Stamping die manufacturing control method and system for new energy motor iron core

Abstract

The invention relates to the field of progressive stamping die manufacturing, in particular to a stamping die manufacturing control method and system for a new energy motor iron core, and the method comprises the following steps: analyzing the material performance and the stamping effect through artificial intelligence, and intelligently recommending an optimal material combination; the die is quickly verified by using digital design and 3D printing, and the design is optimized by combining virtual simulation; developing an intelligent control system, dynamically adjusting the position of the mold, and ensuring accurate alignment of procedures; a big data platform is established, and punching parameters are automatically optimized through machine learning; online detection is integrated, and mold parameters are adjusted in real time to correct deviation; and mold health monitoring and predictive maintenance are implemented, so that shutdown is reduced. The steps jointly achieve intelligentization and self-adaptive optimization of stamping die manufacturing.

Description

Technical Field

[0001] The present application relates to the field of progressive stamping die manufacturing, and in particular to a stamping die manufacturing control method and system for new energy motor cores. Background Art

[0002] The progressive stamping die for high-speed stator and rotor cores of micro motors is a die that punches stator and rotor punchings and directly stacks the stator and rotor punchings into stator and rotor cores. This can save the riveting or welding process of the stator core and the twisting process of the rotor core. The length of the stator and rotor core and the angle of the rotor skew can be changed by adjusting the controller in the die. This die makes the manufacturing time of a single stator and rotor core short, the production batch is large, the manufacturing cost is low, and the produced stator and rotor core has high dimensional accuracy and good electrical performance. The existing progressive stamping dies have high processing efficiency for some stator and rotor cores with relatively simple structures. However, for some stator and rotor cores with complex structures, the precision is low and the finished product yield rate of the manufactured stator and rotor cores will be relatively low. Therefore, it is necessary to further improve the progressive stamping dies used for the production of stator cores with complex structures to improve the precision of the dies, thereby improving the finished product yield rate of stator and rotor cores with complex structures. Summary of the invention

[0003] In order to improve the precision of the progressive stamping die, the present application provides a stamping die manufacturing control method and system for new energy motor cores.

[0004] In a first aspect, the present application provides a stamping die manufacturing control method for a new energy motor core, comprising the following steps: Use artificial intelligence algorithms to analyze the correlation between material properties and stamping effects, and intelligently recommend the best material combination; Use digital design tools and virtual simulation technology to predict the behavior of the mold in the actual stamping process and optimize the mold design; Develop an intelligent control system to monitor the step distance during the stamping process in real time, dynamically adjust the mold position based on real-time data, and ensure precise alignment between consecutive workstation processes; Establish a big data analysis platform to collect and analyze key parameters in the stamping process, such as pressure distribution, die temperature, and material flow, and automatically adjust stamping parameters through machine learning algorithms to achieve intelligent optimization of the manufacturing process; Integrated online detection equipment monitors the size and shape tolerance of the core in real time. Once a deviation is found, the adaptive adjustment mechanism is immediately triggered to automatically fine-tune the mold parameters. Establish a mold health monitoring system to monitor mold status in real time through IoT technology, predict maintenance needs, and reduce unplanned downtime.

[0005] Optionally, the method of analyzing the correlation between material properties and stamping effects by using an artificial intelligence algorithm and intelligently recommending the best material combination comprises the following steps: Establish a material properties database, including the material's microstructure, physical properties and stamping behavior; Use machine learning to predict the properties of new materials and intelligently design customized material formulations; Verify new material performance through experiments, iteratively optimize material formulations, and ensure initial material quality during stamping; An automated pretreatment system is used to intelligently adjust pretreatment parameters according to material characteristics to improve the stamping adaptability of the material.

[0006] Optionally, the method of using a digital design tool to predict the behavior of the mold in the actual stamping process through virtual simulation technology and optimizing the mold design includes the following steps: Create a digital twin model of the mold to achieve real-time synchronization between mold design and actual manufacturing process; Use deep learning algorithms to simulate the behavior of the mold during the stamping process with high precision, predict and optimize the mold design; Through virtual reality technology, the mold design is immersively verified to ensure the innovation and functionality of the design; Based on the simulation and verification results, multiple rounds of design iterations are performed to ensure the optimization of the mold design.

[0007] Optionally, the development of an intelligent control system to monitor the step distance during the stamping process in real time, dynamically adjust the mold position according to the real-time data, and ensure accurate alignment between consecutive workstation processes, includes the following steps: Deploy a high-precision sensor network on the die to monitor the step changes during the stamping process in real time; Use machine learning to predict step deviation, adjust mold position in advance, and achieve adaptive control; Develop a dynamic step adjustment algorithm to automatically optimize the mold position based on real-time data to ensure precise alignment between consecutive workstations; Evaluate the step control effect, feed the results back to the intelligent control system, and continuously optimize the control strategy.

[0008] Optionally, the establishment of a big data analysis platform to collect and analyze key parameters in the stamping process, such as pressure distribution, mold temperature, and material flow, automatically adjusts the stamping parameters through a machine learning algorithm, and realizes intelligent optimization of the manufacturing process, includes the following steps: Establish a big data platform to collect multi-dimensional data during the stamping process, including pressure, speed and temperature; Using reinforcement learning algorithms, stamping parameters are automatically adjusted to achieve self-optimization of the manufacturing process; Through the AI ​​monitoring system, the manufacturing process is monitored in real time, and abnormal situations are automatically identified and handled; Verify the effect of the optimized manufacturing process and continuously iterate the optimization strategy to ensure the stability and efficiency of the manufacturing process.

[0009] Optionally, the integrated online detection device monitors the size and shape and position tolerance of the core in real time, and once a deviation is found, immediately triggers the adaptive adjustment mechanism to automatically fine-tune the mold parameters, including the following steps: Adopt deep learning technology to realize online intelligent visual inspection of iron cores and automatically identify size and shape tolerances; Develop an adaptive adjustment algorithm to automatically fine-tune mold parameters based on test results to ensure core accuracy; Establish a closed-loop quality control system to feed back test results to the production system and continuously optimize the manufacturing process; Through the AI ​​decision support system, intelligent suggestions are provided in the manufacturing process to improve the intelligence level of quality control.

[0010] Optionally, the mold health monitoring system is established to monitor the mold status in real time through the Internet of Things technology, predict maintenance needs, and reduce unplanned downtime, including the following steps: Use IoT technology to monitor mold status in real time, including wear and temperature changes; Through AI prediction algorithms, intelligent prediction of mold maintenance needs can be made to reduce unplanned downtime; Based on the feedback of production data, the mold design and stamping process are automatically adjusted to form an adaptive improvement mechanism; Implement smart maintenance plans, including automated cleaning, lubrication, and replacement of worn parts, to continuously optimize your mold maintenance strategy.

[0011] In a second aspect, the present application provides a stamping die manufacturing control system for a new energy motor core, comprising: Smart materials and pretreatment module, used for material performance analysis, smart material formulation design, material performance verification and optimization, and smart material pretreatment; Digital twin and intelligent design module, used for digital twin model construction, intelligent simulation and optimization, virtual reality verification, and mold design iteration and verification of molds; Adaptive control and optimization module, used for intelligent step control, AI-driven self-optimization of manufacturing processes, intelligent monitoring of manufacturing processes, and verification and iteration of optimization effects; Online intelligent quality control module for online intelligent visual inspection, adaptive adjustment algorithm, quality control closed-loop system, and intelligent decision support; The intelligent maintenance and improvement module is used for intelligent maintenance and continuous improvement of molds through intelligent monitoring of mold status, predictive maintenance strategies, adaptive improvement mechanisms, and intelligent maintenance execution and optimization.

[0012] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the stamping die manufacturing control method for the new energy motor core as described in any one of the above items are implemented.

[0013] In a fourth aspect, the present application provides a readable storage medium, which adopts the following technical solution: A readable storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the stamping die manufacturing control method for the new energy motor core as described in any one of the above items are implemented.

[0014] In summary, this application includes the following beneficial technical effects: 1. Through intelligent material genome engineering, digital twin mold design and intelligent simulation, adaptive intelligent step control and other steps, high-precision control and efficient production are achieved to ensure that the core size and shape and position tolerances meet the standards and improve production efficiency; 2. AI-driven self-optimization of manufacturing processes, online intelligent quality inspection and adaptive adjustment, intelligent maintenance and adaptive improvement modules enable the manufacturing process to automatically adjust according to real-time data and environmental changes, reducing human intervention and improving production stability and flexibility; 3. Through continuous data collection, analysis and feedback, as well as AI-based decision support, this control method promotes continuous optimization and innovation of the manufacturing process, ensures continuous improvement of core manufacturing quality, and provides a solid foundation for performance improvement and cost reduction of new energy motor cores. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a stamping die manufacturing control method for a new energy motor core in an embodiment of the present application.

[0016] Figure 2 It is a sub-step flow chart of step S1 in the embodiment of the present application.

[0017] Figure 3 It is a sub-step flow chart of step S2 in the embodiment of the present application.

[0018] Figure 4 It is a sub-step flow chart of step S3 in the embodiment of the present application.

[0019] Figure 5 It is a sub-step flow chart of step S4 in the embodiment of the present application.

[0020] Figure 6 It is a sub-step flow chart of step S5 in the embodiment of the present application.

[0021] Figure 7 It is a sub-step flow chart of step S6 in the embodiment of the present application.

[0022] Figure 8 It is a principle block diagram of a stamping die manufacturing control system for new energy motor cores in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1-Figure 8 This application is described in further detail.

[0024] Reference Figure 1 The present application embodiment discloses a stamping die manufacturing control method for a new energy motor core, comprising the following steps: S1: Use artificial intelligence algorithms to analyze the correlation between material properties and stamping effects, and intelligently recommend the best material combination.

[0025] Reference Figure 2 , step S1 correspondingly includes the following sub-steps.

[0026] S11: Establish a material properties database, including the material's microstructure, physical properties, and stamping behavior.

[0027] S12: Use machine learning to predict the properties of new materials and intelligently design customized material formulations.

[0028] S13: Verify new material performance through experiments, iteratively optimize material formulations, and ensure initial material quality during the stamping process.

[0029] S14: Adopt the automated pretreatment system to intelligently adjust the pretreatment parameters according to the material characteristics to improve the stamping adaptability of the material.

[0030] For steps S11-S14, the embodiment of the present application provides the following examples.

[0031] First, build a comprehensive material properties database covering the microstructure, physical properties and stamping behavior of various materials. For example, for steel used in progressive stamping dies, the database should include key performance indicators such as hardness, toughness, ductility, and behavior under different stamping conditions.

[0032] Furthermore, based on the material properties database, machine learning algorithms are used to predict the properties of new materials and intelligently design customized material formulas. For example, by analyzing the effects of different alloy compositions on stamping performance in the database, a new alloy is predicted and designed to improve the wear resistance and fatigue resistance of progressive stamping dies.

[0033] In addition, the performance of new materials is verified through experiments, and the material formula is iteratively optimized based on the experimental results to ensure the initial material quality of the stamping process. For example, stamping experiments are carried out on new alloys with predictive design to evaluate their actual performance in progressive stamping dies, and the alloy composition is adjusted according to the experimental data until the optimal performance is achieved.

[0034] Finally, an automated pretreatment system is used to intelligently adjust pretreatment parameters according to material characteristics to improve the stamping adaptability of the material. For example, for materials used in progressive stamping dies, the system automatically adjusts the heating temperature and time according to the hardness and thickness of the material to ensure that the material reaches the best state before stamping, thereby improving stamping accuracy and die life.

[0035] Replay Figure 1 , after step S1, the following steps are also included.

[0036] S2: Use digital design tools and virtual simulation technology to predict the behavior of the mold during the actual stamping process and optimize the mold design.

[0037] Reference Figure 3 , step S2 correspondingly includes the following sub-steps.

[0038] S21: Create a digital twin model of the mold to achieve real-time synchronization between mold design and actual manufacturing process.

[0039] S22: Use deep learning algorithms to perform high-precision simulation of the mold behavior during the stamping process to predict and optimize mold design.

[0040] S23: Use virtual reality technology to immerse yourself in the mold design and ensure the innovation and functionality of the design.

[0041] S24: Based on the simulation and verification results, multiple rounds of design iterations are performed to ensure the optimization of the mold design.

[0042] For steps S21-S24, the embodiment of the present application provides the following examples.

[0043] First, a digital twin model is created for the progressive stamping die, which not only contains the geometric structure of the die, but also simulates its physical behavior during the stamping process. For example, through the digital twin model, the stress distribution, temperature change and wear of the die during the continuous stamping process can be monitored in real time, realizing real-time synchronization between the design and the actual manufacturing process.

[0044] Furthermore, based on the digital twin model, a deep learning algorithm is used to simulate the behavior of the mold during the stamping process with high precision. For example, by training a deep neural network, the impact of different design parameters on the mold life and the quality of stamped parts can be predicted, thereby optimizing the mold design and improving stamping efficiency and product consistency.

[0045] In addition, virtual reality technology is used to immersively verify mold design to ensure the innovation and functionality of the design. For example, designers can wear VR helmets, enter the virtual environment, visually check every detail of the mold, and evaluate its dynamic behavior during the stamping process to ensure that the design meets functional requirements and is innovative.

[0046] Finally, multiple rounds of design iterations are performed based on the simulation results of the digital twin model and the verification feedback of virtual reality technology to ensure the optimization of the mold design. For example, by analyzing the simulation data, it is found that a certain area of ​​the mold has stress concentration during the stamping process, which may lead to early failure. The designer can immediately adjust the design in the virtual environment, optimize the structure of the area, and then simulate and verify again until the optimal design is achieved.

[0047] Replay Figure 1 , after step S2, the following steps are also included.

[0048] S3: Develop an intelligent control system to monitor the step distance during the stamping process in real time, dynamically adjust the mold position based on real-time data, and ensure precise alignment between consecutive workstation processes.

[0049] Reference Figure 4 , step S3 correspondingly includes the following sub-steps.

[0050] S31: Deploy a high-precision sensor network on the die to monitor the step changes during the stamping process in real time.

[0051] S32: Use machine learning to predict step deviation and adjust mold position in advance to achieve adaptive control.

[0052] S33: Develop a dynamic step adjustment algorithm to automatically optimize mold position based on real-time data to ensure precise alignment between consecutive workstation processes.

[0053] S34: Evaluate the step control effect, feed back the results to the intelligent control system, and continuously optimize the control strategy.

[0054] For steps S31-S34, the embodiment of the present application provides the following examples.

[0055] First, a high-precision sensor network is deployed on the mold to monitor the step changes during the stamping process in real time. For example, the sensor can accurately measure the position changes of the mold during continuous stamping, including the tiny displacement and rotation of the mold, ensuring high accuracy and real-time data.

[0056] Secondly, based on the real-time data collected by the sensor network, a machine learning algorithm is used to predict the step deviation. For example, by training the prediction model, it is possible to identify small deviations in the die position in advance and predict their impact on the quality of the stamped parts, so that measures can be taken before the deviation occurs to achieve adaptive control.

[0057] In addition, based on real-time data, a dynamic step adjustment algorithm is developed to automatically optimize the mold position and ensure accurate alignment between consecutive workstation processes. For example, the algorithm will dynamically adjust the mold feed speed and position based on the step change feedback from the sensor to ensure that the stamping parts at each workstation can be accurately aligned, improving stamping accuracy and production efficiency.

[0058] Finally, the step control effect is evaluated and the results are fed back to the intelligent control system to continuously optimize the control strategy. For example, by analyzing the long-term data of step control, potential problems in the control strategy can be identified, such as the impact of mold wear on step control, and then the algorithm parameters can be adjusted to optimize the dynamic adjustment strategy of the mold to ensure long-term high-precision control.

[0059] Replay Figure 1 , after step S3, the following steps are also included.

[0060] S4: Establish a big data analysis platform to collect and analyze key parameters in the stamping process, such as pressure distribution, mold temperature, and material flow, and automatically adjust stamping parameters through machine learning algorithms to achieve intelligent optimization of the manufacturing process.

[0061] Reference Figure 5 , step S4 correspondingly includes the following steps.

[0062] S41: Establish a big data platform to collect multi-dimensional data during the stamping process, including pressure, speed and temperature.

[0063] S42: Using reinforcement learning algorithms, stamping parameters are automatically adjusted to achieve self-optimization of the manufacturing process.

[0064] S43: Through the AI ​​monitoring system, the manufacturing process is monitored in real time, and abnormal situations are automatically identified and handled.

[0065] S44: Verify the effect of the optimized manufacturing process and continuously iterate the optimization strategy to ensure the stability and efficiency of the manufacturing process.

[0066] For steps S41-S44, the embodiment of the present application provides the following examples.

[0067] First, a big data platform is established to collect multi-dimensional data during the stamping process, including key parameters such as pressure, speed, and temperature. For example, by installing high-precision sensors on stamping equipment, the pressure distribution, stamping speed, and mold temperature during the stamping process can be monitored in real time. These data will be transmitted to the big data platform in real time, providing a basis for subsequent analysis and optimization.

[0068] Secondly, based on the real-time data collected by the big data platform, the reinforcement learning algorithm is used to automatically adjust the stamping parameters to achieve self-optimization of the manufacturing process. For example, by training the reinforcement learning model, the algorithm can learn how to adjust the stamping speed and pressure under different working conditions to achieve the best stamping effect while reducing energy consumption and equipment wear. The reinforcement learning algorithm interacts with the environment and continuously tries different parameter combinations to obtain the optimal stamping parameter settings.

[0069] Furthermore, AI monitoring systems can be used to monitor the manufacturing process in real time and automatically identify and handle abnormal situations. For example, AI monitoring systems can analyze sensor data in real time and immediately trigger warnings once abnormal conditions such as abnormal pressure, excessive temperature or speed fluctuations are detected, and automatically take measures such as adjusting parameters, suspending the stamping process or starting the cooling system to prevent equipment damage or product quality problems.

[0070] Finally, verify the effect of the optimized manufacturing process, and continuously iterate the optimization strategy to ensure the stability and efficiency of the manufacturing process. For example, by comparing the quality, production efficiency, and energy consumption of stamping parts before and after optimization, evaluate the optimization effect of the reinforcement learning algorithm. Based on the evaluation results, continuously adjust the parameters of the reinforcement learning model, optimize the automatic adjustment strategy of stamping parameters, ensure the stability and efficiency of the progressive stamping die manufacturing process, and improve product quality and production efficiency.

[0071] Replay Figure 1 , after step S4, the following steps are also included.

[0072] S5: Integrated online detection equipment monitors the size and shape tolerance of the core in real time. Once a deviation is found, the adaptive adjustment mechanism is immediately triggered to automatically fine-tune the mold parameters.

[0073] Reference Figure 6 , step S5 correspondingly includes the following sub-steps.

[0074] S51: Uses deep learning technology to achieve online intelligent visual inspection of iron cores and automatically identify size and shape tolerances.

[0075] S52: Develop an adaptive adjustment algorithm to automatically fine-tune mold parameters based on test results to ensure core accuracy.

[0076] S53: Establish a closed-loop quality control system, feed back test results to the production system, and continuously optimize the manufacturing process.

[0077] S54: Through the AI ​​decision support system, intelligent suggestions are provided during the manufacturing process to improve the intelligence level of quality control.

[0078] For steps S51-S54, the embodiment of the present application provides the following examples.

[0079] First, a deep learning visual inspection system was deployed on its progressive stamping die production line. The system can capture images of the iron core in real time and automatically identify the size and form tolerance of the iron core, such as outer diameter, inner diameter, thickness, and concentricity, through a deep learning algorithm. The application of this technology effectively avoids errors in manual inspection and improves the accuracy and efficiency of inspection.

[0080] Secondly, based on the results of visual inspection, the system's built-in adaptive adjustment algorithm can automatically analyze the dimensional deviation of the core and automatically fine-tune the mold parameters, such as punching force, punching speed, and mold gap, according to the preset tolerance range to ensure that the core's accuracy meets the design requirements. This process does not require human intervention, greatly improving the automation level of the production process.

[0081] Then, by establishing a closed-loop quality control system, the test results are fed back to the production control system in real time. The system can automatically adjust production parameters such as mold position, stamping speed and cooling time according to the feedback information to continuously optimize the manufacturing process and ensure that the size and shape and position tolerances of the core are always in the best state. This closed-loop control mechanism effectively improves the stability of the production process and the consistency of product quality.

[0082] Finally, during the production process, the AI ​​decision support system can provide intelligent suggestions in the manufacturing process based on real-time production data and historical records, such as predicting equipment failures, optimizing production plans, and adjusting process parameters, helping production managers make more informed decisions and improving the intelligence level of quality control. For example, the system can predict mold wear and arrange maintenance in advance to avoid production interruptions caused by mold damage.

[0083] Replay Figure 1 , after step S5, the following steps are also included.

[0084] S6: Establish a mold health monitoring system to monitor mold status in real time through IoT technology, predict maintenance needs, and reduce unplanned downtime.

[0085] Reference Figure 7 , step S6 correspondingly includes the following steps.

[0086] S61: Use IoT technology to monitor mold status in real time, including wear and temperature changes.

[0087] S62: Through AI prediction algorithms, intelligent prediction of mold maintenance needs can be made to reduce unplanned downtime.

[0088] S63: Based on the feedback of production data, the mold design and stamping process are automatically adjusted to form an adaptive improvement mechanism.

[0089] S64: Implement smart maintenance plans, including automated cleaning, lubrication, and replacement of worn parts, to continuously optimize mold maintenance strategies.

[0090] For steps S61-S64, the embodiment of the present application provides the following examples.

[0091] First, IoT sensors are deployed on its progressive stamping die production line to monitor the wear and temperature changes of the dies in real time. By installing temperature sensors and wear monitoring devices at key parts of the dies, the system can collect data in real time, such as die surface temperature, die gap changes, and die wear. The application of this technology enables production managers to understand the health status of the dies in real time and detect potential failure risks in a timely manner.

[0092] Furthermore, based on the real-time data collected by the Internet of Things, the system's built-in AI prediction algorithm can intelligently analyze the mold's wear trend and temperature changes and predict the mold's maintenance needs. For example, when the system detects that the mold temperature rises abnormally or the degree of wear exceeds the preset threshold, it will automatically trigger an early warning to remind production managers to perform corresponding maintenance operations, such as mold cooling, mold gap adjustment, or mold replacement. This intelligent prediction mechanism effectively reduces unplanned downtime and improves the continuity and stability of the production process.

[0093] In addition, by feeding production data back into the mold design and stamping process adjustments, the system can automatically optimize mold parameters and stamping processes to form an adaptive improvement mechanism. For example, when the system detects dimensional deviations of stamped parts caused by mold wear, it will automatically adjust the mold gap and punching force to compensate for the impact of mold wear. In addition, the system can also automatically adjust the mold design based on the analysis results of production data, such as optimizing mold materials, improving mold structure, and enhancing mold cooling systems, to improve mold durability and production efficiency.

[0094] Finally, based on IoT technology and AI prediction algorithms, the system can implement intelligent maintenance plans, including automated cleaning, lubrication, and replacement of worn parts, to continuously optimize mold maintenance strategies. For example, the system can automatically schedule cleaning and lubrication operations based on the usage and wear of the mold to reduce mold wear and failure risks. In addition, the system can also intelligently predict the need for mold replacement and arrange mold replacement plans in advance to avoid production interruptions caused by mold damage. The implementation of this intelligent maintenance plan not only improves the maintenance efficiency of the mold, but also significantly extends the service life of the mold and reduces maintenance costs.

[0095] Reference Figure 8 This embodiment also provides a stamping die manufacturing control system for new energy motor cores, which is used to execute the above-mentioned stamping die manufacturing control method for new energy motor cores, including the following modules.

[0096] Smart materials and pretreatment module, used for material performance analysis, smart material formulation design, material performance verification and optimization, and smart material pretreatment; Digital twin and intelligent design module, used for digital twin model construction, intelligent simulation and optimization, virtual reality verification, and mold design iteration and verification of molds; Adaptive control and optimization module, used for intelligent step control, AI-driven self-optimization of manufacturing processes, intelligent monitoring of manufacturing processes, and verification and iteration of optimization effects; Online intelligent quality control module for online intelligent visual inspection, adaptive adjustment algorithm, quality control closed-loop system, and intelligent decision support; The intelligent maintenance and improvement module is used for intelligent maintenance and continuous improvement of molds through intelligent monitoring of mold status, predictive maintenance strategies, adaptive improvement mechanisms, and intelligent maintenance execution and optimization.

[0097] In summary, in the intelligent upgrade of progressive stamping die manufacturing, a close connection is formed between the modules, and a highly integrated intelligent manufacturing system is jointly constructed. As the basis, the intelligent material and pretreatment module provides performance-optimized material formulas and pretreatment solutions for the entire manufacturing process, ensuring the high quality and consistency of the materials. The digital twin and intelligent design module builds a digital twin model of the mold based on material performance data, and realizes the iteration and verification of the mold design through intelligent simulation and optimization to ensure the accuracy and reliability of the design. The adaptive control and optimization module plays a core role in the manufacturing process. Through intelligent step control and AI-driven self-optimization, it realizes intelligent monitoring of the manufacturing process and verification and iteration of the optimization effect to ensure production efficiency and product quality. The online intelligent quality control module monitors and adjusts the production process in real time through online intelligent visual inspection, adaptive adjustment algorithm and quality control closed-loop system to ensure the stability and consistency of product quality. The intelligent maintenance and improvement module continuously optimizes the mold maintenance strategy, extends the mold life and reduces maintenance costs through intelligent monitoring of mold status, predictive maintenance strategy and adaptive improvement mechanism. These modules collaborate with each other to form a closed-loop intelligent manufacturing ecosystem, jointly promoting the development of progressive stamping die manufacturing towards a more efficient, smarter and more sustainable direction.

[0098] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a stamping die manufacturing control method for a new energy motor core is implemented: S1: Use artificial intelligence algorithms to analyze the correlation between material properties and stamping effects, and intelligently recommend the best material combination.

[0099] S2: Use digital design tools and virtual simulation technology to predict the behavior of the mold during the actual stamping process and optimize the mold design.

[0100] S3: Develop an intelligent control system to monitor the step distance during the stamping process in real time, dynamically adjust the mold position based on real-time data, and ensure precise alignment between consecutive workstation processes.

[0101] S4: Establish a big data analysis platform to collect and analyze key parameters in the stamping process, such as pressure distribution, mold temperature, and material flow, and automatically adjust stamping parameters through machine learning algorithms to achieve intelligent optimization of the manufacturing process.

[0102] S5: Integrated online detection equipment monitors the size and shape tolerance of the core in real time. Once a deviation is found, the adaptive adjustment mechanism is immediately triggered to automatically fine-tune the mold parameters.

[0103] S6: Establish a mold health monitoring system to monitor mold status in real time through IoT technology, predict maintenance needs, and reduce unplanned downtime.

[0104] When the computer program is executed by a processor, it can implement any of the stamping die manufacturing control methods for new energy motor cores in the above method embodiments.

[0105] This embodiment further provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Use artificial intelligence algorithms to analyze the correlation between material properties and stamping effects, and intelligently recommend the best material combination.

[0106] S2: Use digital design tools and virtual simulation technology to predict the behavior of the mold during the actual stamping process and optimize the mold design.

[0107] S3: Develop an intelligent control system to monitor the step distance during the stamping process in real time, dynamically adjust the mold position based on real-time data, and ensure precise alignment between consecutive workstation processes.

[0108] S4: Establish a big data analysis platform to collect and analyze key parameters in the stamping process, such as pressure distribution, mold temperature, and material flow, and automatically adjust stamping parameters through machine learning algorithms to achieve intelligent optimization of the manufacturing process.

[0109] S5: Integrated online detection equipment monitors the size and shape tolerance of the core in real time. Once a deviation is found, the adaptive adjustment mechanism is immediately triggered to automatically fine-tune the mold parameters.

[0110] S6: Establish a mold health monitoring system to monitor mold status in real time through IoT technology, predict maintenance needs, and reduce unplanned downtime.

[0111] When the computer program is executed by a processor, it can implement any of the stamping die manufacturing control methods for new energy motor cores in the above method embodiments.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0113] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0114] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A stamping die manufacturing control method for a new energy motor core, characterized in that: The following steps are involved: Use artificial intelligence algorithms to analyze the correlation between material properties and stamping effects, and intelligently recommend the best material combination; Use digital design tools and virtual simulation technology to predict the behavior of the mold in the actual stamping process and optimize the mold design; Develop an intelligent control system to monitor the step distance during the stamping process in real time, dynamically adjust the mold position based on real-time data, and ensure precise alignment between consecutive workstation processes; Establish a big data analysis platform to collect and analyze key parameters in the stamping process, such as pressure distribution, die temperature, and material flow, and automatically adjust stamping parameters through machine learning algorithms to achieve intelligent optimization of the manufacturing process; Integrated online detection equipment monitors the size and shape tolerance of the core in real time. Once a deviation is found, the adaptive adjustment mechanism is immediately triggered to automatically fine-tune the mold parameters. Establish a mold health monitoring system to monitor mold status in real time through IoT technology, predict maintenance needs, and reduce unplanned downtime.

2. The method for manufacturing and controlling a stamping die for a new energy motor core according to claim 1, characterized in that: The method of using an artificial intelligence algorithm to analyze the correlation between material properties and stamping effects and intelligently recommending the best material combination includes the following steps: Establish a material properties database, including the material's microstructure, physical properties and stamping behavior; Use machine learning to predict the properties of new materials and intelligently design customized material formulations; Verify new material performance through experiments, iteratively optimize material formulations, and ensure initial material quality during stamping; An automated pretreatment system is used to intelligently adjust pretreatment parameters according to material characteristics to improve the stamping adaptability of the material.

3. The method for manufacturing and controlling a stamping die for a new energy motor core according to claim 1, characterized in that: The method of using digital design tools to predict the behavior of the mold in the actual stamping process through virtual simulation technology and optimizing the mold design includes the following steps: Create a digital twin model of the mold to achieve real-time synchronization between mold design and actual manufacturing process; Use deep learning algorithms to simulate the behavior of the mold during the stamping process with high precision, predict and optimize the mold design; Through virtual reality technology, the mold design is immersively verified to ensure the innovation and functionality of the design; Based on the simulation and verification results, multiple rounds of design iterations are performed to ensure the optimization of the mold design.

4. The method for manufacturing and controlling a stamping die for a new energy motor core according to claim 1, characterized in that: The development of an intelligent control system to monitor the step distance during the stamping process in real time and dynamically adjust the mold position according to the real-time data to ensure accurate alignment between consecutive workstation processes includes the following steps: Deploy a high-precision sensor network on the die to monitor the step changes during the stamping process in real time; Use machine learning to predict step deviation, adjust mold position in advance, and achieve adaptive control; Develop a dynamic step adjustment algorithm to automatically optimize the mold position based on real-time data to ensure precise alignment between consecutive workstations; Evaluate the step control effect, feed the results back to the intelligent control system, and continuously optimize the control strategy.

5. The method for manufacturing and controlling a stamping die for a new energy motor core according to claim 1, characterized in that: The big data analysis platform is established to collect and analyze key parameters in the stamping process, such as pressure distribution, mold temperature, and material flow, and the stamping parameters are automatically adjusted through machine learning algorithms to achieve intelligent optimization of the manufacturing process, including the following steps: Establish a big data platform to collect multi-dimensional data during the stamping process, including pressure, speed and temperature; Using reinforcement learning algorithms, stamping parameters are automatically adjusted to achieve self-optimization of the manufacturing process; Through the AI ​​monitoring system, the manufacturing process is monitored in real time, and abnormal situations are automatically identified and handled; Verify the effect of the optimized manufacturing process and continuously iterate the optimization strategy to ensure the stability and efficiency of the manufacturing process.

6. The method for manufacturing and controlling a stamping die for a new energy motor core according to claim 1, characterized in that: The integrated online detection equipment monitors the size and shape and position tolerance of the core in real time. Once a deviation is found, the adaptive adjustment mechanism is immediately triggered to automatically fine-tune the mold parameters, including the following steps: Adopt deep learning technology to realize online intelligent visual inspection of iron cores and automatically identify size and shape tolerances; Develop an adaptive adjustment algorithm to automatically fine-tune mold parameters based on test results to ensure core accuracy; Establish a closed-loop quality control system to feed back test results to the production system and continuously optimize the manufacturing process; Through the AI ​​decision support system, intelligent suggestions are provided in the manufacturing process to improve the intelligence level of quality control.

7. The method for manufacturing and controlling a stamping die for a new energy motor core according to claim 1, characterized in that: The establishment of a mold health monitoring system to monitor the mold status in real time through the Internet of Things technology, predict maintenance needs, and reduce unplanned downtime includes the following steps: Use IoT technology to monitor mold status in real time, including wear and temperature changes; Through AI prediction algorithms, intelligent prediction of mold maintenance needs can be made to reduce unplanned downtime; Based on the feedback of production data, the mold design and stamping process are automatically adjusted to form an adaptive improvement mechanism; Implement smart maintenance plans, including automated cleaning, lubrication, and replacement of worn parts, to continuously optimize your mold maintenance strategy.

8. A stamping die manufacturing control system for a new energy motor core, used to execute a stamping die manufacturing control method for a new energy motor core as claimed in any one of claims 1 to 7, characterized in that: include: Smart materials and pretreatment module, used for material performance analysis, smart material formulation design, material performance verification and optimization, and smart material pretreatment; Digital twin and intelligent design module, used for digital twin model construction, intelligent simulation and optimization, virtual reality verification, and mold design iteration and verification of molds; Adaptive control and optimization module, used for intelligent step control, AI-driven self-optimization of manufacturing processes, intelligent monitoring of manufacturing processes, and verification and iteration of optimization effects; Online intelligent quality control module for online intelligent visual inspection, adaptive adjustment algorithm, quality control closed-loop system, and intelligent decision support; The intelligent maintenance and improvement module is used for intelligent maintenance and continuous improvement of molds through intelligent monitoring of mold status, predictive maintenance strategies, adaptive improvement mechanisms, and intelligent maintenance execution and optimization.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the stamping die manufacturing control method for the new energy motor core as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the stamping die manufacturing control method for the new energy motor core are implemented as described in any one of claims 1-7.

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