Dynamic control method and system for elastic sheet punch forming

Abstract

The invention relates to a dynamic control method and system for elastic sheet punch forming in the field of high-end equipment manufacturing, and the method comprises the steps: obtaining optimized pressure distribution data, and calculating the punching speed adjustment amount of each region in combination with a preset model of the impact of the punching speed on the deformation of an elastic sheet, thereby obtaining a speed distribution adjustment matrix; the stamping speed data and the pressure distribution data which are adjusted in real time are adopted to drive stamping equipment to execute dynamic stamping operation, and the size and performance measurement data of the formed elastic piece are obtained; obtaining the size measurement data of the formed elastic sheet, and scanning the surface of the elastic sheet through a high-precision three-dimensional scanner to obtain an elastic sheet size deviation distribution matrix; and the updated stamping process parameters are adopted, stamping operation is circularly executed, dimensional deviation and performance consistency data are continuously collected, and finally elastic piece product data meeting the precision requirement are obtained.

Description

Technical Field

[0001] The present invention relates to the field of high-end equipment manufacturing, and in particular to an intelligent control technology for a stamping process, and more particularly to a dynamic control method and system for spring sheet stamping. Background Art

[0002] Metal spring stamping technology is crucial in high-precision manufacturing fields such as electronics, automotive, and aviation, directly impacting product performance stability and production efficiency. As a core functional component, springs require extremely high dimensional accuracy and performance consistency to meet the needs of complex application scenarios.

[0003] However, traditional stamping methods have significant limitations. The main limitation is that they use fixed process parameters, making it difficult to adapt to material thickness fluctuations or deformation differences. This results in dimensional deviations exceeding ±0.1 mm, which seriously affects the production quality and material utilization of ultra-thin springs (0.1 mm thick). Furthermore, traditional methods cannot precisely control the local properties of springs, limiting their potential for multifunctional, high-performance applications.

[0004] In this field, the core challenges focus on how to deal with the impact of material thickness fluctuations and deformation differences on stamping accuracy, and how to achieve dynamic regulation of shrapnel performance. Slight deviations in material thickness (±5%) will lead to uneven pressure distribution during the stamping process, which in turn causes dimensional deviations or inconsistent performance. In order to achieve gradient changes in the local elastic coefficient or microstructure of the shrapnel, it is necessary to make real-time and precise dynamic adjustments to the pressure and speed during the stamping process. Traditional stamping equipment with fixed parameters lacks real-time feedback and regulation capabilities, making it difficult to meet the high-precision and multifunctional production requirements of ultra-thin shrapnel. The unresolved problems of these technical factors have led to the problems of insufficient dimensional accuracy, material waste, and single shrapnel performance.

[0005] Therefore, how to effectively compensate for material thickness deviation and achieve precise control of the local performance of shrapnel by real-time monitoring and dynamic control of pressure and speed during the stamping process has become a key issue in improving the quality and functionality of hardware shrapnel stamping. Summary of the Invention

[0006] The present invention provides a dynamic control method for spring sheet stamping, comprising the following steps:

[0007] Obtain real-time measurement data of material thickness during the stamping process. Use a high-precision laser ranging sensor to scan the surface of the shrapnel material to obtain a material thickness distribution matrix, where the matrix elements represent the thickness value of each area.

[0008] According to the material thickness distribution matrix, the theoretical pressure requirements of each area during the stamping process are calculated. The finite element analysis model is used to input the thickness value and material mechanical parameters to obtain the pressure distribution prediction results;

[0009] If the pressure distribution prediction result shows that the pressure deviation in a certain area exceeds the preset threshold, the preset threshold is ±

[0010] If the pressure is less than 5%, the local pressure output of the stamping die is adjusted by the servo motor control system to obtain the optimized pressure distribution data;

[0011] Obtain optimized pressure distribution data, combine it with the preset model of the impact of stamping speed on spring deformation, calculate the stamping speed adjustment amount of each area, and obtain the speed distribution adjustment matrix;

[0012] If the speed adjustment amount of a certain area in the speed distribution adjustment matrix exceeds the preset range, the preset range is ±

[0013] If the speed is less than 10%, the speed parameters of the stamping equipment are dynamically updated through the CNC system to obtain the real-time adjusted stamping speed data;

[0014] Using the real-time adjusted stamping speed data and pressure distribution data, the stamping equipment is driven to perform dynamic stamping operations to obtain the size and performance measurement data of the formed spring;

[0015] Obtain the dimensional measurement data of the formed shrapnel, scan the surface of the shrapnel with a high-precision 3D scanner, and obtain the shrapnel dimensional deviation distribution matrix;

[0016] If the spring fragment size deviation distribution matrix shows that the deviation in a certain area exceeds ±0.05 mm, the local pressure and speed parameters of the next round of stamping are adjusted according to the deviation value and the preset performance control model to obtain the updated stamping process parameters;

[0017] Using the updated stamping process parameters, the stamping operation is executed cyclically, and dimensional deviation and performance consistency data are continuously collected to obtain the final spring product data that meets the precision requirements.

[0018] The present invention provides a dynamic control system for spring sheet stamping, which mainly includes:

[0019] Real-time thickness measurement module, used to obtain real-time measurement data of material thickness during the stamping process. It uses a high-precision laser ranging sensor to scan the surface of the shrapnel material to obtain a material thickness distribution matrix, where the matrix elements represent the thickness value of each area;

[0020] Theoretical pressure calculation module is used to calculate the theoretical pressure requirements of each area during the stamping process based on the material thickness distribution matrix. It uses the finite element analysis model to input the thickness value and material mechanical parameters to obtain the pressure distribution prediction results;

[0021] The pressure deviation detection and adjustment module is used to adjust the local pressure output of the stamping die through the servo motor control system to obtain optimized pressure distribution data if the pressure distribution prediction result shows that the pressure deviation in a certain area exceeds the preset threshold value (the preset threshold value is ±5%);

[0022] The speed adjustment calculation module is used to obtain the optimized pressure distribution data, combine the preset model of the impact of stamping speed on spring deformation, calculate the stamping speed adjustment amount of each area, and obtain the speed distribution adjustment matrix;

[0023] The speed parameter dynamic update module is used to dynamically update the speed parameters of the stamping equipment through the CNC system if the speed adjustment amount of a certain area in the speed distribution adjustment matrix exceeds the preset range (the preset range is ±10%) to obtain the real-time adjusted stamping speed data;

[0024] The dynamic stamping execution module is used to use the real-time adjusted stamping speed data and pressure distribution data to drive the stamping equipment to perform dynamic stamping operations and obtain the size and performance measurement data of the formed spring piece;

[0025] The dimensional deviation measurement module is used to obtain the dimensional measurement data of the formed spring piece. The surface of the spring piece is scanned by a high-precision 3D scanner to obtain the dimensional deviation distribution matrix of the spring piece.

[0026] The process parameter adjustment module is used to adjust the local pressure and speed parameters of the next round of stamping based on the deviation value and the preset performance control model if the spring piece size deviation distribution matrix shows that the deviation in a certain area exceeds ±0.05 mm, thereby obtaining updated stamping process parameters;

[0027] The cyclic stamping optimization module is used to adopt the updated stamping process parameters, cyclically execute the stamping operation, and continuously collect dimensional deviation and performance consistency data to obtain the spring product data that ultimately meets the precision requirements.

[0028] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0029] The present invention discloses a dynamic control method for the stamping and forming of shrapnel. The material thickness distribution is obtained through high-precision laser ranging, and the pressure distribution is predicted in combination with finite element analysis to adjust the local pressure and speed parameters of the stamping die in real time. Three-dimensional scanning is used to measure the dimensional deviation of the shrapnel after forming, and the stamping process parameters are dynamically optimized according to the deviation value to achieve closed-loop control. This method can accurately control the pressure and speed distribution during the stamping process, effectively compensate for the unevenness of the material thickness, and significantly improve the dimensional accuracy and performance consistency of the shrapnel. By continuously collecting data and optimizing parameters, the present invention can achieve intelligent and refined control of the shrapnel stamping and forming process, greatly improving product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The figure is a flow chart of a dynamic control method for spring sheet stamping forming of the present invention.

[0031] Figure 2 Schematic diagram of a dynamic control method for spring sheet stamping according to the present invention.

[0032] Figure 3 This is another schematic diagram of a dynamic control method for spring sheet stamping according to the present invention.

[0033] Figure 4 The figure is a schematic diagram of the framework of a dynamic control system for spring stamping forming of the present invention. DETAILED DESCRIPTION

[0034] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0035] like Figure 1-4 In this embodiment, a dynamic control method and system for spring sheet stamping can specifically include:

[0036] Step S101 , obtaining real-time measurement data of material thickness during the stamping process, scanning the surface of the spring material using a high-precision laser ranging sensor, and obtaining a material thickness distribution matrix, wherein the matrix elements represent the thickness values ​​of each region.

[0037] The surface of the shrapnel material is scanned in real time by a laser ranging sensor to obtain the original thickness data and form an initial data set. Based on the initial data set, the thickness data is denoised by a pre-processing method to obtain a smoothed thickness data set. If there are abnormal values ​​in the smoothed thickness data set, the data points that exceed the range are filtered through a preset threshold range to eliminate the data points and determine the corrected thickness data set. For the corrected thickness data set, a material thickness distribution matrix is ​​constructed, and the matrix elements correspond to the thickness values ​​of each region to generate structured thickness distribution information. Based on the structured thickness distribution information, the matrix data is classified and processed using a support vector machine algorithm to determine whether there is an area with uneven thickness distribution. If it is determined that there is an area with uneven thickness distribution, the area position where the deviation is greater than the preset threshold is determined by comparing with the preset standard thickness distribution model. Based on the area position where the deviation is greater than the preset threshold, adjustment parameter data is generated and reference information for subsequent stamping process optimization is output.

[0038] Specifically, to obtain real-time measurement data on material thickness during the stamping process, a high-precision laser ranging sensor can be used to scan the surface of the shrapnel material. The specific implementation method is as follows: First, a laser ranging sensor system is deployed. Assuming the sensor's measurement accuracy is 0.01mm, the scanning range covers the entire surface of the shrapnel material, the size is set to 100mm×100mm, and the scanning resolution is 1mm×1mm, forming a 100×100 measurement grid. The sensor collects data at a frequency of 1000 times per second to ensure real-time performance. The collected data is the distance value from each grid point to the sensor reference plane. For example, if the distance value of a grid point is 50.23mm, and the fixed distance from the reference plane to the bottom surface of the material is 50.00mm, the thickness of the point is calculated as 50.23-50.00=0.23mm. By traversing all grid points, a 100×100 thickness distribution matrix is ​​generated. The matrix elements represent the thickness values ​​at each point. For example, the thickness at matrix position (1,1) is 0.23 mm, and at position (1,2) is 0.22 mm. Next, a data processing algorithm is used to smooth the matrix. A 3×3 mean filter is used to calculate the average thickness of the nine points surrounding each grid point to reduce the influence of noise. For example, if the original thickness of a point is 0.23 mm and the average thickness of the eight surrounding points is 0.22 mm, the smoothed thickness is updated to 0.22 mm. Subsequently, the thickness distribution matrix is ​​analyzed, and a thickness anomaly threshold is set to ±0.05 mm. If the thickness value in a region exceeds 0.25 mm or falls below 0.15 mm, it is marked as an abnormal area. For example, if the thickness of a point in the matrix is ​​0.28 mm, which exceeds the threshold, the system automatically records the coordinates of the point and generates an anomaly report. Finally, the thickness distribution matrix is ​​linked to the process parameters of the stamping equipment. For example, if the stamping pressure is 5000N and the abnormal area accounts for more than 5%, the system automatically adjusts the pressure to 4800N. A feedback mechanism compares the adjustment results with the thickness data, forming a closed-loop control loop to ensure stamping quality. This method forms a complete technical chain from data acquisition to analysis and processing, achieving real-time monitoring and optimization of material thickness.

[0039] Step S102 , calculating the theoretical pressure requirements of each region during the stamping process based on the material thickness distribution matrix, using a finite element analysis model, inputting thickness values ​​and material mechanical parameters, and obtaining a pressure distribution prediction result.

[0040] Initial thickness data is obtained from the material thickness distribution matrix, and the initial thickness data is standardized using a data preprocessing method to obtain a processed thickness data set. Based on the processed thickness data set, combined with a pre-established material mechanical parameter database, the corresponding mechanical parameter values ​​are obtained to determine the mechanical characteristic data of each region. Using a finite element analysis model, meshing is performed on the mechanical characteristic data and the thickness data set to obtain gridded distribution data for each region. If there are abnormal values ​​in the gridded distribution data, the data smoothing method is used to correct them and obtain corrected grid distribution data. Based on the corrected grid distribution data, the theoretical pressure values ​​of each region during the stamping process are calculated to obtain preliminary pressure distribution data. By iteratively optimizing the preliminary pressure distribution data and performing multiple simulations using the finite element method, the stability of the pressure demand of each region is determined to obtain the final pressure distribution prediction result. Based on the final pressure distribution prediction result, combined with the regional division characteristics of the stamping process, the pressure demand range of each region is determined and a complete pressure distribution data set is output.

[0041] Specifically, when calculating the theoretical pressure requirements of each area during the stamping process, we first obtain the material thickness distribution matrix through information technology. Assume that the thickness distribution matrix of a stamping part is a 5x5 two-dimensional array with the unit of millimeter. The data example is [[2.5, 2.3, 2.1, 2.0, 1.9],

[0042] [2.4,2.2,2.0,1.8,1.7], [2.3,2.1,1.9,1.7,1.6],

[0043] [2.2, 2.0, 1.8, 1.6, 1.5], [2.1, 1.9, 1.7, 1.5, 1.4]]. Subsequently, these thickness values ​​are input into the finite element analysis model, combined with the material mechanics parameters, such as the yield strength of the material is 250 MPa, the elastic modulus is 200 GPa, and the Poisson's ratio is 0.3. The geometric model of the stamping part is established through finite element software (such as ANSYS), and the corresponding thickness value and material properties are assigned to each grid unit. Next, the boundary conditions are set, assuming that a uniform load is applied to the contact surface of the stamping die, the initial pressure is 0.1 MPa, and the nonlinear contact algorithm is used for iterative calculation to obtain the stress distribution of each grid unit. The calculation formula is σ = F / A, where F is the force and A is the unit area. The local pressure demand is adjusted in combination with the thickness difference. The prediction results show that the pressure demand in the thinner area (such as 1.4 mm) is 1.8 MPa, while the pressure demand in the thicker area (such as 2.5 mm) is 1.2 MPa, forming a pressure distribution matrix. The inverse relationship between pressure distribution and thickness is further analyzed to verify the accuracy of the model. If the error exceeds 5%, the calculation accuracy is optimized by adjusting the grid density (such as increasing it from 1000 cells to 2000 cells). Finally, the pressure distribution results are compared with the stamping equipment capability database. Assuming that the maximum pressure of the equipment is 2.0 MPa, it is confirmed that the pressure requirements in all areas are within the equipment capability range to ensure process feasibility. Through the above process, a complete logical chain from data input to result verification is formed to ensure the scientificity and practicality of the calculation results.

[0044] Step S103 : If the pressure distribution prediction result shows that the pressure deviation of a certain area exceeds a preset threshold value, which is ±5%, the local pressure output of the stamping die is adjusted by the servo motor control system to obtain optimized pressure distribution data.

[0045] The pressure distribution data is acquired from the sensor, and the pressure distribution data is processed using a pre-established analysis model to obtain preliminary prediction data. If there is a regional deviation in the preliminary prediction data that exceeds a preset threshold, the position and degree of the deviation area are judged by a data comparison tool to determine the range of the area that needs to be adjusted. According to the deviation area range, an adjustment instruction is generated by a servo motor control system to obtain a control signal for the local pressure of the stamping die. The local pressure output is adjusted by transmitting the control signal to the execution unit of the stamping die to obtain the adjusted pressure distribution data. If there is still a regional deviation in the adjusted pressure distribution data that exceeds the preset threshold, the position and degree of the deviation area are judged again by a cyclic comparison mechanism to obtain a new adjustment instruction. According to the new adjustment instruction, the support vector machine model is used to optimize and predict the pressure distribution data to determine the final pressure output scheme. By applying the final pressure output scheme to the control system of the stamping die, the optimized pressure distribution data is obtained.

[0046] Specifically, the pressure distribution of the stamping die is first predicted using finite element analysis software. Assuming that the die size is 1000mm×500mm, the material is Q235 steel, the punching force is 500kN, the grid is divided into quadrilateral units, and the number of units is 10,000, the pressure value of a certain area (such as the center area of ​​the die, coordinate range x=400-600mm, y=200-300mm) is calculated to be 550kPa, the preset target pressure is 500kPa, and the deviation is (550-500) / 500×100%=10%, which exceeds the ±5% threshold. Next, based on the deviation data, the servo motor was adjusted using a PID control algorithm, with proportional coefficient Kp = 0.5, integral coefficient Ki = 0.1, differential coefficient Kd = 0.05, and deviation e = 50 kPa. The control output u(t) was calculated as Kp·e + Ki·∫e(t)dt + Kd·de(t) / dt. The initial integral term was 0, and the differential term was estimated based on the rate of change of the deviation at the previous moment, resulting in u(t) = 25 + 0 + 0 = 25. The servo motor then adjusted its output power accordingly, increasing its torque by 25%, to approximately 12.5 Nm (assuming the motor's rated torque is 50 Nm). Subsequently, the adjusted pressure distribution data was collected in real time using a pressure sensor array (resolution 0.1 kPa, sampling frequency 100 Hz). The optimized central pressure was 505 kPa, with a deviation of (505-500) / 500 × 100% = 1%, meeting the ±5% threshold. To ensure stability, the system analyzes the frequency spectrum of pressure data using Fourier transforms to confirm the absence of high-frequency oscillations (main frequency <10Hz). If abnormal oscillations are detected, secondary adjustments are triggered. Finally, the optimized pressure distribution data is stored in a database for subsequent process parameter optimization. The database uses MySQL, and the table structure includes the following fields: timestamp, region coordinates, pressure value, and adjustment amount.

[0047] Step S104 , obtaining optimized pressure distribution data, and calculating the stamping speed adjustment amount of each area in combination with a preset model of the influence of stamping speed on spring deformation, to obtain a speed distribution adjustment matrix.

[0048] Obtain the initial pressure distribution data, and extract the pressure characteristic value of each area from the initial pressure distribution data. Combined with the preset punch shape model, calculate the influence of the punching speed on the deformation of the spring, and obtain the speed influence factor of each area. If the speed influence factor of a certain area exceeds the preset threshold, the corresponding speed adjustment value is generated by the iterative optimization algorithm; if it does not exceed, the original speed parameter is kept unchanged to obtain a set of adjustment values. Based on the set of adjustment values, combined with the regional division data, a distribution matrix of speed adjustment is constructed to determine the speed adjustment vector of each area. The support vector machine algorithm is used to verify the distribution matrix, judge the rationality of the adjustment value of each area, and obtain the verified adjustment matrix. Based on the verified adjustment matrix, combined with the real-time deformation data of the spring, the parameters of the preset punch shape model are dynamically updated to generate the final optimized speed adjustment strategy matrix.

[0049] Specifically, first obtain the optimized pressure distribution data. Assume that the shrapnel stamping process is simulated by the finite element analysis software ANSYS, and obtain the 100x100 grid pressure distribution matrix P, where each element P(i,j) represents the pressure value at the coordinate (i,j), in MPa.

[0050] For example, the center region has P(50,50) = 200, and the edge region has P(1,1) = 50. Data is processed using Python, the CSV file output by ANSYS is read, converted into a NumPy array, and noise is filtered (pressure values ​​< 10 are set to 0). Then, combining the preset model of the effect of punching speed on spring deformation, it is assumed that the deformation d, velocity v, and pressure P satisfy the relationship d = k·P·v^2, where k = 0.001 is the material constant. By fitting the experimental data, the initial velocity distribution matrix V is obtained, in units of mm / s, for example, V(50,50) = 100, V(1,1) = 20. To optimize deformation uniformity, the target deformation d_target = 0.5mm, and the required velocity adjustment Δv for each region is calculated. Using the formula Δv = sqrt(d_target / (k·P))-v, the matrices P and V are traversed to generate the adjustment matrix ΔV.

[0051] For example, when P(50,50)=200, Δv=sqrt(0.5 / (0.001·200))-100≈-97.5; when P(1,1)=50, Δv=sqrt(0.5 / (0.001·50))-20≈-10. Handle outliers (in the region where P<10, Δv is set to 0). Finally, the velocity distribution adjustment matrix ΔV is obtained, saved as a 100x100 NumPy array, and output to a CSV file for use by the stamping machine controller. To ensure logical rigor, verify whether the deformation after applying ΔV is close to d_target, recalculate d_new=k·P·(V+ΔV)^2, and calculate the mean square error (MSE) between d_new and 0.5=0.001, which meets the accuracy requirements. If MSE>0.01, iteratively adjust the k value and repeat the calculation of ΔV. This process is linked to the real-time control system of the stamping machine. ΔV is transmitted to the controller through the API to adjust the servo motor speed and achieve closed-loop optimization.

[0052] Step S105: If the speed adjustment amount of a certain area in the speed distribution adjustment matrix exceeds the preset range, which is ±10%, the speed parameters of the stamping equipment are dynamically updated through the numerical control system to obtain real-time adjusted stamping speed data.

[0053] Acquire the regional speed adjustment data in the speed distribution adjustment matrix, and the regional speed adjustment data is used to characterize the speed change of the stamping equipment. If the regional speed adjustment data exceeds the preset range, the preset threshold comparison method is used to determine whether to trigger a dynamic update. If the judgment result is to trigger a dynamic update, the adjusted speed parameter instruction is generated by the numerical control system. According to the speed parameter instruction, the speed parameter is transmitted to the stamping equipment through the numerical control system to obtain the real-time adjusted stamping speed data. A data verification algorithm is used to extract key features from the stamping speed data to determine whether the adjusted speed parameter meets the preset range. If the speed parameter does not meet the preset range, the speed distribution adjustment matrix is ​​corrected by an iterative optimization algorithm to obtain new regional speed adjustment data. According to the new regional speed adjustment data, the dynamic update and verification process is repeated to determine the final stable stamping speed data.

[0054] Specifically, during the operation of the stamping equipment, the speed data of each area of ​​the equipment is first monitored and analyzed in real time through the speed distribution adjustment matrix. Assuming that the initial stamping speed of a certain area is 100 meters per minute, the system detects that the speed adjustment amount in the area is 12%, which exceeds the upper limit of the preset range of ±10%. The system automatically triggers the adjustment mechanism, compares the data with the preset range, calculates the excess part as 2%, and generates an adjustment instruction. Then, the system dynamically optimizes the speed parameters through a built-in algorithm. The specific algorithm is: adjusted speed = initial speed × (1-exceedance ratio × attenuation coefficient), where the attenuation coefficient is set to 0.8, so the adjusted speed = 100 × (1-0.02 × 0.8) = 98.4 meters per minute. The system compares and analyzes this value with historical data to ensure that the adjusted speed does not cause equipment vibration or a decrease in processing accuracy. The analysis results show that 98.4 meters per minute is within a safe range. Subsequently, after receiving the adjustment instruction, the CNC system automatically updates the speed parameters of the stamping equipment, adjusts the speed of the area from 100 meters per minute to 98.4 meters per minute, and records the data differences before and after the adjustment to form a log file for subsequent tracing. At the same time, the system collects the adjusted stamping speed data in real time, and confirms through sensor feedback that the actual speed is 98.3 meters per minute, with an error of only 0.1 meters per minute from the target value, which meets the accuracy requirements. To ensure the stability of the entire production line, the system will also perform correlation analysis on the adjustment data and the speed distribution of other areas, calculate the overall speed balance index, and assume that the index is 0.95 (the full score is 1), indicating that the adjustment has no significant impact on the production line. The system automatically archives the analysis results and continuously monitors to ensure subsequent stable operation.

[0055] Step S106 , using the real-time adjusted stamping speed data and pressure distribution data, driving the stamping equipment to perform a dynamic stamping operation, and obtaining the size and performance measurement data of the formed spring piece.

[0056] Acquire real-time stamping speed data and pressure distribution data, monitor the operating status of the stamping equipment through the sensor system, and obtain an initial set of operating parameters. Based on the initial set of operating parameters, use preset logical rules to analyze the stamping speed and pressure distribution. If it is detected that the speed or pressure exceeds the preset threshold range, trigger the dynamic adjustment mechanism and determine the adjusted operating parameters. Drive the stamping equipment to perform dynamic operations through the adjusted operating parameters, continuously collect the real-time feedback operating data, and obtain updated information on the equipment's operating status. Based on the updated information on the equipment's operating status, analyze the processing of the formed spring piece. If the pressure distribution during the processing is uneven, adjust the equipment control instructions to obtain an optimized set of operating instructions. Use the optimized set of operating instructions to control the stamping equipment to complete the spring piece forming task, measure the dimensions of the formed spring piece, and obtain a record of dimensional data. Based on the dimensional data record, combine the performance testing tool to test the performance data of the formed spring piece, determine whether the performance data meets the preset standards, and obtain the final measurement result data. Feedback information is generated through the final measurement result data and input into the real-time adjustment module, so as to perform a new round of optimization on the punching speed and pressure distribution and obtain an updated operating parameter configuration.

[0057] Specifically, in the process of realizing dynamic operation of stamping equipment, the stamping speed data and pressure distribution data are first collected in real time through sensors. For example, the stamping speed is initially set to 5.0 m / s, the pressure distribution in the center area of ​​the mold is 800 N / cm2, and the edge area is 600 N / cm2. These data are transmitted to the central control system through the Industrial Internet of Things for processing. The algorithm embedded in the system will be adjusted according to the real-time data, using the PID control algorithm, setting the proportional coefficient P to 0.5, the integral coefficient I to 0.2, and the differential coefficient D to 0.1. It is calculated that the stamping speed needs to be adjusted to 5.2 m / s to optimize the forming effect. At the same time, the pressure distribution is adjusted to 820 N / cm2 in the center area through the finite element analysis model.

[0058] To ensure uniformity, analysis results showed that the stress distribution deviation was reduced from 5% to 2% after adjustment. The adjusted parameters were then automatically transmitted to the stamping equipment controller, driving the equipment to perform dynamic stamping operations. The equipment's built-in servo motor precisely adjusted the stroke and pressure output according to the instructions to complete a stamping cycle. The formed spring pieces were automatically inspected using a high-precision laser measuring instrument for dimensional data. For example, the length was 50.00 mm, the width was 20.00 mm, and the thickness was 0.50 mm, all within ±0.02 mm of the design values. Performance data was also measured using a tensile testing machine, revealing an elastic modulus of 200 GPa and a yield strength of 400 MPa, demonstrating that performance met design requirements. This data was further fed back into the system database for predictive analysis using a machine learning model. The results showed that optimizing the combination of stamping speed and pressure distribution could improve forming accuracy by 3%, thus forming a closed-loop control logic to ensure continuous optimization of subsequent production batches. The system automatically generates adjustment recommendations for the next production cycle.

[0059] Step S107 , obtaining the measurement data of the size of the formed spring piece, scanning the surface of the spring piece with a high-precision three-dimensional scanner, and obtaining a spring piece size deviation distribution matrix.

[0060] The surface of the shrapnel is scanned using a high-precision 3D scanner to obtain raw 3D point cloud data and preliminary geometric information of the shrapnel surface. The raw 3D point cloud data is processed using point cloud denoising technology to remove noise points and outliers, thereby obtaining a clean point cloud dataset. Based on the clean point cloud dataset, a 3D mesh model of the shrapnel surface is constructed to generate a digital model of the shrapnel surface. The digital model is compared with a preset standard shrapnel size model, and the geometric differences are calculated to obtain size deviation distribution data. Based on the size deviation distribution data, a deviation distribution matrix is ​​generated, and the deviation distribution matrix is ​​processed using heat map visualization technology to obtain an intuitive representation of the deviation distribution. The deviation distribution matrix is ​​classified using a support vector machine algorithm to determine whether the deviation exceeds a preset threshold range. If so, it is marked as an abnormal area. The specific location information of the abnormal area is extracted from the deviation distribution matrix, and a data record of the abnormal area is generated to obtain a basis for targeted adjustments.

[0061] Specifically, a high-precision 3D scanner is used to obtain dimensional measurements of the molded shrapnel. Structured light scanning technology is required. The scanner's accuracy is set to 0.01mm, with a resolution of 1000×1000 pixels. The scanning range covers the entire shrapnel surface area (assuming a size of 50mm×30mm×2mm). First, the system automatically calibrates the scanner's raster projection, keeping the calibration error within ±0.005mm. Triangulation is then used to calculate the shrapnel surface point cloud data, generating a 3D coordinate matrix (x, y, z) of approximately 1 million points. This point cloud data is aligned with a standard CAD model (STL format) using a point cloud registration algorithm (ICP algorithm). The number of iterations is set to 50, and the registration error is less than 0.02mm. Next, the key dimensional features of the shrapnel (such as length, width, thickness, and curvature) are extracted. A RANSAC-based plane fitting algorithm is used to calculate the deviation of each feature point, generating a dimensional deviation distribution matrix (matrix dimensions are 100×60, with element values ​​representing deviations in mm and a range of [-0.1, 0.1]). The deviation matrix is ​​smoothed by Gaussian filtering (σ=1.5) to eliminate the influence of noise. The analysis process uses statistical methods to calculate the deviation mean (for example, 0.015mm) and standard deviation (0.008mm), and generate a deviation heat map to identify areas where deviations are concentrated (for example, the deviation in the edge area reaches 0.05mm). If the deviation exceeds the tolerance (±0.03mm), the system automatically marks the out-of-tolerance area and outputs a report. The report contains deviation statistics and heat maps for reference in subsequent process optimization. The entire process is controlled by automated software, with a data processing time of approximately 120 seconds. It is connected to the production management system and updates the quality control database in real time to ensure the closed-loop logic of process adjustments.

[0062] In step S108 , if the spring fragment size deviation distribution matrix shows that the deviation in a certain area exceeds ±0.05 mm, the local pressure and speed parameters of the next round of stamping are adjusted according to the deviation value and the preset performance control model to obtain updated stamping process parameters.

[0063] Obtain a shrapnel size deviation distribution matrix, wherein the deviation distribution matrix includes deviation data for multiple regions. Extract the deviation data for a specific region from the deviation distribution matrix, determine whether the deviation value for the specific region exceeds a preset range, and obtain a deviation determination result. If the deviation determination result indicates that the deviation value exceeds the preset range, extract a specific deviation value from the deviation data for the specific region, and calculate the degree of difference between the deviation value and the preset range. Based on the degree of difference between the deviation value and the preset range, use a pre-established performance control model to analyze the impact of the deviation data on the stamping process and obtain a control suggestion. Based on the control suggestion and the output of the performance control model, calculate the adjustment range of the local pressure and determine the pressure parameters for the next round of stamping. Based on the adjustment range of the local pressure, analyze the matching requirements of the speed parameters and determine the speed parameters for the next round of stamping. Generate updated stamping process parameters based on the pressure parameters and speed parameters, and determine whether the updated stamping process parameters meet the preset process parameter range. If the updated stamping process parameters do not meet the preset process parameter range, return to the step of extracting the specific deviation value from the deviation data for the specific region and reanalyze. The final process adjustment plan is generated through the updated stamping process parameters, and the execution data for the next round of stamping is determined.

[0064] Specifically, in the process of optimizing the size deviation control of shrapnel, assuming that the size deviation distribution matrix of a batch of shrapnel is obtained through high-precision detection equipment, it is found that the deviation value in a certain area reaches +0.08 mm, which exceeds the preset range of ±0.05 mm. The system automatically triggers the deviation analysis module. First, the system inputs the deviation data of the area into the deviation distribution analysis algorithm, calculates the mean and standard deviation of the deviation, and obtains a mean of +0.075 mm and a standard deviation of 0.012 mm, indicating that the deviation is relatively concentrated and tends to be positive. Next, the system calls the preset performance control model and compares the deviation value with the parameter mapping relationship in the model. The regression equation constructed by the model based on historical data is P_new=P_old*(1-0.5*Δd), where P_new is the adjusted pressure, P_old is the original pressure, and Δd is the deviation value. It is calculated that if the original pressure is 1000 Newtons, the adjusted pressure is 960 Newtons. At the same time, the stamping speed control adopts a linear adjustment algorithm, V_new = V_old*(1-0.3*Δd), the original speed is 50 mm / s, and it is adjusted to 48 mm / s after calculation. Subsequently, the system transmits the updated pressure of 960 Newtons and speed of 48 mm / s parameters to the control unit of the stamping equipment, and automatically updates the next round of stamping process parameters. To ensure the adjustment effect, the system is also associated with a real-time monitoring module to record the adjusted deviation data. If it still exceeds the standard, it will be iteratively optimized to form a closed-loop control logic. Through the above process, deviation analysis, parameter calculation and adjustment, and equipment update are seamlessly connected to ensure accurate optimization of process parameters and improve the consistency of shrapnel size.

[0065] Step S109 , using the updated stamping process parameters, cyclically executing the stamping operation, and continuously collecting dimensional deviation and performance consistency data to obtain the final spring product data that meets the accuracy requirements.

[0066] The optimized process parameter data is obtained from a pre-established stamping process database, and the initial stamping operation configuration scheme is determined based on the production requirements of the spring clip product. According to the initial stamping operation configuration scheme, the stamping operation is performed, and the dimensional deviation data and performance consistency data are collected in real time during the operation to obtain a preliminary production data set. For the preliminary production data set, the support vector machine algorithm is used to classify the dimensional deviation data. If the classification result shows that the deviation exceeds the preset threshold, the process parameters are adjusted to generate an updated configuration scheme. According to the updated configuration scheme, the stamping operation is continuously performed, and the real-time dimensional deviation data and performance consistency data are obtained through the sensor system to determine the adjusted production data set. For the adjusted production data set, the changing trend of the performance consistency data is analyzed. If the changing trend does not meet the accuracy standard, the potential deviation point is predicted through the regression analysis model to obtain the optimized direction data. According to the optimized direction data, the cycle frequency and process parameters of the stamping operation are dynamically adjusted, and the adjusted production data are continuously collected to determine whether it meets the accuracy standard. By integrating the production data that meets the accuracy standard, the final spring clip product data set is generated, and the stability result of the production process is determined.

[0067] Specifically, for the process flow of using updated stamping process parameters and cyclically performing stamping operations to continuously collect dimensional deviation and performance consistency data, and finally obtaining shrapnel product data that meets the precision requirements, the following specific implementation method can be used to achieve automated processing. First, the system will be initialized according to the preset stamping process parameters, such as setting the stamping pressure to 5000 Newtons, the stamping speed to 30 times per minute, and the die gap to 0.05 mm. These parameters are automatically loaded into the stamping equipment control system through the process database to ensure the consistency of each operation. Then, the equipment enters the cyclic stamping mode. After each stamping is completed, the built-in laser measurement sensor will collect the dimensional data of the shrapnel in real time. For example, if the target length is 50.00 mm and the actual measured value is 50.03 mm, the deviation is 0.03 mm. At the same time, the elastic recovery rate of the shrapnel is recorded. If the target recovery rate is 98%, the actual value is 97.5%, and the deviation is 0.5%. These data are uploaded to the central data processing platform through the Internet of Things interface. The platform uses a preset deviation analysis algorithm, such as the root mean square error calculation method, to calculate the root mean square value of the dimensional deviation to be 0.02 mm, and combines it with the performance consistency data for a comprehensive score. The scoring formula is: comprehensive score = 80% * dimensional consistency + 20% * performance consistency. Assuming that the current batch score is 85 points, which does not reach the target of 90 points, the system will automatically trigger the parameter optimization module. According to the results of the historical data regression analysis, the stamping pressure will be fine-tuned to 5050 Newtons, the speed will be adjusted to 28 times per minute, and the next round of stamping cycle will be re-entered. During the cycle, the system continuously monitors data fluctuations. If the scores of 5 consecutive batches are all over 90 points and the dimensional deviation is stable within 0.01 mm, it is determined to meet the accuracy requirements. The final shrapnel product data report is automatically generated, including key indicators such as the average deviation value of 0.01 mm and the performance consistency rate of 98.2%, and stored in the database for subsequent traceability. This process forms a closed loop through data-driven and algorithmic optimization to ensure the logic of process adjustments and result verification, while also connecting with the quality management system to ensure that the data can be used for subsequent process improvements.

[0068] The present invention provides a dynamic control system for spring sheet stamping, which mainly includes:

[0069] Real-time thickness measurement module, used to obtain real-time measurement data of material thickness during the stamping process. It uses a high-precision laser ranging sensor to scan the surface of the shrapnel material to obtain a material thickness distribution matrix, where the matrix elements represent the thickness value of each area;

[0070] Theoretical pressure calculation module is used to calculate the theoretical pressure requirements of each area during the stamping process based on the material thickness distribution matrix. It uses the finite element analysis model to input the thickness value and material mechanical parameters to obtain the pressure distribution prediction results;

[0071] The pressure deviation detection and adjustment module is used to adjust the local pressure output of the stamping die through the servo motor control system to obtain optimized pressure distribution data if the pressure distribution prediction result shows that the pressure deviation in a certain area exceeds the preset threshold value (the preset threshold value is ±5%);

[0072] The speed adjustment calculation module is used to obtain the optimized pressure distribution data, combine the preset model of the impact of stamping speed on spring deformation, calculate the stamping speed adjustment amount of each area, and obtain the speed distribution adjustment matrix;

[0073] The speed parameter dynamic update module is used to dynamically update the speed parameters of the stamping equipment through the CNC system if the speed adjustment amount of a certain area in the speed distribution adjustment matrix exceeds the preset range (the preset range is ±10%) to obtain the real-time adjusted stamping speed data;

[0074] The dynamic stamping execution module is used to use the real-time adjusted stamping speed data and pressure distribution data to drive the stamping equipment to perform dynamic stamping operations and obtain the size and performance measurement data of the formed spring piece;

[0075] The dimensional deviation measurement module is used to obtain the dimensional measurement data of the formed spring piece. The surface of the spring piece is scanned by a high-precision 3D scanner to obtain the dimensional deviation distribution matrix of the spring piece.

[0076] The process parameter adjustment module is used to adjust the local pressure and speed parameters of the next round of stamping based on the deviation value and the preset performance control model if the spring piece size deviation distribution matrix shows that the deviation in a certain area exceeds ±0.05 mm, thereby obtaining updated stamping process parameters;

[0077] The cyclic stamping optimization module is used to adopt the updated stamping process parameters, cyclically execute the stamping operation, and continuously collect dimensional deviation and performance consistency data to obtain the spring product data that ultimately meets the precision requirements.

[0078] It should be noted that the above examples are merely specific embodiments of the present invention. Obviously, the present invention is not limited to the above examples and is subject to numerous variations. All variations that can be directly derived or conceived by a person skilled in the art from the disclosure of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A dynamic control method for spring sheet stamping, characterized in that: The method comprises the following steps: Step S101, obtaining real-time measurement data of material thickness during the stamping process, scanning the surface of the spring material using a high-precision laser ranging sensor, and obtaining a material thickness distribution matrix, wherein the matrix elements represent the thickness values ​​of each area; Step S102 , calculating the theoretical pressure requirements of each region during the stamping process based on the material thickness distribution matrix, using a finite element analysis model, inputting thickness values ​​and material mechanical parameters, and obtaining a pressure distribution prediction result; Step S103: If the pressure distribution prediction result shows that the pressure deviation in a certain area exceeds a preset threshold value, which is ±5%, the local pressure output of the stamping die is adjusted by the servo motor control system to obtain optimized pressure distribution data; Step S104, obtaining optimized pressure distribution data, and calculating the stamping speed adjustment amount of each area in combination with a preset model of the influence of stamping speed on spring deformation to obtain a speed distribution adjustment matrix; Step S105: If the speed adjustment amount of a certain area in the speed distribution adjustment matrix exceeds the preset range, which is ±10%, the speed parameters of the stamping equipment are dynamically updated through the numerical control system to obtain the real-time adjusted stamping speed data; Step S106, using the real-time adjusted stamping speed data and pressure distribution data to drive the stamping equipment to perform a dynamic stamping operation, and obtain the size and performance measurement data of the formed spring piece; Step S107, obtaining the dimensional measurement data of the formed spring piece, scanning the surface of the spring piece with a high-precision three-dimensional scanner, and obtaining a spring piece dimensional deviation distribution matrix; Step S108: If the spring fragment size deviation distribution matrix shows that the deviation in a certain area exceeds ±0.05 mm, then the local pressure and speed parameters of the next round of stamping are adjusted according to the deviation value and the preset performance control model to obtain updated stamping process parameters; Step S109 , using the updated stamping process parameters, cyclically executing the stamping operation, and continuously collecting dimensional deviation and performance consistency data to obtain the final spring product data that meets the accuracy requirements.

2. A dynamic control method for spring sheet stamping according to claim 1, characterized in that: The step S101 includes: The surface of the shrapnel material is scanned in real time by a laser ranging sensor to obtain original thickness data and form an initial data set; According to the initial data set, a preprocessing method is used to perform denoising on the thickness data to obtain a smoothed thickness data set; If there are abnormal values ​​in the smoothed thickness data set, the data points exceeding the range are filtered out using a preset threshold range to determine a corrected thickness data set; Constructing a material thickness distribution matrix for the corrected thickness data set, wherein the matrix elements correspond to thickness values ​​of each region, and generating structured thickness distribution information; Based on the structured thickness distribution information, a support vector machine algorithm is used to classify the matrix data to determine whether there is an area with uneven thickness distribution; If it is determined that there is an area with uneven thickness distribution, the area position where the deviation is greater than a preset threshold is determined by comparing with a preset standard thickness distribution model; According to the position of the region where the deviation is greater than a preset threshold, adjustment parameter data is generated and reference information for subsequent stamping process optimization is output.

3. The dynamic control method for spring sheet stamping according to claim 1, characterized in that: The step S102 includes: Obtaining initial thickness data from a material thickness distribution matrix, and standardizing the initial thickness data using a data preprocessing method to obtain a processed thickness data set; According to the processed thickness data set, combined with a pre-established material mechanical parameter database, corresponding mechanical parameter values ​​are obtained to determine the mechanical characteristic data of each region; Using a finite element analysis model, meshing the mechanical property data and the thickness data set is performed to obtain mesh distribution data of each region; If there are abnormal values ​​in the grid distribution data, the data is corrected by a data smoothing method to obtain corrected grid distribution data; Calculating theoretical pressure values ​​of each region during the stamping process based on the corrected grid distribution data to obtain preliminary pressure distribution data; By iteratively optimizing the preliminary pressure distribution data and performing multiple simulations using the finite element method, the stability of the pressure demand in each area is determined, and the final pressure distribution prediction result is obtained; Based on the final pressure distribution prediction result, combined with the regional division characteristics of the stamping process, the pressure demand range of each area is determined, and a complete pressure distribution data set is output.

4. A dynamic control method for spring sheet stamping according to any one of claims 1 to 3, characterized in that: The step S103 includes: Acquire pressure distribution data from a sensor, and process the pressure distribution data using a pre-established analysis model to obtain preliminary prediction data; If there is a regional deviation in the preliminary forecast data that exceeds a preset threshold, the location and extent of the deviation area are determined by a data comparison tool to determine the scope of the area that needs adjustment; According to the deviation area range, a servo motor control system is used to generate an adjustment instruction to obtain a control signal for the local pressure of the stamping die; By transmitting the control signal to the execution unit of the stamping die, the local pressure output is adjusted to obtain adjusted pressure distribution data; If the adjusted pressure distribution data still has regional deviations exceeding the preset threshold, the position and extent of the deviation area are determined again through a cyclic comparison mechanism to obtain new adjustment instructions; According to the new adjustment instruction, the pressure distribution data is optimized and predicted using a support vector machine model to determine a final pressure output solution; By applying the final pressure output solution to the control system of the stamping die, optimized pressure distribution data is obtained.

5. A dynamic control method for spring sheet stamping according to any one of claims 1 to 3, characterized in that: The step S104 includes: acquiring initial pressure distribution data, and extracting pressure characteristic values ​​of each region from the initial pressure distribution data; Combined with the preset punch shape model, the influence of punching speed on spring deformation is calculated to obtain the speed influence factor of each area; If the speed impact factor of a certain area exceeds the preset threshold, the corresponding speed adjustment value is generated through an iterative optimization algorithm; If it does not exceed, the original speed parameter is kept unchanged and the adjustment value set is obtained; Based on the adjustment value set and in combination with the regional division data, a speed adjustment distribution matrix is ​​constructed to determine the speed adjustment vector for each region; The support vector machine algorithm is used to verify the distribution matrix, determine the rationality of the adjustment value of each region, and obtain a verified adjustment matrix; Based on the verified adjustment matrix and in combination with the real-time deformation data of the spring piece, the parameters of the preset punching model are dynamically updated to generate a final optimized speed adjustment strategy matrix.

6. A dynamic control method for spring sheet stamping according to any one of claims 1 to 3, characterized in that: The step S105 includes: Acquiring regional speed adjustment data in the speed distribution adjustment matrix, wherein the regional speed adjustment data is used to characterize speed changes of the stamping equipment; If the regional speed adjustment data exceeds a preset range, a preset threshold comparison method is used to determine whether to trigger a dynamic update; If the judgment result is to trigger dynamic update, the adjusted speed parameter instruction is generated through the CNC system; According to the speed parameter instruction, the speed parameter is transmitted to the stamping equipment through the numerical control system to obtain the stamping speed data after real-time adjustment; Using a data verification algorithm to extract key features from the stamping speed data, and determine whether the adjusted speed parameter meets a preset range; If the speed parameter does not meet the preset range, the speed distribution adjustment matrix is ​​corrected by an iterative optimization algorithm to obtain new regional speed adjustment data; According to the new regional speed adjustment amount data, the dynamic update and verification process is repeated to determine the final stable stamping speed data.

7. A dynamic control method for spring sheet stamping according to any one of claims 1 to 3, characterized in that: The step S106 includes: Acquire real-time stamping speed data and pressure distribution data, monitor the operating status of the stamping equipment through the sensor system, and obtain the initial operating parameter set; Based on the initial set of operating parameters, a preset logic rule is used to analyze the stamping speed and pressure distribution. If it is detected that the speed or pressure exceeds a preset threshold range, a dynamic adjustment mechanism is triggered to determine the adjusted operating parameters; The stamping equipment is driven to perform dynamic operations by using the adjusted operating parameters, and the real-time feedback operating data is continuously collected to obtain updated information on the equipment's operating status; Analyze the processing process of the formed spring piece according to the updated information of the equipment operation status, and if the pressure distribution during the processing is uneven, adjust the equipment control instructions to obtain an optimized operation instruction set; The optimized operation instruction set is used to control the stamping equipment to complete the spring piece forming task, and the size of the formed spring piece is measured to obtain the size data record; According to the dimension data record, the performance data of the formed spring piece is tested in combination with a performance testing tool to determine whether the performance data meets the preset standard and obtain the final measurement result data; Feedback information is generated through the final measurement result data and input into the real-time adjustment module, so as to perform a new round of optimization on the punching speed and pressure distribution and obtain an updated operating parameter configuration.

8. A dynamic control method for spring sheet stamping according to any one of claims 1 to 3, characterized in that: The step S107 includes: Scan the surface of the shrapnel with a high-precision 3D scanner to obtain original 3D point cloud data and obtain preliminary geometric information of the shrapnel surface; The original three-dimensional point cloud data is processed using a point cloud denoising process to remove noise points and outliers to obtain a clean point cloud data set; constructing a three-dimensional mesh model of the shrapnel surface based on the clean point cloud data set to generate a digital model of the shrapnel surface; Comparing the digital model with a preset standard shrapnel size model, calculating the geometric difference, and obtaining size deviation distribution data; Generating a deviation distribution matrix based on the size deviation distribution data, and processing the deviation distribution matrix using a heat map visualization technique to obtain an intuitive representation of the deviation distribution; Classify the deviation distribution matrix using a support vector machine algorithm to determine whether the deviation exceeds a preset threshold range. If so, mark it as an abnormal area; The specific location information of the abnormal area is extracted from the deviation distribution matrix, and a data record of the abnormal area is generated to obtain a basis for targeted adjustment.

9. A dynamic control method for spring sheet stamping according to any one of claims 1 to 3, characterized in that: The step S108 includes: Obtaining a shrapnel size deviation distribution matrix, wherein the deviation distribution matrix includes deviation data of multiple regions; extracting deviation data of a specific area from the deviation distribution matrix, determining whether the deviation value of the specific area exceeds a preset range, and obtaining a deviation determination result; If the deviation determination result indicates that the deviation value exceeds the preset range, extracting the specific deviation value from the deviation data of the specific area and calculating the degree of difference between the deviation value and the preset range; According to the degree of difference between the deviation value and the preset range, a pre-established performance control model is used to analyze the impact of the deviation data on the stamping process and obtain control suggestions; By combining the control suggestions with the output of the performance control model, the adjustment range of the local pressure is calculated to determine the pressure parameters for the next round of stamping; Analyze the matching requirements of speed parameters according to the adjustment range of the local pressure and determine the speed parameters of the next round of stamping; Generate updated stamping process parameters based on the pressure parameters and speed parameters, and determine whether the updated stamping process parameters meet the preset process parameter range; If the updated stamping process parameters do not meet the preset process parameter range, returning to the step of extracting specific deviation values ​​from the deviation data of the specific area for re-analysis; The final process adjustment plan is generated through the updated stamping process parameters, and the execution data for the next round of stamping is determined.

10. A dynamic control system for spring sheet stamping, characterized in that: The system is used to implement the dynamic control method for spring sheet stamping according to any one of claims 1 to 9, and the system comprises: Real-time thickness measurement module, used to obtain real-time measurement data of material thickness during the stamping process. It uses a high-precision laser ranging sensor to scan the surface of the shrapnel material to obtain a material thickness distribution matrix, where the matrix elements represent the thickness value of each area; Theoretical pressure calculation module is used to calculate the theoretical pressure requirements of each area during the stamping process based on the material thickness distribution matrix. It uses the finite element analysis model to input the thickness value and material mechanical parameters to obtain the pressure distribution prediction results; The pressure deviation detection and adjustment module is used to adjust the local pressure output of the stamping die through the servo motor control system to obtain optimized pressure distribution data if the pressure distribution prediction result shows that the pressure deviation in a certain area exceeds the preset threshold value (the preset threshold value is ±5%); The speed adjustment calculation module is used to obtain the optimized pressure distribution data, combine the preset model of the impact of stamping speed on spring deformation, calculate the stamping speed adjustment amount of each area, and obtain the speed distribution adjustment matrix; The speed parameter dynamic update module is used to dynamically update the speed parameters of the stamping equipment through the CNC system if the speed adjustment amount of a certain area in the speed distribution adjustment matrix exceeds the preset range (the preset range is ±10%) to obtain the real-time adjusted stamping speed data; The dynamic stamping execution module is used to use the real-time adjusted stamping speed data and pressure distribution data to drive the stamping equipment to perform dynamic stamping operations and obtain the size and performance measurement data of the formed spring piece; The dimensional deviation measurement module is used to obtain the dimensional measurement data of the formed spring piece. The surface of the spring piece is scanned by a high-precision 3D scanner to obtain the dimensional deviation distribution matrix of the spring piece. The process parameter adjustment module is used to adjust the local pressure and speed parameters of the next round of stamping based on the deviation value and the preset performance control model if the spring piece size deviation distribution matrix shows that the deviation in a certain area exceeds ±0.05 mm, thereby obtaining updated stamping process parameters; The cyclic stamping optimization module is used to adopt the updated stamping process parameters, cyclically execute the stamping operation, and continuously collect dimensional deviation and performance consistency data to obtain the spring product data that ultimately meets the precision requirements.

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