Manufacturing method of multi-station precision stamping die for isolation strip metal product

Abstract

The application belongs to the field of high-end equipment manufacturing industry, and relates to a manufacturing method of a multi-station precision stamping die for isolation strip metal products, which comprises the following steps: obtaining initial design data of a multi-station layout, calculating a potential range of error accumulation by analyzing the transfer path and positioning point distribution of a workpiece between stations, and obtaining a distribution model of workpiece transfer error; obtaining matching parameters of integrated design optimization, determining the deformation and size deviation of the workpiece between the stations by running a virtual machining process on a continuous stamping stability test platform, and obtaining real-time data of error accumulation control; and for the real-time data of error accumulation control, if the deformation exceeds a preset stability threshold, the guide gap and support point position of the precision guide mechanism are adjusted iteratively to determine the final guide system configuration scheme.

Description

Technical Field

[0001] This invention belongs to the field of high-end equipment manufacturing industry, specifically involving intelligent mold design and control technology, and in particular, a method for manufacturing multi-station precision stamping dies for metal products with isolation strips. Background Technology

[0002] The field of metal stamping die technology occupies a vital position in modern manufacturing, directly affecting the processing accuracy, production efficiency, and die life of metal products. Technological innovation is particularly crucial in the continuous stamping of high-precision metal products such as spacer strips. With the continuous improvement of industrial demands, the manufacturing methods of stamping dies urgently need to break through the limitations of traditional processes to adapt to production scenarios with complex structures and high precision requirements.

[0003] However, current technical solutions have significant limitations. Traditional single-station stamping dies rely on multiple clamping operations, which are not only inefficient but also generate significant cumulative errors due to repeated workpiece positioning. While multi-station dies improve production efficiency to some extent, their insufficient positioning accuracy often leads to increased risk of stamped parts deformation, and their die life is relatively short. Furthermore, for the forming requirements of complex separator strips, existing multi-station dies often require multiple sets of dies to work together, which not only increases production costs but also makes it difficult to guarantee process stability. These limitations reflect several core technical challenges. First, in multi-station continuous stamping, the transfer of workpieces between stations is prone to error accumulation due to material springback or positioning deviations, affecting the dimensional accuracy of the final product. Second, the integrated design of the die structure and the accuracy of the guiding system directly determine the stability of the stamping process, and existing solutions are insufficiently optimized in this regard, making it difficult to balance machining accuracy and die durability.

[0004] Effectively controlling error propagation in multi-station layouts and improving positioning accuracy through structural design have become pressing technical challenges. Therefore, the key issue of this research is how to develop a manufacturing method that can offset error accumulation, ensure high precision, and extend die life during continuous stamping by optimizing the multi-station integrated design and precision guiding mechanism of the mold. Solving this problem will provide a practical and feasible technical path for the efficient and stable production of metal separator strips. Summary of the Invention

[0005] This invention provides a method for manufacturing a multi-station precision stamping die for metal partition strip products, comprising the following steps:

[0006] The process begins by acquiring initial design data for a multi-station layout. By analyzing the workpiece's transfer path and positioning point distribution between stations, the potential range of error accumulation is calculated, resulting in a workpiece transfer error distribution model. Based on this model, a preset error threshold is used to determine if the error accumulation exceeds the requirements for high-precision machining. If it does, the station spacing and transfer angle are adjusted to determine the optimized layout parameters. For the optimized layout parameters, the geometric features and rigidity data of the guiding components are obtained from a precision guiding mechanism database. By simulating the workpiece's stress state in the guiding system, a preliminary scheme for improving positioning accuracy is derived. Based on this preliminary scheme, finite element analysis is used to calculate the stamping force distribution required to suppress material springback and the stress changes on the mold surface, determining the matching parameters for the integrated design-optimized guiding and mold structures. Finally, the integrated design-optimized matching parameters are obtained. By running a virtual machining process on a continuous stamping stability test platform, the deformation and dimensional deviation of the workpiece between multiple stations are assessed, yielding real-time data for error accumulation control. For real-time data on error accumulation control, if the deformation exceeds a preset stability threshold, the guide clearance and support point positions of the precision guide mechanism are iteratively adjusted to determine the final guide system configuration. Based on the final guide system configuration, digital twin technology is used to simulate the stress distribution and wear trend of the mold in continuous stamping, assessing the stability of the structural design and the degree of mold life extension, thus obtaining a mold life prediction model. Using the mold life prediction model, the stamping speed and workpiece transfer frequency are optimized under the constraints of high-precision machining requirements to determine the dynamic trend of continuous stamping stability and final operating parameters for the multi-station layout. For the final operating parameters of the multi-station layout, the entire stamping process is run in a virtual manufacturing environment to obtain workpiece dimensional accuracy and surface quality detection data, assessing the combined effect of error accumulation control and positioning accuracy improvement, and obtaining verification results for process optimization.

[0007] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0008] This invention discloses a method for manufacturing a multi-station precision stamping die for metal separator strips. The method establishes an error accumulation model by analyzing the workpiece transfer path and positioning point distribution, and adjusts the station layout according to high-precision machining requirements. Combining the integrated design of a precision guiding mechanism and die structure, this invention uses finite element analysis and digital twin technology to optimize the guiding system configuration, achieving error accumulation control and improved positioning accuracy during continuous stamping. Verification in a virtual manufacturing environment shows that this method can effectively optimize stamping speed and workpiece transfer frequency, significantly improve workpiece dimensional accuracy and surface quality, and extend die life. This invention provides an innovative process optimization scheme for high-precision continuous stamping, effectively solving the problems of error accumulation and positioning accuracy in multi-station layouts, and improving production efficiency and product quality. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the manufacturing method of a multi-station precision stamping die for a metal partition strip according to the present invention.

[0010] Figure 2 This is a schematic diagram of the manufacturing method of the multi-station precision stamping die for the metal isolation strip product of the present invention.

[0011] Figure 3 This is another schematic diagram of the manufacturing method of the multi-station precision stamping die for the metal product of the isolation strip according to the present invention. Detailed Implementation

[0012] The technical solutions of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0013] like Figure 1-3 The manufacturing method of the multi-station precision stamping die for the metal isolation strip in this embodiment may specifically include:

[0014] Step S101: Obtain the initial design data of the multi-station layout. By analyzing the transfer path and positioning point distribution of the workpiece between each station, calculate the potential range of error accumulation and obtain the distribution model of workpiece transfer error.

[0015] Initial design data is obtained from a multi-station layout. The transfer path and positioning point distribution characteristics of the workpiece between each station are analyzed to obtain preliminary characteristic data of workpiece transfer. Based on the preliminary characteristic data, the workpiece transfer path is decomposed using path analysis to determine the impact of the spacing between each station on error accumulation and to identify the error distribution interval on the critical path. If the error distribution interval on the critical path exceeds a preset threshold, it is corrected using the positioning point distribution data, and the positioning point offset is calculated to obtain the corrected error distribution data. For the corrected error distribution data, an evaluation framework for the potential range is constructed to analyze the superposition effect of error accumulation between each station and determine the potential error accumulation range. Based on the potential error accumulation range and combined with the transfer error characteristics, a regression analysis model is used to fit the error range to determine the distribution law of workpiece transfer error. Using the distribution law data, a distribution model of workpiece transfer error is constructed, and the influence of each parameter in the model on the error range is analyzed to obtain the final error distribution model parameters. Based on the final error distribution model parameters, an error control strategy under a multi-station layout is generated to determine the impact of adjusting the spacing between each station on error accumulation and to determine the optimized layout design data.

[0016] Specifically, when obtaining the initial design data for the multi-station layout, a production layout model containing 5 stations can be generated using digital modeling software. The position coordinates of each station are (0,0), (10.5,0), (20.0,0), (30.5,0), and (40.0,0), respectively, in meters. At the same time, the transfer path data of the workpiece between each station is recorded. For example, the path length of the workpiece from station 1 to station 2 is 10.5 meters, and the allowable path deviation is ±0.1 meters. Next, the transfer paths and positioning point distribution of the workpieces between each workstation were analyzed. An algorithm was used to calculate the potential range of accumulated errors. Specifically, a Monte Carlo simulation method was employed, assuming that the positioning error at each workstation follows a normal distribution with a mean of 0 and a standard deviation of 0.05 meters. Through 10,000 random simulations, the cumulative error of the workpiece from workstation 1 to workstation 5 was calculated. The results showed that the error range was between -0.22 meters and +0.25 meters, with 95% of the error values ​​concentrated between -0.18 meters and +0.20 meters. Subsequently, a distribution model of the workpiece transfer error was constructed based on this data. A Gaussian distribution was used to fit the cumulative error data, resulting in a distribution function with a mean of 0.015 meters and a standard deviation of 0.09 meters. Statistical analysis verified the accuracy of the model, showing an error fit of over 98%. To further optimize the layout, the error distribution model can be linked to production cycle data based on business needs. Assuming each workstation has a processing time of 2 minutes, workstations with larger errors can reduce their errors by adjusting equipment precision or adding calibration steps. For example, optimizing the standard deviation of the positioning error at workstation 3 from 0.05 meters to 0.03 meters reduces the cumulative error range to -0.18 meters to +0.21 meters after recalculation, thereby improving overall production accuracy. Through the above methods, from initial data acquisition to error modeling and optimization, a complete technical logic chain is formed, ensuring the scientific nature and operability of the multi-workstation layout design.

[0017] Step S102: Based on the distribution model of workpiece transfer error, a preset error threshold is used to determine whether the cumulative error exceeds the range required for high-precision machining. If it does, the optimized layout parameters are determined by adjusting the station spacing and transfer angle of the multi-station layout.

[0018] Transfer error data during multi-station machining is acquired. This data is collected and feature-extracted using a distribution model to obtain the regularity characteristics of the error distribution. Based on these regularity characteristics, the cumulative error value for each station is calculated to determine the specific range of the cumulative error. A preset threshold is used to compare the specific range of the cumulative error. If the cumulative error exceeds the preset threshold, a high-precision machining requirement determination logic is triggered, resulting in a machining requirement assessment. If the assessment result indicates that the high-precision machining requirement is not met, a structural analysis is performed on the multi-station layout to extract the configuration data of the station spacing and transfer angle, obtaining initial values ​​for the layout parameters. Based on the initial values ​​of the layout parameters and the distribution characteristics of the cumulative error, a genetic algorithm is used to iteratively adjust the station spacing and transfer angle to determine the optimized layout parameter configuration. For the optimized layout parameter configuration, the distribution of transfer error and cumulative error is recalculated to obtain the adjusted error distribution characteristics. The adjusted error distribution characteristics are compared with a preset threshold a second time. If the error still exceeds the preset threshold, the process returns to the layout parameter adjustment stage for a new round of optimization to obtain the final parameter configuration that meets the processing requirements.

[0019] Specifically, regarding the distribution model of workpiece transfer errors, a mathematical model is first established to describe the error distribution. Assuming the error follows a normal distribution with a mean of 0 and a standard deviation of 0.5 mm, 10,000 random error samples are generated using Monte Carlo simulation to calculate the probability distribution of the cumulative error. The maximum cumulative error at the 95% confidence interval is found to be 1.2 mm. Next, an error threshold of 0.8 mm is set for high-precision machining requirements. Comparison reveals that the current cumulative error of 1.2 mm exceeds the threshold, and the system automatically triggers an optimization process. Subsequently, the error accumulation judgment stage begins. The system calls a preset algorithm to compare the cumulative error with the threshold item by item. Analysis shows that the main error originates from the cumulative deviation caused by excessive transfer distance between workstations. Specifically, when the distance between the 3rd and 4th workstations is 500 mm, the error contribution accounts for 40%. To optimize the layout, the system employs a genetic algorithm to adjust the multi-station layout parameters. The initial population contains 50 layout schemes, each including station spacing and transfer angle. The objective function is to minimize the cumulative error. After 100 iterations, the optimal solution is found to shorten the spacing between the 3rd and 4th stations to 300 mm and adjust the transfer angle from 45 degrees to 30 degrees. The recalculated cumulative error is 0.7 mm, below the threshold of 0.8 mm, meeting the high-precision requirements. The entire process is automated. The error distribution model parameters are updated and the layout optimization results are stored in the database in real time, forming a closed-loop feedback. If subsequent processing requirements change, the model parameters can be further adjusted based on historical data to ensure continuous error control.

[0020] Step S103: For the optimized layout parameters, obtain the geometric features and rigidity data of the guide components from the database of precision guide mechanisms, and obtain a preliminary scheme to improve positioning accuracy by simulating the force state of the workpiece in the guide system.

[0021] Geometric and rigidity data of the guide components are retrieved from the database. Preliminary matching of layout parameters is performed to determine the basic configuration combination of the guide components. Based on the basic configuration combination, a stress state model simulating the workpiece is constructed. Finite element analysis is used to obtain deformation distribution data under the stress state. The deformation distribution data is used to analyze the changes in the geometric characteristics of the guide components under stress to determine the deviation range of the positioning accuracy. If the deviation range exceeds a preset threshold, the geometric parameters are adjusted, and the simulation results are obtained again. Based on the adjusted geometric parameters and the rigidity data, the overall stability of the guiding mechanism is evaluated to obtain a stability evaluation index. Based on the stability evaluation index, the matching relationship between the layout parameters and the guide components is optimized. If the stability index does not meet the standard, the layout parameters are adjusted through iterative calculation to determine the optimized parameter combination. Using the optimized parameter combination, the stress state of the workpiece in the guiding system is re-simulated to obtain the final positioning accuracy data.

[0022] Specifically, for the optimized layout parameters, the geometric features and rigidity data of the guide components are first extracted from the database of the precision guiding mechanism. Assuming the database stores the guide component's length of 200.5 mm, width of 50.3 mm, thickness of 10.2 mm, and Young's modulus of the material of 210 GPa, the SQL query is automatically invoked through the data interface to filter out component information that meets the design parameters. This data is then stored in a structured format for subsequent calculations. Next, the stress state of the workpiece in the guiding system is simulated using finite element analysis software. The workpiece weight is set to 5.2 kg, the applied force to 100 N, and the friction coefficient of the guide component's contact surface to 0.15. The model is divided into 10,000 elements through mesh generation. A static analysis algorithm is used to calculate the stress distribution and deformation of the workpiece at different locations. For example, the maximum deformation is 0.002 mm, and the maximum stress is 50 MPa. The analysis results show that insufficient rigidity of the guide component in the stress concentration area may lead to positioning errors. Based on this, a preliminary positioning accuracy improvement scheme is proposed. By optimizing the contact point layout of the guide component using algorithms, it is calculated that adjusting the contact point spacing from the original 50 mm to 40 mm can reduce deformation to 0.0015 mm. Simultaneously, combined with rigidity data, it is recommended to increase the thickness to 12.2 mm at key stress points to improve overall rigidity, with the positioning accuracy expected to improve from ±0.01 mm to ±0.005 mm. The above process is completed through automated system calculation and analysis. The logical relationships between the data are close; for example, geometric features directly affect the stress simulation results, and the simulation results provide a basis for the optimization scheme, ensuring the feasibility of the scheme and the scientific nature of the accuracy improvement. Furthermore, to further verify the effectiveness of the scheme, the optimized parameters can be fed back to the database, forming a closed-loop verification mechanism to ensure the accuracy of subsequent design iterations.

[0023] Step S104: Based on the preliminary plan for improving positioning accuracy, use finite element analysis technology to calculate the stamping force distribution and mold surface stress changes required to suppress material springback, and determine the matching parameters of the integrated design optimization guide and mold structure.

[0024] Using finite element analysis (FEM), springback characteristic data of the material is obtained from a preset material model. Combined with the distribution pattern of the stamping pressure, an initial stress variation distribution map is calculated. Based on this initial stress variation distribution map, the stress variation on the mold surface is simulated and analyzed using the finite element method to obtain the response characteristics of the mold structure under different stamping pressures. If the response characteristics show that the stress variation on the mold surface exceeds a preset threshold range, the distribution pattern of the stamping pressure is adjusted, and the stress variation distribution map is recalculated until a response characteristic that meets the requirements is obtained. For the adjusted stress variation distribution map, optimized guiding parameters for integrated design are obtained, and matching relationship data adapted to the mold structure is derived. Using this matching relationship data, the optimal correspondence between the stamping pressure and the mold structure is determined, generating optimized mold structure design parameters. The optimized mold structure design parameters are used to simulate and verify the improvement in positioning accuracy, determining whether the preset accuracy target is met. If the simulation verification result does not meet the preset accuracy target, the mold structure design parameters are readjusted based on the optimized guiding parameters, and the calculation is iterated until a final design scheme that meets the positioning accuracy requirements is obtained.

[0025] Specifically, regarding the preliminary scheme for improving positioning accuracy, finite element analysis (FEM) technology is used to calculate the required stamping force distribution and mold surface stress changes to suppress material springback. The matching parameters between the integrated design optimization guide and the mold structure are then determined. The specific implementation method is as follows: First, a geometric model of the stamping material is established using finite element analysis software (such as ANSYS). Assuming the material is a steel plate with a thickness of 2.0 mm and a yield strength of 350 MPa, an elastoplastic constitutive model is used for simulation. Tetrahedral elements are used for mesh generation, with the mesh size controlled within 0.5 mm to ensure calculation accuracy. Boundary conditions are then set, with an initial stamping force of 10 kN, gradually increasing to 50 kN in 5 kN increments. The springback amount of the material under different pressures is calculated, obtaining the nonlinear relationship curve between springback and pressure. Through regression analysis, the stamping force distribution function is derived, for example, P(x) = 10 + 2.5x (where x is the mold contact surface position), thus determining the optimal pressure distribution for suppressing springback to be in the range of 30 kN to 40 kN. Secondly, based on the aforementioned pressure distribution, the stress variation on the mold surface was analyzed. The mold material was set to high-strength steel with a hardness of HRC60. The maximum stress on the mold surface under 30kN pressure was calculated to be 800MPa. The Von Mises criterion was used to determine if it exceeded the material's yield strength of 850MPa. If it did not, the design feasibility was verified. Simultaneously, the location data of stress concentration areas were recorded, with errors controlled within 0.1mm, providing a basis for subsequent optimization. Finally, combining the stamping force distribution and stress analysis results, the guiding parameters for integrated design optimization were determined. Assuming a guiding accuracy requirement of 0.01mm, the matching gap between the mold structure and the guide pin was calculated. A genetic algorithm was used for optimization, with an initial population size of 100 and 50 iterations, yielding an optimal gap value of 0.02mm. The stability at a stamping frequency of 60 times / minute was verified, ensuring a positioning error of less than 0.005mm. Through these methods, a complete logical chain from material springback suppression to mold structure optimization was formed, and the data-driven analysis process provided precise guidance for the design.

[0026] Step S105: Obtain the matching parameters of integrated design optimization. By running a virtual machining process on a continuous stamping stability test platform, determine the deformation and dimensional deviation of the workpiece between multiple stations and obtain real-time data for error accumulation control.

[0027] A virtual machining process is used to simulate the stamping stability environment, collecting deformation and dimensional deviation data of the workpiece across multiple workstations to obtain preliminary error distribution information. Based on this preliminary error distribution information, a dynamic monitoring model for error accumulation is constructed. A support vector machine algorithm is used to classify real-time data, determine error accumulation trends and outliers, and identify the sources of deviation at key workstations. For the sources of deviation at these key workstations, the workpiece deformation characteristics across multiple workstations are analyzed, obtaining the correlation matrix between deformation and dimensional deviation to identify the main factors affecting error accumulation. If these main factors exceed a preset threshold, the matching parameters of the integrated design are adjusted to generate an adjusted parameter configuration scheme, resulting in an optimized parameter combination. Using this optimized parameter combination, the virtual machining process is rerun, collecting new real-time data to determine if error accumulation is under control and obtaining an updated error distribution state. Based on the updated error distribution state, the stamping stability is verified to meet design requirements. Statistical analysis tools are used to evaluate the optimization results, yielding the final error control effect. If the final error control effect does not meet the preset standard, the parameter adjustment and virtual processing flow are executed repeatedly to continuously acquire real-time data, judge the improvement of the error accumulation, and determine the optimal matching parameter scheme.

[0028] Specifically, in the process of obtaining the matching parameters for integrated design optimization, the virtual machining process is first simulated on a continuous stamping stability test platform. The platform is assumed to use finite element analysis software to model the stress and deformation of the workpiece across multiple stations. The workpiece material is set to Q235 steel with a thickness of 2.0 mm, the number of stamping stations is 5, the stamping force at each station is 500 kN, and the simulation time step is 0.01 seconds. The deformation at each station is calculated; for example, the deformation at the first station is 0.05 mm, and at the second station it accumulates to 0.08 mm. The deformation data is then superimposed station by station using an accumulation algorithm, and the final total deformation at the fifth station is 0.25 mm. The analysis shows that the deformation increases linearly with the number of stations, with a correlation coefficient R² of 0.98, indicating high model prediction accuracy. Next, regarding the assessment of dimensional deviations, the platform automatically collects dimensional data from each workstation. Assuming the design dimension is 100.00 mm, the actual measured dimension at the first workstation is 100.02 mm, and the cumulative deviation at the fifth workstation reaches 0.15 mm. The total deviation is calculated using the error propagation formula Δtotal=∑Δi (i=1 to 5), and combined with the Statistical Process Control (SPC) method, the standard deviation is found to be 0.03 mm, indicating that dimensional stability is controllable within a tolerance range of ±0.1 mm. Subsequently, in the real-time data acquisition stage for error accumulation control, the system collects workstation data every 0.1 seconds via sensor interfaces. Using a Kalman filter algorithm to filter out noise, the optimized error data fluctuation range is reduced from ±0.05 mm to ±0.02 mm. Analysis shows that the filtered data is closer to the true value, improving the error accumulation control accuracy by approximately 60%. To establish a logical chain, error data is correlated with subsequent process parameter optimization. Assuming the accumulated error exceeds 0.2mm, the system automatically adjusts the punching force to 480kN. Simulation verification shows that the deformation decreased to 0.18mm after adjustment, proving the effectiveness of parameter optimization. Through this method, integrated design parameters are optimized, ensuring processing stability and accuracy.

[0029] Step S106: For the real-time data of error accumulation control, if the deformation exceeds the preset stability threshold, the final guidance system configuration scheme is determined by iteratively adjusting the guide gap and support point position of the precision guidance mechanism.

[0030] Real-time deformation measurement data is acquired and compared with a preset stability threshold to determine if it exceeds the threshold range. If the deformation measurement data exceeds the stability threshold range, feature extraction is performed on the accumulated error data, and a support vector machine algorithm is used to classify the features of the accumulated error data to obtain the main influencing factors of error accumulation. Based on the main influencing factors, relevant parameters of the precision guidance mechanism are acquired, and the current state of the guide gap and support points is evaluated to determine the priority direction of adjustment. An iterative adjustment method is used to fine-tune the parameters of the guide gap, and the trend of deformation measurement change after adjustment is obtained through simulation analysis to determine if it is within the stability threshold range. If the trend of deformation measurement change does not meet the stability threshold range, the position of the support points is optimized, and a new position configuration scheme is obtained by calculating the geometric distribution to determine the adjustment scheme of the support points. Based on the adjustment scheme of the guide gap and the support points, the operating data of the precision guidance mechanism is acquired, and the stability of the configuration scheme is verified by data monitoring to obtain the system optimization result. If the system optimization results show deviations, the data monitoring feedback is sent to the error accumulation analysis stage to obtain new influencing factors and determine the iterative direction for subsequent adjustments.

[0031] Specifically, for real-time data processing for error accumulation control, deformation data is first collected via sensors. Assuming a device's real-time deformation during operation is 2.5 mm, while the preset stability threshold is 1.8 mm, the system automatically determines that the deformation exceeds the threshold, triggering an adjustment mechanism. The data is processed using a time-series analysis algorithm, calculating a deformation change rate of 0.3 mm / hour, predicting it may reach 2.8 mm within the next hour, further confirming the need for intervention. Next, the system enters an iterative adjustment phase, optimizing the guide gap and support point positions of the precision guide mechanism. The initial guide gap is set to 0.5 mm, and the support points are distributed at both ends of the device with a spacing of 500 mm. The system simulates the adjustment effect based on a finite element analysis model, calculating that reducing the guide gap to 0.4 mm and simultaneously adjusting the support point spacing to 450 mm can control the deformation within 1.7 mm, meeting the threshold requirement. During the iteration process, each adjustment step is 0.05 mm, and the support point movement step is 10 mm. After three iterations, the optimal parameters are determined. Subsequently, the system generates the final guide system configuration scheme, automatically uploads the adjusted parameters to the control module, locks the guide gap at 0.4 mm, updates the support point positions to a spacing of 450 mm, and verifies through simulation that the deformation is stable at 1.6 mm, below the threshold of 1.8 mm, ensuring system stability. The entire process forms a closed-loop logic through data acquisition, algorithm analysis, and automated adjustment. If the deformation is still unstable, the system will correlate with equipment load data to analyze whether it is caused by uneven load. For example, if the load distribution deviation reaches 10%, the force distribution ratio of the support points will be further adjusted to ensure effective error control.

[0032] Step S107: Based on the final guide system configuration scheme, digital twin technology is used to simulate the stress distribution and wear trend of the mold in continuous stamping, to determine the stability of the structural design and the degree of realization of mold life extension, and to obtain the mold life prediction model.

[0033] A virtual simulation environment for a mold in continuous stamping is constructed using digital twin technology. The combined parameters of the guiding system and configuration scheme are dynamically mapped to obtain the mold's operating status data under different working conditions, yielding preliminary simulation results. Based on these preliminary simulation results, stress distribution characteristics during continuous stamping are extracted, and combined with the dynamic changes in wear trends, key stress areas and potential wear points are identified. The structural design stability of the key stress areas and potential wear points is evaluated using finite element analysis. If the evaluation results show that the stress values ​​in certain areas exceed a preset threshold, the parameters of the configuration scheme are adjusted to determine the optimization direction of the structural design. The adjusted configuration scheme is then re-simulated using digital twin technology to re-simulate the stress distribution and wear trends during continuous stamping, obtaining adjusted operating status data to determine the mold's performance under optimized configuration. Based on the adjusted operating status data, a machine learning-based mold life prediction model is constructed. The support vector machine algorithm is used to train the model on historical and simulated data to obtain the prediction model's output. If the deviation between the prediction model's output and the actual operating data exceeds a preset threshold, the model parameters are iteratively adjusted to determine the final life prediction accuracy.

[0034] Specifically, based on the final guide system configuration, digital twin technology is used to simulate and analyze the stress distribution and wear trend of the die during continuous stamping to evaluate the stability of the structural design and the extent to which the die life extension can be achieved, and to construct a life prediction model. First, by constructing a digital twin model of the die, the geometric parameters and material properties of the actual die are input into the system. For example, the die material is Cr12MoV steel with a hardness of 60 HRC, the stamping frequency is set to 300 times per minute, and the stamping force is 500 kN. Combined with the finite element analysis software ANSYS, stress distribution simulation is performed, revealing that the maximum stress value at key parts of the die, such as the guide post and the punch, is 850 MPa, far below the material yield strength of 1200 MPa, indicating that the structural design has stability under static load. Next, a wear simulation algorithm was introduced, using the Archard wear model with a friction coefficient of 0.15 and a sliding distance of 0.5 mm per stamping cycle. After simulating 1 million stamping cycles, the wear depth on the guide post surface was calculated to be 0.08 mm. Combined with historical data analysis, it was found that for every 0.01 mm increase in wear depth, the die accuracy decreased by 1%, thus inferring that the die accuracy under the current design can be maintained above 90%. Subsequently, a life prediction model was constructed using a machine learning algorithm. The Support Vector Machine (SVM) method was selected, with input parameters such as stress, wear depth, and number of stamping cycles. The training dataset contained 5000 sets of die operation data. The prediction results showed that the die life could reach approximately 1.5 million stamping cycles, with a deviation of less than 5% from actual tests, verifying the model's reliability. Finally, the prediction model was integrated with the production monitoring system to collect stamping data in real time, such as pressure fluctuations of ±10 kN and temperature changes of ±5℃, dynamically updating wear trends and optimizing maintenance cycles. For example, when the wear depth approaches 0.1 mm, an automatic maintenance reminder is generated to ensure production continuity. Through the above methods, a complete technical loop is formed from stress analysis to wear prediction and life modeling, which fully demonstrates the application value of digital twin technology in mold design optimization.

[0035] Step S108: Obtain the mold life prediction model, optimize the stamping speed and workpiece transfer frequency under the constraint of high-precision processing requirements, determine the dynamic change trend of continuous stamping stability, and determine the final operating parameters of the multi-station layout.

[0036] By combining historical and real-time monitoring data, a dataset of influencing factors related to die life is obtained. This dataset includes stamping speed, transfer frequency, and stability indicators during continuous stamping. Based on this dataset, a support vector machine model is used to predict and analyze die life, extracting features based on dynamic changes to obtain a die life prediction result. If the prediction result is lower than a preset threshold, the stamping speed and transfer frequency are adjusted to obtain the adjusted parameter combination, and the degree of influence of the parameter combination on life prediction is determined. Using the adjusted parameter combination, the stability indicators during continuous stamping are analyzed, and the dynamic change trend is monitored in real time to obtain a stability analysis result. Based on the stability analysis result and combined with high-precision machining constraints, the operating parameters of the multi-station layout are optimized and adjusted to obtain an optimized operating parameter configuration. Using the optimized operating parameter configuration, the operating state of the multi-station layout is simulated, and the machining constraints and the stability analysis result are verified to determine the final operating parameter scheme. Based on the final operating parameter scheme, a control strategy suitable for continuous stamping is generated, and the dynamic change trend is continuously monitored to obtain a long-term stability evaluation result.

[0037] Specifically, regarding the construction and optimization of the die life prediction model, a prediction model was first established through historical data collection and machine learning algorithms. Assuming 1000 sets of stamping die usage data were collected, including stamping counts, material hardness, and die wear rate, a random forest algorithm was used for training. The model's prediction accuracy reached 85%. Analysis showed that the correlation coefficient between die life and stamping speed was 0.75, indicating that speed has a significant impact on lifespan. Next, under the constraint of high-precision machining requirements, the stamping speed and workpiece transfer frequency were optimized. The machining accuracy error range was set to ±0.01mm. Through genetic algorithm optimization, the initial stamping speed was set to 200 times / minute, and the workpiece transfer frequency to 10 times / minute. After 50 iterations, the optimal speed was obtained: 180 times / minute and the transfer frequency to 12 times / minute. At this point, the accuracy error was controlled within ±0.008mm, and the die life was extended by approximately 15%. Subsequently, the dynamic trend of continuous stamping stability was assessed. Using time-series analysis, based on hourly vibration data (assuming a vibration threshold of 0.5 mm / s), the stability trend for the next 24 hours was predicted using the ARIMA model. It was found that when the speed exceeded 190 cycles / minute, the vibration value increased to 0.6 mm / s, and stability decreased by 20%. Therefore, parameters were adjusted to ensure stability. Finally, the final operating parameters for the multi-station layout were determined. Simulation software was used to model the production process of six stations, setting the stamping load of each station to be uniformly distributed with a maximum load of 5000 N. Analysis of 10,000 simulations using the Monte Carlo method revealed that the total load fluctuation rate under the optimal layout was only 5%. The final parameters were a speed of 175 cycles / minute and a transfer frequency of 11 cycles / minute for each station, ensuring a 10% increase in production efficiency while maintaining stability. This process forms a closed-loop logic through data-driven and algorithmic optimization. The output of the predictive model directly guides parameter optimization, and the stability analysis results are fed back to layout adjustments, ensuring consistent parameters across all stages.

[0038] Step S109: For the final operating parameters of the multi-station layout, the complete stamping process is run in the virtual manufacturing environment to obtain the detection data of workpiece dimensional accuracy and surface quality, judge the comprehensive effect of error accumulation control and positioning accuracy improvement, and obtain the verification results of process optimization.

[0039] A digital model of a multi-station layout is constructed using a virtual manufacturing environment. The operating parameters of the stamping process are loaded to simulate the complete production process, obtaining initial simulation data for workpiece dimensions and surface quality. Based on this initial simulation data, the deviation distribution of workpiece dimensions is analyzed, and a preset threshold is used for comparison. If the deviation exceeds the threshold range, the error value of the corresponding station is recorded, resulting in a distribution of dimensional accuracy anomalies. For this distribution, feature values ​​of error accumulation are extracted. Combined with the surface quality simulation data, if the surface quality index is lower than a preset standard, the relevant stations are marked, identifying key areas of influence for error accumulation. From these key areas, simulation parameters for positioning accuracy are obtained, and the transmission path of positioning deviations between stations is analyzed to determine the contribution of positioning accuracy to the overall impact. Based on the contribution of positioning accuracy, the virtual parameters of the multi-station layout are adjusted, and the stamping process simulation is rerun to obtain adjusted workpiece dimensions and surface quality data, determining the optimized error control effect. Using the optimized error control effect, a support vector machine algorithm is employed to classify and compare the data before and after adjustment, obtaining a quantitative result of the process optimization.

[0040] Specifically, for optimizing the final operating parameters of the multi-station layout, a digital twin model containing five stamping stations is first constructed in a virtual manufacturing environment. Each station is set with a stamping force of 200 tons and a stamping speed of 30 times / minute. The complete stamping process is simulated using finite element analysis algorithms to calculate the deformation and stress distribution of the workpiece at each station. For example, the deformation at the first station is 0.5 mm, and the peak stress is 300 MPa. The data is then passed to the next station for cumulative calculation, ultimately obtaining the stress distribution map and predicted deformation values ​​for the entire process. Next, the dimensional accuracy and surface quality inspection data of the workpiece are acquired. Virtual laser scanning technology is used to simulate the inspection, setting the dimensional tolerance to ±0.05 mm and the surface roughness standard to Ra1.6. The algorithm compares the deviation between the virtual workpiece and the design model to calculate the cumulative dimensional error. For example, the total error is 0.12 mm, with the second station contributing 0.04 mm of error, while the surface quality score decreases to Ra2.0. Furthermore, the combined effect of error accumulation control and positioning accuracy was assessed. Statistical Process Control (SPC) was used to analyze the error distribution, setting a control limit of ±0.1mm. When the error exceeded this limit, the positioning parameters of the third station were adjusted, improving the positioning accuracy from 0.02mm to 0.01mm. The simulation was then rerun, reducing the error to 0.08mm, meeting the control requirements. Finally, the process optimization results were verified. By comparing data before and after optimization, the dimensional accuracy improvement rate reached 33.3%, and the surface quality score recovered to Ra1.8. Based on business requirements, the optimized parameters were fed back to the PLC control system of the actual production line, forming a closed-loop optimization. This ensured that the multi-station layout achieved the expected results in actual operation, logically forming a complete chain from virtual simulation to data analysis and parameter adjustment.

[0041] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and additions without departing from the principle of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. A method for manufacturing a multi-station precision stamping die for metal partition strips, characterized in that, The method includes the following steps: Step S101: Obtain the initial design data of the multi-station layout. By analyzing the transfer path and positioning point distribution of the workpiece between each station, calculate the potential range of error accumulation and obtain the distribution model of workpiece transfer error. Step S102: Based on the distribution model of workpiece transfer error, use a preset error threshold to determine whether the error accumulation exceeds the range required for high-precision machining. If it does, adjust the station spacing and transfer angle of the multi-station layout to determine the optimized layout parameters. Step S103: For the optimized layout parameters, obtain the geometric features and rigidity data of the guide components from the database of precision guide mechanisms. By simulating the force state of the workpiece in the guide system, obtain a preliminary scheme for improving positioning accuracy. Step S104: Based on the preliminary scheme for improving positioning accuracy, use finite element analysis technology to calculate the stamping force distribution and mold surface stress change required to suppress material springback, and determine the matching parameters of the integrated design optimized guide and mold structure. Step S105: Obtain the matching parameters of the integrated design optimization. By running a virtual machining process on a continuous stamping stability test platform, determine the deformation and dimensional deviation of the workpiece between multiple stations to obtain real-time data for error accumulation control. Step S106: For real-time data on error accumulation control, if the deformation exceeds the preset stability threshold, the guide gap and support point position of the precision guide mechanism are iteratively adjusted to determine the final guide system configuration scheme. Step S107: Based on the final guide system configuration scheme, digital twin technology is used to simulate the stress distribution and wear trend of the mold in continuous stamping, judging the degree of achievement of structural design stability and mold life extension, and obtaining a mold life prediction model. Step S108: Obtaining the mold life prediction model, the stamping speed and workpiece transfer frequency are optimized under the constraint of high-precision processing requirements to judge the dynamic change trend of continuous stamping stability and determine the final operating parameters of the multi-station layout. Step S109: For the final operating parameters of the multi-station layout, the entire stamping process is run in a virtual manufacturing environment to obtain workpiece dimensional accuracy and surface quality detection data, judging the comprehensive effect of error accumulation control and positioning accuracy improvement, and obtaining the verification results of process optimization.

2. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to claim 1, characterized in that, Step S101 includes: Initial design data is obtained from the multi-station layout. The transfer path and positioning point distribution characteristics of the workpiece between each station are analyzed to obtain preliminary characteristic data of workpiece transfer. Based on the preliminary feature data, the workpiece transfer path is decomposed using path analysis methods to determine the impact of the distance between each station on error accumulation and to identify the error distribution range on the critical path. If the error distribution range on the critical path exceeds a preset threshold, the error distribution data is corrected by using the positioning point distribution data, the positioning point offset is calculated, and the corrected error distribution data is obtained. Based on the corrected error distribution data, an evaluation framework for the potential range is constructed to analyze the cumulative effect of error accumulation across different workstations and determine the potential range of error accumulation. Based on the potential error accumulation range and combined with the transfer error characteristics, a regression analysis model is used to fit the error range and determine the distribution law of workpiece transfer error. Using the distribution data, a distribution model of workpiece transfer error is constructed, the influence of each parameter in the model on the error range is analyzed, and the final error distribution model parameters are obtained. Based on the final error distribution model parameters, an error control strategy for a multi-station layout is generated, the impact of adjusting the spacing between each station on error accumulation is determined, and the optimized layout design data is finalized.

3. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to claim 1, characterized in that, Step S102 includes: The transfer error data in the multi-station processing is acquired, and the transfer error data is collected and feature extracted through a distribution model to obtain the regularity characteristics of the error distribution; Based on the regularity of the error distribution, calculate the cumulative error value for each workstation and determine the specific range of the cumulative error; The specific range of the accumulated error is compared using a preset threshold. If the accumulated error exceeds the preset threshold, the judgment logic for high-precision processing requirements is triggered to obtain the judgment result of the processing requirements. If the judgment result is that the high-precision processing requirements are not met, then a structural analysis is performed on the multi-station layout to extract the configuration data of the station spacing and transfer angle, and the initial values ​​of the layout parameters are obtained. Based on the initial values ​​of the layout parameters and the distribution characteristics of the accumulated error, a genetic algorithm is used to iteratively adjust the workstation spacing and transfer angle to determine the optimized layout parameter configuration. For the optimized layout parameter configuration, the distribution of transfer error and error accumulation is recalculated to obtain the adjusted error distribution characteristics; The adjusted error distribution characteristics are compared with a preset threshold a second time. If the error still exceeds the preset threshold, the process returns to the layout parameter adjustment stage for a new round of optimization to obtain the final parameter configuration that meets the processing requirements.

4. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to any one of claims 1-3, characterized in that, Step S103 includes: Geometric feature data and rigidity data of the guide components are obtained from the database, and preliminary matching is performed based on the layout parameters to determine the basic configuration combination of the guide components; Based on the basic configuration combination, a stress state model of the simulated workpiece is constructed, and the deformation distribution data under the stress state is obtained by using the finite element analysis method. By analyzing the deformation distribution data, the geometric characteristics of the guide component under stress are determined, and the deviation range of the positioning accuracy is judged. If the deviation range exceeds the preset threshold, the geometric feature parameters are adjusted, and the simulation results are obtained again. Based on the adjusted geometric feature parameters and the rigidity data, the overall stability of the guidance mechanism is evaluated to obtain a stability evaluation index. Based on the stability evaluation index, the matching relationship between the layout parameters and the guide component is optimized. If the stability index fails to meet the standard, the layout parameters are adjusted through iterative calculation to determine the optimized parameter combination. Using the optimized parameter combination, the force state of the workpiece in the guiding system is re-simulated to obtain the final positioning accuracy data.

5. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to any one of claims 1-3, characterized in that, Step S104 includes: Using finite element analysis tools, the springback characteristic data of the material is obtained from the preset material model, and the initial stress change distribution diagram is calculated by combining the distribution law of impact force. Based on the initial stress variation distribution diagram, the stress variation on the mold surface is simulated and analyzed using the finite element method to obtain the response characteristics of the mold structure under different stamping pressure intensities. If the response characteristics show that the stress change on the mold surface exceeds the preset threshold range, then the distribution law of the stamping force is adjusted, and the stress change distribution map is recalculated until the response characteristics that meet the requirements are obtained. Based on the adjusted stress variation distribution diagram, the optimized guiding parameters of the integrated design are obtained, and the matching relationship data adapted to the mold structure is derived. By using the matching relationship data, the optimal correspondence between the stamping force and the mold structure is determined, and optimized mold structure design parameters are generated. The improved positioning accuracy was verified by simulation using the optimized mold structure design parameters to determine whether the preset accuracy target was met. If the simulation verification results do not meet the preset accuracy target, the mold structure design parameters are readjusted based on the optimized guiding parameters, and the calculation is repeated until the final design scheme that meets the positioning accuracy requirements is obtained.

6. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to any one of claims 1-3, characterized in that, Step S105 includes: By simulating the stamping stability environment through a virtual machining process, the deformation and dimensional deviation data of the workpiece are collected between multiple workstations to obtain preliminary error distribution information. Based on the preliminary error distribution information, a dynamic monitoring model for error accumulation is constructed. The support vector machine algorithm is used to classify and process real-time data, determine the error accumulation trend and outliers, and identify the sources of deviations at key workstations. For the sources of deviation at the key workstations, the workpiece deformation characteristics between multiple workstations are analyzed, the correlation matrix between deformation and dimensional deviation is obtained, and the main factors affecting error accumulation are determined. If the main factors exceed the preset threshold, the matching parameters of the integrated design are adjusted to generate an adjusted parameter configuration scheme and obtain an optimized parameter combination. By using the optimized parameter combination, the virtual processing flow is rerun, new real-time data is collected, it is determined whether the error accumulation is under control, and the updated error distribution status is obtained. Based on the updated error distribution, verify whether the stamping stability meets the design requirements, use statistical analysis tools to evaluate the optimization results, and obtain the final error control effect. If the final error control effect does not meet the preset standard, the parameter adjustment and virtual processing flow are executed repeatedly to continuously acquire real-time data, judge the improvement of the error accumulation, and determine the optimal matching parameter scheme.

7. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to any one of claims 1-3, characterized in that, Step S106 includes: Acquire real-time deformation measurement data, compare the deformation measurement data with a preset stability threshold, and determine whether it exceeds the stability threshold range. If the deformation measurement data exceeds the stability threshold range, feature extraction is performed on the error accumulation data, and the features of the error accumulation data are classified using the support vector machine algorithm to obtain the main influencing factors of error accumulation. Based on the main influencing factors, relevant parameters of the precision guidance mechanism are obtained, and the current status of the guide gap and support points is evaluated to determine the priority direction for adjustment. An iterative adjustment method is used to fine-tune the parameters of the guide gap. The trend of the deformation measurement after adjustment is obtained through simulation analysis to determine whether it is within the stability threshold range. If the trend of the deformation measurement does not meet the stability threshold range, the position of the support point is optimized, a new position configuration scheme is obtained by calculating the geometric distribution, and the adjustment scheme of the support point is determined. Based on the adjustment scheme of the guide gap and the support point, the operation data of the precision guide mechanism is obtained, the stability of the configuration scheme is verified by data monitoring, and the system optimization result is obtained. If the system optimization results show deviations, the data monitoring feedback is sent to the error accumulation analysis stage to obtain new influencing factors and determine the iterative direction for subsequent adjustments.

8. The method for manufacturing a multi-station precision stamping die for a metal partition strip according to any one of claims 1-3, characterized in that, Step S107 includes: A virtual simulation environment for the mold in a continuous stamping environment is constructed using digital twin technology. The combined parameters of the guiding system and the configuration scheme are dynamically mapped to obtain the operating status data of the mold under different working conditions and obtain preliminary simulation results data. Based on the preliminary simulation results, the stress distribution characteristics during the continuous stamping process are extracted, and the key stress areas and potential wear points are determined by combining the dynamic changes in wear trends. The structural design stability of the key stress areas and potential wear points is evaluated using the finite element analysis method. If the evaluation results show that the stress values ​​in some areas exceed the preset threshold, the parameters of the configuration scheme are adjusted to determine the optimization direction of the structural design. The digital twin technology is used to re-simulate the stress distribution and wear trend during continuous stamping process of the adjusted configuration scheme, obtain the adjusted operating status data, and determine the performance of the mold under the optimized configuration. Based on the adjusted operating status data, a machine learning-based mold life prediction model is constructed. The support vector machine algorithm is used to train the model on historical and simulated data to obtain the output results of the prediction model. If the deviation between the output of the prediction model and the actual operating data exceeds a preset threshold, the model parameters are iteratively adjusted to determine the final lifetime prediction accuracy.

9. A method for manufacturing a multi-station precision stamping die for a metal partition strip according to any one of claims 1-3, characterized in that, Step S108 includes: By using historical data and real-time monitoring data, a dataset of influencing factors related to die life is obtained. The dataset includes stamping speed, transfer frequency, and stability indicators during continuous stamping. Based on the dataset, a support vector machine model is used to predict and analyze the mold life, and features are extracted for dynamic changes to obtain the mold life prediction results. If the prediction result is lower than the preset threshold, the stamping speed and transfer frequency are adjusted to obtain the adjusted parameter combination and determine the degree of influence of the parameter combination on the life prediction. By using the adjusted parameter combination, the stability index of the continuous stamping process is analyzed, and the dynamic change trend is monitored in real time to obtain the stability analysis results. Based on the stability analysis results and combined with the high-precision machining constraints, the operating parameters of the multi-station layout are optimized and adjusted to obtain the optimized operating parameter configuration. The optimized operating parameters are used to simulate the operation of a multi-station layout. The operation is then verified against the processing constraints and the stability analysis results to determine the final operating parameter scheme. Based on the final operating parameter scheme, a control strategy suitable for continuous stamping is generated, and the dynamic change trend is continuously monitored to obtain the long-term operational stability assessment results.

Images