 1: Configure the cutting scale threshold through the user end.

[0019] Specifically, first, provide a user-friendly interface, which can be a physical control panel or a computer software interface, allowing users to enter and adjust the cutting scale threshold. This interface should be clear and intuitive, easy for users to understand and operate. The user enters the setting menu through the software interface of the cutting device and finds the cutting scale threshold configuration option. Then, according to the material, thickness, cutting requirements and other factors of the metal sheet, set the appropriate cutting scale threshold to determine the allowable error range of the sheet size during the cutting process. For thicker metal sheets, users may need to set a larger cutting scale threshold to allow for greater deviations; while for thinner metal sheets, users may need to set a smaller cutting scale threshold to achieve higher cutting accuracy. For example, if the user needs to cut a metal sheet with a dimensional accuracy of ±0.5 mm, then in the configuration interface, the user sets the minimum threshold to -0.5 mm and the maximum threshold to +0.5 mm. After completing the settings, the user clicks the Save or Apply button, and the system applies these thresholds to the cutting control logic. According to the specific needs of the user, the cutting accuracy is customized to better adapt to different processing scenarios and accuracy requirements.

[0020] Step 2: When the metal sheet starts to be cut, the image sensor is used to collect a multi-dimensional image of the sheet.

[0021] Specifically, an image sensor is a device that can convert light into electronic signals. During the metal sheet cutting process, the image sensor is used to capture the visual information of the sheet, including its appearance features and geometric shape. Choose a suitable image sensor according to the needs, such as a high-resolution camera, a 3D scanner, or a line scan camera. Install the image sensor in the appropriate position of the cutting equipment to ensure that all angles and dimensions of the sheet can be clearly captured. At the same time as the sheet starts to be cut, the image sensor begins to collect multi-dimensional images of the sheet in real time, including images of the front, side, and top surfaces of the sheet, which can provide detailed information about the overall shape and size of the sheet. For example, the camera captures the image of the sheet from different angles at the same time; the 3D scanner obtains the depth information of the sheet by emitting lasers or other forms of scanning signals; the line scan camera or other continuous acquisition device continuously captures images along the moving direction of the sheet. The collected image data needs to be processed for subsequent analysis and use. Data processing may include steps such as image enhancement, filtering, and feature extraction to improve the clarity and analyzability of the image. Through multi-dimensional image acquisition, the actual state of the sheet can be understood more accurately, thereby improving cutting accuracy.

[0022] Step three: constructing a three-dimensional model of the plate according to the multi-dimensional image of the plate, wherein the three-dimensional model of the plate has a fixed position mark.

[0023] Specifically, based on the collected multi-dimensional images, computer vision and image processing technology are used to construct a three-dimensional model of the plate. The three-dimensional model can more accurately reflect the shape and size of the plate, which helps to improve the accuracy and efficiency of cutting. In the constructed three-dimensional model, fixed position markers are set. These markers can be used in the subsequent cutting process to ensure accuracy and consistency during the cutting process. Fixed position markers can be specific points or lines on the plate to determine the starting point and direction of cutting. In the automated cutting system, fixed position markers serve as important reference points, making the programming and execution of the cutting path more automated and accurate.

[0024] Step 4: According to the plate cutting control parameters and the fixed position mark, the three-dimensional model of the plate is cut and simulated in the digital twin model of the metal plate cutting equipment to obtain a first metal plate cutting prediction model.

[0025] Specifically, according to the material, thickness, cutting requirements and other factors of the metal sheet, setting the plate cutting control parameters, including cutting speed, cutting depth, feed speed, etc., is crucial for the smooth progress of the cutting process. A digital twin model that fully corresponds to the actual metal sheet cutting equipment is established to simulate all functions and operations of the actual equipment. Ensure that the parameters in the digital twin model are consistent with the parameters of the actual equipment, including cutting speed, feed rate, specifications of cutting tools, etc. According to the plate cutting control parameters and fixed position identification, the three-dimensional model of the plate is cut in the digital twin model. Cutting simulation is an important technical means. It simulates the real cutting process through the digital twin model, predicts and optimizes the cutting parameters, thereby improving the cutting quality and efficiency. Cutting simulation can predict the tool path, chip formation, stress distribution, etc. during the cutting process, so as to optimize the cutting parameters and process. Through cutting simulation, the actual cutting process is simulated, and all data in the simulation process are monitored and recorded. Analyze the simulation results to identify potential problems and errors in the cutting process. Based on the simulation data and results, the first metal sheet cutting prediction model is obtained. This predictive model can reflect various conditions in the cutting process, including cutting path, chip formation, stress distribution, etc., which helps to optimize cutting parameters and processes. Cutting simulation through digital twin models can improve the accuracy and efficiency of metal sheet cutting.

[0026] Step 5: Obtaining first deviation scale features of the first metal sheet cutting prediction model and the metal sheet cutting standard model.

[0027] Specifically, according to the cutting process under ideal conditions, theoretical calculations are performed to determine the ideal state of the metal sheet cutting process, including cutting path, cutting depth, etc. A standard model for metal sheet cutting is created, which reflects the expected cutting results under optimal conditions. The first metal sheet cutting prediction model is compared with the metal sheet cutting standard model, and the deviation scale characteristics between the two are calculated. The deviation scale characteristics can reflect the differences between the prediction model and the standard model, including position deviation, size deviation, shape deviation, etc. By comparing the prediction model and the standard model, deviations in the cutting process can be identified, and measures can be taken to adjust them, thereby improving cutting accuracy.

[0028] Step 6: When any one of the first deviation scale features is greater than the cutting scale threshold, the fixed position marker is optimized to obtain a recommended fixed position marker.

[0029] Specifically, when any feature of the first deviation scale feature is greater than the cutting scale threshold, it means that the fixed position identifier needs to be optimized, which means that there is a large deviation between the actual cutting result and the expected standard, which is caused by equipment performance problems, improper cutting parameter settings or environmental factors. The fixed position identifier is adjusted, and an optimization algorithm, such as genetic algorithm, simulated annealing, etc., is used to find the best fixed position identifier. During the optimization process, the optimization algorithm will try different combinations of fixed position identifiers for cutting simulation and evaluate their impact on cutting deviation. Through iterative calculation, the algorithm will gradually converge to the best fixed position identifier. The deviation scale feature between the prediction model and the standard model is guided to be less than or equal to the cutting scale threshold, and the fixed position adjustment result at this time is set as the recommended fixed position identifier. The recommended fixed position identifier obtained through optimization helps to improve the quality and efficiency of the metal sheet cutting process.

[0030] Step 7: Automatically correct the deviation of the metal sheet according to the recommended fixed position mark.

[0031] Specifically, the recommended fixed position mark contains information such as the optimal plate fixing position and cutting starting point, which is used to guide the cutting equipment on how to adjust the position of the plate in actual operation to reduce or eliminate deviations. According to the recommended fixed position mark, the cutting equipment automatically adjusts the position of the plate, moves the support device of the plate, changes the inclination angle of the plate, or adjusts the position of the cutting head. At the same time, the cutting equipment adjusts the cutting parameters such as cutting speed and feed speed according to the recommended fixed position mark to adapt to the new plate position. During the adjustment process, the equipment monitors the position and cutting parameters of the plate in real time to ensure that they meet the requirements of the recommended fixed position mark. Once all adjustments are completed, the equipment performs the cutting operation according to the optimized parameters and position. Through automatic deviation correction control, the metal sheet cutting equipment can automatically adjust to reduce deviations during the cutting process, thereby improving production quality and efficiency, and providing a flexible response to various situations that may occur during the production process.

[0032] Further, step 2 of this application includes:

[0033] An error analysis is performed on the metal sheet cutting equipment to obtain a cutting control statistical error, wherein the cutting control statistical error includes a statistical system error and a statistical random error; when the statistical random error is less than the cutting scale threshold, the initial control parameters for sheet cutting are fitted according to the statistical system error to generate sheet cutting control parameters; when the statistical random error is greater than or equal to the cutting scale threshold, an equipment debugging instruction is generated and sent to the user end.

[0034] Specifically, the performance data of the cutting equipment over a period of time is collected and analyzed to obtain the statistical error of cutting control. The statistical error of cutting control usually includes two parts: statistical systematic error and statistical random error. Error analysis is a process of quantitatively evaluating the difference between the output results of the equipment or system and the expected results to determine the source, size and distribution of these differences. Statistical systematic error is an error caused by the inherent design or manufacturing defects of the cutting equipment. This error is fixed and predictable during multiple cutting processes. For example, if there is a deviation in the installation position of the blade of the cutting equipment, each cutting will produce an error in the same direction. Statistical systematic error can be reduced or eliminated by calibrating the equipment or adjusting the cutting parameters. Statistical random error is an error caused by unpredictable factors in the cutting process, such as material non-uniformity, ambient temperature changes, etc. This error varies randomly during multiple cutting processes. Statistical random error cannot usually be completely eliminated, but its impact can be reduced through control measures, such as using more stable materials and improving the working environment.

[0035] A cutting scale threshold is set according to the material, thickness, cutting requirements and other factors of the metal sheet. This threshold will be used as a standard to judge whether the deviation is acceptable in the subsequent cutting process. When the statistical random error is less than the cutting scale threshold, it means that the influence of random factors in the cutting process on the cutting accuracy is within an acceptable range. In this case, the initial control parameters for sheet cutting can be fitted according to the statistical systematic error to generate a set of new cutting control parameters that can compensate or correct the systematic error, thereby improving the cutting accuracy. The fitting process involves using statistical methods or algorithms to adjust cutting parameters such as cutting speed, feed rate and cutting path to compensate for the systematic error. For example, if the cutting equipment has a systematic error in the horizontal direction, resulting in a deviation in the cutting line, this error can be corrected by adjusting the compensation value of the cutting path.

[0036] When the statistical random error is greater than or equal to the cutting scale threshold, it indicates that the influence of random factors in the cutting process on the cutting accuracy exceeds the acceptable range. In this case, the cutting equipment needs to be adjusted or maintained to reduce the influence of random errors. In order to guide users to debug the equipment, equipment debugging instructions can be generated, including specific debugging steps, parameters to be adjusted, reference values, etc., to guide users to make necessary adjustments or maintenance to the cutting equipment to improve its performance. The debugging instructions will be sent to the user end, and the operator or maintenance personnel can adjust the equipment according to these instructions, such as calibrating sensors, replacing worn parts, or adjusting equipment settings. For example, if it is found that the cutting error is related to the temperature of the equipment, the debugging instructions may include steps to adjust the cooling system of the equipment. In this way, users can make targeted adjustments to the cutting equipment according to the debugging instructions to reduce the influence of random errors, thereby improving cutting accuracy and consistency. At the same time, this also helps to improve the reliability and stability of the equipment, reduce downtime and maintenance costs.

[0037] Furthermore, the present application also includes the following steps:

[0038] Collect a set of first cutting action error monitoring values ​​of a preset historical time zone of the metal sheet cutting equipment, wherein the preset historical time zone represents the time from the last time the equipment debugging was completed to the present time; perform box plot analysis on the first cutting action error monitoring value set to obtain concentrated first cutting action error monitoring values; extract the maximum value of the concentrated first cutting action error monitoring values ​​and set it as a first characteristic error vector; extract the minimum value of the concentrated first cutting action error monitoring values ​​and set it as a second characteristic error vector; perform error analysis based on the first characteristic error vector and the second characteristic error vector to obtain a first cutting action control statistical error, and add it into the cutting control statistical error.

[0039] Specifically, first, a time range needs to be determined, that is, from the last time the equipment debugging was completed to the current time. This time range can be set according to the equipment debugging cycle, production plan or maintenance cycle. For example, if the equipment debugging cycle is once a quarter, then the preset historical time zone can be the length of the most recent quarter. In this preset historical time zone, it is necessary to collect the first cutting action error monitoring value of the equipment during the cutting process, including cutting position deviation, cutting speed fluctuation, cutting force change, etc., which can be obtained through the built-in sensor of the equipment or external detection equipment. The collected error monitoring values ​​are sorted into a data set for subsequent analysis and processing. Ensure that the collected first cutting action error monitoring value set is complete and there are no missing or erroneous data points. If there are missing values ​​or outliers in the data set, data cleaning is required first.

[0040] Use statistical software or data analysis tools, such as Excel, R, Python, etc., to draw a box plot of the first cutting action error monitoring value. The box plot will show the distribution of the data, including the median, quartiles, and possible outliers. In a box plot, there are usually "tentacles" or "whiskers" that extend to the minimum and maximum values ​​of the data. Points beyond the tentacles are potential outliers. These outliers may be caused by equipment failure, operating errors, or other external factors. Box plot analysis is a statistical method used to identify outliers in data and evaluate the distribution of data. A box plot, also known as a box and whisker plot, is a chart that shows the distribution of data. It shows the minimum value, first quartile, median, third quartile, and maximum value of the data. Through box plot analysis, you can understand the distribution of the middle 50% of the data, that is, the first quartile to the third quartile, as well as the extreme values ​​of the data. Identify the central tendency in the data, that is, the interval where most of the error monitoring values ​​are located, and exclude those abnormal error values ​​to obtain the concentrated first cutting action error monitoring value.

[0041] Extract the maximum and minimum values ​​of the error monitoring values ​​of the first cutting action in the set. These two values ​​represent the maximum deviation and the minimum deviation in the data, respectively. The maximum value is set as the first characteristic error vector, and the minimum value is set as the second characteristic error vector. Analyze the first characteristic error vector and the second characteristic error vector, and calculate the control statistical error of the first cutting action according to the analysis results of the characteristic error vector. Similarly, each other cutting action is subjected to error analysis according to the above steps to obtain the control statistical error of each cutting action. These error values ​​reflect the performance stability of each cutting action, including systematic errors and random errors. Add the control statistical error of each cutting action to the cutting control statistical error. This data set contains all error information from the moment the equipment is debugged to the present moment, which can be used to evaluate the long-term performance and stability of the equipment. Through this process, the metal sheet cutting processing plant can more accurately evaluate the performance stability of the equipment, and optimize and adjust each cutting action, thereby improving the cutting accuracy and consistency and reducing the production of unqualified products.

[0042] Furthermore, the present application also includes the following steps:

[0043] Calculate the mean of the first characteristic error vector and the second characteristic error vector, and set it as the first cutting action system error; trace back to the concentrated first cutting action error monitoring value according to the first cutting action system error to perform statistics, and obtain the first cutting action random error; add the first cutting action system error into the statistical system error, add the first cutting action random error into the statistical random error, store them as the first cutting action control statistical error, and add them into the cutting control statistical error.

[0044] Specifically, the first characteristic error vector and the second characteristic error vector are determined, the mean of the two characteristic error vectors is calculated, and the calculated mean is used as the first cutting action systematic error, because it reflects the fixed deviation of the equipment under normal working conditions. The mean is the sum of all values ​​divided by the number of values. For example, if the first characteristic error vector is +3 and the second characteristic error vector is -1, the mean is calculated as (3-1) / 2=1, that is, the systematic error is +1. According to the first cutting action systematic error, trace back to the centralized first cutting action error monitoring value set and perform statistical analysis to obtain the first cutting action random error. Subtract the systematic error from each cutting action error monitoring value to eliminate the inherent deviation of the equipment. In this way, a series of adjusted error values ​​are obtained, which reflect the errors caused by random factors, that is, the first cutting action random error. The first cutting action systematic error is added to the statistical systematic error, and the first cutting action random error is added to the statistical random error. The first cutting action control statistical error is obtained, which consists of two parts: the systematic error and the random error. The first cutting action control statistical error is stored and added to the cutting control statistical error. By calculating the mean of the first characteristic error vector and the second characteristic error vector, and tracing back to the original error monitoring value based on these error vectors for statistics, the systematic error and random error of the first cutting action are obtained, providing a comprehensive error analysis for the equipment, allowing the operator to understand the performance of the equipment more accurately and adjust and optimize the cutting process based on these statistical information.

[0045] Furthermore, step six of this application includes:

[0046] Obtain a plate deviation vector that does not meet the cutting scale threshold, wherein the plate deviation vector has a vector modulus and a vector direction; set the plate movement direction in the opposite direction of the vector direction; set the vector modulus as the plate movement constraint step; configure a single movement step; adjust the fixed position identifier according to the plate movement direction and the plate movement constraint step, combined with the single movement step, to obtain a fixed position adjustment result; perform cutting simulation on the plate three-dimensional model in the digital twin model of the metal plate cutting equipment according to the plate cutting control parameter and the fixed position adjustment result, to obtain a second metal plate cutting prediction model; when the second deviation scale characteristics of the second metal plate cutting prediction model and the metal plate cutting standard model are both less than or equal to the cutting scale threshold, set the fixed position adjustment result as the recommended fixed position identifier.

[0047] Specifically, the deviation values ​​of the plate in multiple directions are obtained through various measurement methods (such as laser scanning, mechanical measurement, etc.). The initial plate deviation vector contains multiple deviation values, and these deviation values ​​are synthesized to obtain a composite vector that comprehensively reflects the overall deviation of the plate. By calculating the composite vector, a plate deviation vector that comprehensively reflects the overall deviation of the plate is obtained. Determine whether this plate deviation vector meets the cutting scale threshold. If the vector modulus of the plate deviation vector exceeds the set cutting scale threshold, it is considered that the deviation of this plate exceeds the acceptable range, that is, it does not meet the cutting scale threshold. Record the plate deviation vector that does not meet the cutting scale threshold, which includes the vector modulus and vector direction. The vector modulus indicates the magnitude of the deviation, and the vector direction indicates the direction of the deviation.

[0048] The opposite direction of the plate deviation vector is used as the direction of plate movement. The plate needs to move in the opposite direction of reducing the deviation in order to correct the deviation. If the direction of the plate deviation vector is from one end of the plate to the other end, then in order to reduce the deviation, the plate should move in the opposite direction of this vector, that is, from the end with larger deviation to the end with smaller deviation. The modulus of the plate deviation vector (that is, the size of the vector) is set as the constraint step of the plate movement, which determines the degree to which the plate can reduce the deviation each time it moves. According to factors such as the material, thickness, cutting requirements, and the previously set plate movement constraint step, a suitable single movement step is configured. This step should be a reasonable value that can effectively reduce the deviation without causing the plate to move too much and affect the cutting accuracy. Combine the configured single movement step with the plate movement direction and the plate movement constraint step. Each time the plate moves, it will move in the opposite direction of reducing the deviation, and the distance moved is the single movement step. According to the plate movement direction and the single movement step, the fixed position mark is adjusted to obtain the fixed position adjustment result. The fixed position mark is an important reference point used to determine the cutting starting point and direction during the cutting process. The fixed position adjustment result reflects the new position of the plate after each movement, as well as the direction and step length of the plate movement. By adjusting the fixed position mark, it can be ensured that the plate moves in the correct direction every time it moves, thereby achieving precise automatic deviation control.

[0049] According to the plate cutting control parameters and the new fixed position adjustment results, the parameters in the digital twin model of the metal plate cutting equipment are updated, and the cutting simulation is performed again to obtain the second metal plate cutting prediction model. The second metal plate cutting prediction model is compared with the metal plate cutting standard model to obtain the second deviation scale characteristics, including position deviation, size deviation, shape deviation, etc. If the second deviation scale characteristics are all less than or equal to the cutting scale threshold, this means that the difference between the actual cutting result and the expected standard is within an acceptable range, indicating that the correction effect is good. The fixed position adjustment result is set as the recommended fixed position mark, which can be used in the subsequent actual cutting process to ensure the accuracy and consistency of the cutting. Through automatic correction control, the deviation in the plate cutting process is reduced, and cutting simulation and deviation comparison are performed multiple times to continuously optimize the fixed position adjustment results and improve the cutting effect.

[0050] Furthermore, the present application also includes the following steps:

[0051] An initial plate deviation vector is obtained; when there are multiple initial plate deviation vectors, a vector combination of the initial plate deviation vectors is obtained to obtain the plate deviation vector.

[0052] Specifically, first, various measurement methods (such as laser scanning, mechanical measurement, etc.) are used to obtain the deviation values ​​of the plate in multiple directions. These deviation values ​​constitute the initial plate deviation vector, which may include length deviation, width deviation, thickness deviation, etc. When there are deviations in multiple directions, these deviation vectors need to be synthesized to obtain a composite vector that comprehensively reflects the overall deviation of the plate. Usually, the composite vector is the geometric or arithmetic sum of these vectors, which means that the size and direction of each deviation vector are combined to form a comprehensive deviation vector. Multiple initial plate deviation vectors are summed to obtain a comprehensive plate deviation vector. After obtaining the synthesized plate deviation vector, it is necessary to determine whether this vector meets the cutting scale threshold. The plate deviation vector is usually represented by a point in a three-dimensional coordinate system, which contains the size and direction of the deviation. By obtaining the plate deviation vector, the initial state of the plate can be understood more accurately, which helps to optimize the cutting parameters and process.

[0053] Furthermore, the present application also includes the following steps:

[0054] When any second deviation scale feature between the second metal sheet cutting prediction model and the metal sheet cutting standard model is greater than the cutting scale threshold, the plate cutting control parameter is adjusted to obtain the plate cutting control adjustment parameter; according to the plate cutting control adjustment parameter and the fixed position adjustment result, the cutting simulation of the plate three-dimensional model is performed in the digital twin model of the metal sheet cutting equipment to obtain a third metal sheet cutting prediction model; when the third deviation scale feature between the third metal sheet cutting prediction model and the metal sheet cutting standard model is less than or equal to the cutting scale threshold, the fixed position adjustment result is set as the recommended fixed position identifier, wherein the recommended fixed position identifier has a plate cutting control adjustment parameter label.

[0055] Specifically, after each movement, the deviation between the actual position and the ideal position of the sheet is detected again. The deviation after the movement is analyzed, and the control parameters are adjusted again to improve the convergence probability. This is because the shape and size of the sheet may change during the actual cutting process, and the cutting parameters need to be continuously adjusted to adapt to these changes. If any of the second deviation scale features of the second metal sheet cutting prediction model and the metal sheet cutting standard model is greater than the cutting scale threshold, it indicates that the adjusted cutting process still does not meet expectations and needs to be further adjusted. Adjust the sheet cutting control parameters, such as cutting speed, cutting depth, feed speed, etc. For example, if the prediction model shows that the cutting speed is too fast and causes too much chips, you can consider reducing the cutting speed; if the prediction model shows that the cutting depth is insufficient, you can increase the cutting depth. According to the sheet cutting control adjustment parameters and the fixed position adjustment results, the three-dimensional model of the sheet is cut and simulated in the digital twin model to obtain the third metal sheet cutting prediction model. When the third deviation scale feature of the third metal sheet cutting prediction model and the third metal sheet cutting standard model is less than or equal to the cutting scale threshold, the fixed position adjustment result is set as the recommended fixed position mark, wherein the recommended fixed position mark has a plate cutting control adjustment parameter label, which means that the difference between the prediction model and the standard model is within an acceptable range and the performance of the cutting process meets the requirements. The metal sheet cutting equipment automatically adjusts the control parameters to reduce the deviation in the cutting process, thereby improving production quality and efficiency.

[0056] In summary, the metal sheet optimization processing method based on automatic deviation correction provided by the present application has the following technical effects:

[0057] Through the user end, configure the cutting scale threshold; when the metal sheet starts to be cut, collect the multi-dimensional image of the sheet through the image sensor; construct a three-dimensional model of the sheet according to the multi-dimensional image of the sheet, wherein the three-dimensional model of the sheet has a fixed position mark; according to the sheet cutting control parameters and the fixed position mark, perform cutting simulation on the three-dimensional model of the sheet in the digital twin model of the metal sheet cutting equipment to obtain a first metal sheet cutting prediction model; obtain the first deviation scale feature of the first metal sheet cutting prediction model and the metal sheet cutting standard model; when any one of the first deviation scale features is greater than the cutting scale threshold, optimize the fixed position mark to obtain a recommended fixed position mark; perform automatic deviation correction control on the metal sheet according to the recommended fixed position mark. In other words, by fine-tuning the clamping position and re-adjusting the cutting control parameters, the technical effect of achieving high-precision cutting control is achieved.

[0058] Embodiment 2, based on the same inventive concept as the metal sheet optimization processing method based on automatic deviation correction in the above embodiment, the present application also provides a metal sheet optimization processing system based on automatic deviation correction, please refer to the attached Figure 2 , the metal sheet optimization processing system based on automatic deviation correction includes:

[0059] The threshold configuration module 11 is used to configure the cutting scale threshold through the user terminal.

[0060] The image acquisition module 12 is used to acquire a multi-dimensional image of the metal sheet through an image sensor when the metal sheet starts to be cut.

[0061] The three-dimensional modeling module 13 is used to construct a three-dimensional model of the plate according to the multi-dimensional image of the plate, wherein the three-dimensional model of the plate has a fixed position mark.

[0062] The cutting simulation module 14 is used to perform cutting simulation on the three-dimensional model of the sheet metal in the digital twin model of the metal sheet metal cutting equipment according to the sheet metal cutting control parameters and the fixed position mark, so as to obtain a first metal sheet metal cutting prediction model.

[0063] The deviation analysis module 15 is used to obtain the first deviation scale feature of the first metal sheet cutting prediction model and the metal sheet cutting standard model.

[0064] The optimization module 16 is used to optimize the fixed position identifier to obtain a recommended fixed position identifier when any one of the first deviation scale features is greater than the cutting scale threshold.

[0065] The automatic deviation correction control module 17 is used to perform automatic deviation correction control on the metal sheet according to the recommended fixed position mark.

[0066] Furthermore, the image acquisition module 12 in the metal sheet optimization processing system based on automatic deviation correction is also used for:

[0067] An error analysis is performed on the metal sheet cutting equipment to obtain a cutting control statistical error, wherein the cutting control statistical error includes a statistical system error and a statistical random error; when the statistical random error is less than the cutting scale threshold, the initial control parameters for sheet cutting are fitted according to the statistical system error to generate sheet cutting control parameters; when the statistical random error is greater than or equal to the cutting scale threshold, an equipment debugging instruction is generated and sent to the user end.

[0068] Furthermore, the image acquisition module 12 in the metal sheet optimization processing system based on automatic deviation correction is also used for:

[0069] Collect a set of first cutting action error monitoring values ​​of a preset historical time zone of the metal sheet cutting equipment, wherein the preset historical time zone represents the time from the last time the equipment debugging was completed to the present time; perform box plot analysis on the first cutting action error monitoring value set to obtain concentrated first cutting action error monitoring values; extract the maximum value of the concentrated first cutting action error monitoring values ​​and set it as a first characteristic error vector; extract the minimum value of the concentrated first cutting action error monitoring values ​​and set it as a second characteristic error vector; perform error analysis based on the first characteristic error vector and the second characteristic error vector to obtain a first cutting action control statistical error, and add it into the cutting control statistical error.

[0070] Furthermore, the metal sheet optimization processing system based on automatic deviation correction also includes an error analysis module for:

[0071] Calculate the mean of the first characteristic error vector and the second characteristic error vector, and set it as the first cutting action system error; trace back to the concentrated first cutting action error monitoring value according to the first cutting action system error to perform statistics, and obtain the first cutting action random error; add the first cutting action system error into the statistical system error, add the first cutting action random error into the statistical random error, store them as the first cutting action control statistical error, and add them into the cutting control statistical error.

[0072] Furthermore, the optimization module 16 in the metal sheet optimization processing system based on automatic deviation correction is also used for:

[0073] Obtain a plate deviation vector that does not meet the cutting scale threshold, wherein the plate deviation vector has a vector modulus and a vector direction; set the plate movement direction in the opposite direction of the vector direction; set the vector modulus as the plate movement constraint step; configure a single movement step; adjust the fixed position identifier according to the plate movement direction and the plate movement constraint step, combined with the single movement step, to obtain a fixed position adjustment result; perform cutting simulation on the plate three-dimensional model in the digital twin model of the metal plate cutting equipment according to the plate cutting control parameter and the fixed position adjustment result, to obtain a second metal plate cutting prediction model; when the second deviation scale characteristics of the second metal plate cutting prediction model and the metal plate cutting standard model are both less than or equal to the cutting scale threshold, set the fixed position adjustment result as the recommended fixed position identifier.

[0074] Furthermore, the optimization module 16 in the metal sheet optimization processing system based on automatic deviation correction is also used for:

[0075] An initial plate deviation vector is obtained; when there are multiple initial plate deviation vectors, a vector combination of the initial plate deviation vectors is obtained to obtain the plate deviation vector.

[0076] Furthermore, the optimization module 16 in the metal sheet optimization processing system based on automatic deviation correction is also used for:

[0077] When any second deviation scale feature between the second metal sheet cutting prediction model and the metal sheet cutting standard model is greater than the cutting scale threshold, the plate cutting control parameter is adjusted to obtain the plate cutting control adjustment parameter; according to the plate cutting control adjustment parameter and the fixed position adjustment result, the cutting simulation of the plate three-dimensional model is performed in the digital twin model of the metal sheet cutting equipment to obtain a third metal sheet cutting prediction model; when the third deviation scale feature between the third metal sheet cutting prediction model and the metal sheet cutting standard model is less than or equal to the cutting scale threshold, the fixed position adjustment result is set as the recommended fixed position identifier, wherein the recommended fixed position identifier has a plate cutting control adjustment parameter label.

[0078] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The metal sheet optimization processing method based on automatic deviation correction and the specific examples in the first embodiment are also applicable to the metal sheet optimization processing system based on automatic deviation correction in this embodiment. Through the above detailed description of the metal sheet optimization processing method based on automatic deviation correction, those skilled in the art can clearly understand the metal sheet optimization processing system based on automatic deviation correction in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method description.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A metal sheet optimization processing method based on automatic deviation correction, characterized in that: include: Configure the cutting scale threshold through the user end; When the metal sheet starts to be cut, the multi-dimensional image of the sheet is collected through the image sensor; Constructing a three-dimensional model of the plate according to the multi-dimensional image of the plate, wherein the three-dimensional model of the plate has a fixed position mark; According to the plate cutting control parameters and the fixed position identifier, a cutting simulation is performed on the three-dimensional model of the plate in the digital twin model of the metal plate cutting equipment to obtain a first metal plate cutting prediction model; Obtaining first deviation scale features of the first metal sheet cutting prediction model and the metal sheet cutting standard model; When any one of the first deviation scale features is greater than the cutting scale threshold, optimizing the fixed position identifier to obtain a recommended fixed position identifier; Automatic deviation correction control of the metal sheet is performed according to the recommended fixed position mark, and the fixed position mark is a specific point or line on the sheet, which is used to determine the cutting starting point and direction.

2. The metal sheet optimization processing method based on automatic deviation correction according to claim 1 is characterized in that: When the metal sheet starts to be cut, the image sensor is used to collect multi-dimensional images of the sheet, including: Performing error analysis on the metal sheet cutting equipment to obtain cutting control statistical errors, wherein the cutting control statistical errors include statistical systematic errors and statistical random errors. The statistical systematic errors are errors caused by inherent design or manufacturing defects of the cutting equipment, and the statistical random errors are errors caused by unpredictable factors in the cutting process. When the statistical random error is less than the cutting scale threshold, the plate cutting initial control parameter is fitted according to the statistical system error to generate the plate cutting control parameter; When the statistical random error is greater than or equal to the cutting scale threshold, a device debugging instruction is generated and sent to the user end.

3. The metal sheet optimization processing method based on automatic deviation correction as claimed in claim 2, characterized in that: Perform error analysis on the metal sheet cutting equipment to obtain cutting control statistical errors, wherein the cutting control statistical errors include statistical systematic errors and statistical random errors, including: Collecting a set of first cutting action error monitoring values ​​of a preset historical time zone of the metal sheet cutting device, wherein the preset historical time zone represents the time from the last completion of device debugging to the current time; Performing a box plot analysis on the first cutting action error monitoring value set to obtain a concentrated first cutting action error monitoring value; Extracting the maximum value of the concentrated first cutting action error monitoring value and setting it as a first characteristic error vector; Extracting the minimum value of the concentrated first cutting action error monitoring value and setting it as a second characteristic error vector; An error analysis is performed based on the first characteristic error vector and the second characteristic error vector to obtain a first cutting action control statistical error, which is added to the cutting control statistical error.

4. The metal sheet optimization processing method based on automatic deviation correction as claimed in claim 3 is characterized in that: Performing error analysis according to the first characteristic error vector and the second characteristic error vector to obtain a first cutting action control statistical error, and adding the first cutting action control statistical error to the cutting control statistical error, comprises: Calculate the mean of the first characteristic error vector and the second characteristic error vector, and set it as the first cutting action system error; According to the first cutting action system error, the first cutting action random error is obtained by tracing back to the centralized first cutting action error monitoring value for statistics; The first cutting action systematic error is added into the statistical systematic error, the first cutting action random error is added into the statistical random error, stored as the first cutting action control statistical error, and added into the cutting control statistical error.

5. The metal sheet optimization processing method based on automatic deviation correction according to claim 1 is characterized in that: Optimizing the fixed position identifier to obtain a recommended fixed position identifier includes: Obtaining a plate deviation vector that does not meet the cutting dimension threshold, wherein the plate deviation vector has a vector modulus and a vector direction; The opposite direction of the vector direction is set as the moving direction of the plate; The vector modulus is set as the plate movement constraint step length; Configure the single move step size; According to the plate moving direction and the plate moving constraint step length, combined with the single moving step length, the fixed position mark is adjusted to obtain a fixed position adjustment result; According to the plate cutting control parameter and the fixed position adjustment result, a cutting simulation is performed on the three-dimensional model of the plate in the digital twin model of the metal plate cutting equipment to obtain a second metal plate cutting prediction model; When the second deviation scale characteristics of the second metal sheet cutting prediction model and the metal sheet cutting standard model are both less than or equal to the cutting scale threshold, the fixed position adjustment result is set as the recommended fixed position identifier.

6. The metal sheet optimization processing method based on automatic deviation correction as claimed in claim 5, characterized in that: Obtaining a plate deviation vector that does not meet the cutting scale threshold includes: Obtaining the initial plate deviation vector; When there are multiple initial plate deviation vectors, the plate deviation vector is obtained by summing up the initial plate deviation vectors.

7. The metal sheet optimization processing method based on automatic deviation correction as claimed in claim 5, characterized in that: Also includes: When any second deviation scale feature between the second metal sheet cutting prediction model and the metal sheet cutting standard model is greater than the cutting scale threshold, adjusting the sheet cutting control parameter to obtain a sheet cutting control adjustment parameter; According to the plate cutting control adjustment parameter and the fixed position adjustment result, a cutting simulation is performed on the three-dimensional model of the plate in the digital twin model of the metal plate cutting equipment to obtain a third metal plate cutting prediction model; When the third deviation scale characteristics of the third metal sheet cutting prediction model and the metal sheet cutting standard model are both less than or equal to the cutting scale threshold, the fixed position adjustment result is set as the recommended fixed position identifier, wherein the recommended fixed position identifier has a plate cutting control adjustment parameter label.

8. The metal sheet optimization processing system based on automatic deviation correction is characterized by: The steps for implementing the metal sheet optimization processing method based on automatic deviation correction as described in any one of claims 1 to 7, the metal sheet optimization processing system based on automatic deviation correction comprises: A threshold configuration module, wherein the threshold configuration module is used to configure a cutting scale threshold through a user terminal; An image acquisition module, wherein the image acquisition module is used to acquire a multi-dimensional image of the metal sheet through an image sensor when the metal sheet begins to be cut; A three-dimensional modeling module, the three-dimensional modeling module is used to construct a three-dimensional model of the plate according to the multi-dimensional image of the plate, wherein the three-dimensional model of the plate has a fixed position mark; A cutting simulation module, wherein the cutting simulation module is used to perform cutting simulation on the three-dimensional model of the sheet metal in the digital twin model of the metal sheet metal cutting equipment according to the sheet metal cutting control parameters and the fixed position identifier, so as to obtain a first metal sheet metal cutting prediction model; A deviation analysis module, the deviation analysis module is used to obtain a first deviation scale feature of the first metal sheet cutting prediction model and the metal sheet cutting standard model; An optimization module, wherein the optimization module is used to optimize the fixed position identifier when any one of the first deviation scale features is greater than the cutting scale threshold, to obtain a recommended fixed position identifier; An automatic deviation correction control module is used to perform automatic deviation correction control on the metal sheet according to the recommended fixed position mark. The fixed position mark is a specific point or line on the sheet, which is used to determine the cutting starting point and direction.

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