Design method of automobile covering part mold

Abstract

The invention discloses a design method of an automobile panel mold, which relates to the technical field of mold design, and comprises the following steps: applying a load condition and a boundary condition to a three-dimensional model of the mold to obtain a working condition data report, optimizing the three-dimensional model of the mold based on the working condition data report, creating a digital twin model at the same time, and building a virtual production environment. The construction of the virtual production environment enables an engineer to carry out various simulation and debugging operations under the condition of not interfering actual production, discover potential problems in advance and carry out optimization. And more importantly, the real-time monitoring and remote maintenance functions are used, so that the running state of the production line can be mastered at any time, the forming parameters of the mold can be adjusted in time, and the stability and high efficiency of the production process are ensured. Intelligent management of the whole life cycle of the mold is achieved through the digital twinning technology, and the production efficiency and the product quality are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of mold design, in particular to a design method for an automobile covering part mold. Background Art

[0002] As an important part of the appearance of automobiles, the manufacturing quality of automobile panels directly affects the appearance and performance of the whole vehicle. The traditional panel mold design method mainly relies on engineers to design based on manual calculations, and then gradually optimize the mold structure and process parameters through trial molds and debugging. With the rapid development of the automobile industry, traditional design methods have gradually exposed problems such as low efficiency, high cost, and difficulty in meeting complex geometric shapes and high precision requirements.

[0003] The application depth of existing digital twin technology is insufficient, making it difficult to achieve intelligent design of the entire process. Most applications are limited to virtual simulation and fault diagnosis, and have not truly achieved full-process intelligence from design to production. In addition, when applying load conditions and boundary conditions, simplified assumptions are often relied on, which cannot truly reflect the complex stress conditions of the mold in actual production, resulting in a large deviation between the simulation results and the actual situation. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a design method for an automobile cover mold, which solves the problem that the application depth of digital twins in the design of automobile cover molds is insufficient and it is difficult to realize intelligent design of the entire process.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for designing a mold for an automobile panel, which comprises creating a three-dimensional model of the mold; Apply load conditions and boundary conditions to the three-dimensional model of the mold to obtain a working condition data report; Optimize the mold 3D model based on the working condition data report; Based on the optimized 3D mold model, create a digital twin model and build a virtual production environment; Debug the mold in the virtual production environment and obtain simulation results; Monitor and remotely maintain according to simulation results to obtain real-time production data and monitoring results; Adjust the molding parameters of the mold based on real-time data and monitoring results, and generate a final design report.

[0007] As a preferred solution of the design method of the automobile cover mold of the present invention, the three-dimensional model of the mold is created, including the following steps: Select weight reduction, stiffness improvement and manufacturing cost reduction as the main design objectives of the mold; Set constraints based on the main design goals of the mold; The constraints include minimum thickness, maximum stress, surface finish, fatigue life, and thermal expansion coefficient of the mold; Select Siemens MindSpher as the digital twin platform according to the set constraints and generate a parameter report; The parameter report includes the main design objectives and constraints of the mold; Based on the parameter report, CATIAV5 was selected as the 3D modeling tool; Create the main structural components of the mold according to the specific shape and size of the cover; The main structural components of the mold include an upper mold, a lower mold, a limit block and a guide column; After creation, export the 3D model of the mold.

[0008] As a preferred solution of the design method of the automobile cover mold of the present invention, wherein: applying load conditions and boundary conditions to the three-dimensional model of the mold to obtain a working condition data report includes the following steps: Select ANSYS Workbench as the simulation tool and COMSOL Multiphysics as the auxiliary simulation platform; Input the 3D model of the mold into ANSYS Workbench; According to the specific properties of the mold material, the load conditions and boundary conditions of the mold are applied and analyzed to generate a preliminary working condition data report; The load conditions of the mold include punching force, supporting force, friction force and temperature field; The boundary conditions of the mold include a fixed end, a free end and a sliding support; According to the preliminary working condition data report and the specific working environment of the mold, define the simulation conditions of multi-physics coupling in COMSOL Multiphysics and perform multi-physics coupling simulation to generate a multi-physics coupling analysis report; Integrate the preliminary operating data report and multi-physics coupling analysis report to form the final operating data report; The final working condition data report includes a punching force distribution diagram, a supporting force distribution diagram, a friction force distribution diagram, a temperature field distribution diagram, and the results of multi-physical field coupling analysis.

[0009] As a preferred solution of the design method of the automobile cover mold of the present invention, the three-dimensional model of the mold is optimized based on the working condition data, including the following steps: Altair OptiStruct was chosen as the topology optimization tool; Input the final working condition data report into Altair OptiStruct; Based on the main structural components of the mold, set the mesh refinement parameters and adjust the material distribution of the mold to generate the optimized structure of the mold; Perform additive manufacturing compatibility check on the optimized structure of the mold and obtain the inspection results; The optimized structure of the mold is modified according to the inspection results to obtain an optimized three-dimensional model of the mold.

[0010] As a preferred solution of the design method of the automobile cover mold of the present invention, wherein: based on the optimized mold three-dimensional model, a digital twin model is created and a virtual production environment is built, including the following steps: The optimized mold 3D model is input into Siemens MindSphere for adjustment to create a digital twin model; Use digital twin models to build a virtual production environment; Build production equipment and set up production processes in a virtual production environment; The production equipment includes a punching machine, a conveyor belt, a robot and a testing equipment; The production process includes stamping process, conveyor belt transportation, robot operation and testing process.

[0011] As a preferred solution of the design method of the automobile cover mold of the present invention, the mold is debugged in a virtual production environment to obtain a simulation result, including the following steps: Introduce simulation parameters into the virtual production environment and combine them into simulation vectors; The load conditions and boundary conditions of the mold are combined into a working condition combination matrix and normalized at the same time; The simulation parameters include mold preheating time, cooling efficiency, cover thickness, and lubricant type; Combining the simulation vector and the working condition combination matrix, the influence scores of different working condition combinations on the forming quality of the cover are calculated, and the expression is: ; in, It indicates the influence score of different working condition combinations on the forming quality of the cover. represents the upper limit of simulation time, represents the number of operating condition combinations, Indicates The forming quality index of each working condition combination, Represents the normalized The working condition combination matrix, Represents the simulation vector The weight function of the impact on the operating condition combination matrix, Represents a very small positive number; Set a threshold for the quality of the panel forming, and determine whether the standard for virtual commissioning is met based on the impact of different working conditions on the quality of the panel forming within the threshold. When the standard for virtual debugging is met, virtual simulation is started to perform virtual debugging and obtain simulation results.

[0012] As a preferred solution of the design method of the automobile cover mold of the present invention, wherein: monitoring and remote maintenance are performed according to the simulation results to obtain real-time production data and monitoring results, including the following steps: Combining the simulation results with the actual production needs of the mold, a variety of sensors are equipped on the actual production line and installed on the production equipment to collect real-time production data; The multiple sensors include temperature sensors, force sensors, displacement sensors, vibration sensors and visual sensors; Developed a web-based remote monitoring platform based on real-time production data and the Siemens MindSphere digital twin platform; The remote monitoring platform is used to monitor the operating status of the production line and obtain the monitoring results.

[0013] As a preferred solution of the design method of the automobile cover mold of the present invention, wherein: adjusting the molding parameters of the mold based on real-time data and monitoring results to generate a final design report includes the following steps: Based on real-time data and monitoring results, convolutional neural network is selected as the model architecture to build a prediction model; Use the prediction model to predict the production performance of the mold in the future and dynamically adjust the molding parameters of the mold; The molding parameters of the mold include punching speed parameters, punching force parameters, temperature parameters, friction parameters, displacement parameters and preheating time parameters; Optimize the production process according to the adjusted mold molding parameters and generate the final design report; The final design report includes the mold geometry, selected materials and molding parameters.

[0014] In a second aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for designing a mold for an automobile cover as described in the first aspect of the present invention is implemented.

[0015] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for designing a mold for an automobile cover as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: based on the working condition data report, the three-dimensional model of the mold is optimized and a digital twin model is created to build a virtual production environment. The construction of the virtual production environment allows engineers to perform various simulation and debugging operations without interfering with actual production, discover potential problems in advance and optimize them. More importantly, through real-time monitoring and remote maintenance functions, the operating status of the production line can be grasped at any time, the molding parameters of the mold can be adjusted in time, and the stability and efficiency of the production process can be ensured. Through digital twin technology, intelligent management of the entire life cycle of the mold is realized, which significantly improves production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 is a flow chart of the design method of the automobile cover mold in Example 1; Figure 2 This is a judgment diagram of the virtual commissioning standard in Example 1. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for designing a mold for an automobile cover, comprising the following steps: S1. Create a 3D model of the mold.

[0023] The following steps are included: Select weight reduction, stiffness improvement and manufacturing cost reduction as the main design objectives of the mold; By clarifying these design goals, the performance and economy of the mold can be fully considered at the design stage, ensuring that the final designed mold not only meets the forming quality requirements, but also has good economy and manufacturability. This approach avoids the design defects caused by relying on experience in traditional design methods, and improves the scientificity and reliability of the design; Set constraints based on the main design goals of the mold; Constraints include the minimum thickness of the mold (set based on the material's stress conditions and manufacturing process), maximum stress (set based on the material's yield strength), surface finish (need to consider the material of the cover, the forming process, and the subsequent coating process), fatigue life (set based on the material's fatigue strength and actual working conditions), and thermal expansion coefficient (need to consider the mold's operating temperature range); These constraints are set with full consideration of the mechanical properties, surface quality and thermal stability of the mold, ensuring that the mold can work safely and reliably in actual use, effectively avoiding mold failure problems caused by unreasonable design, and improving the reliability and service life of the mold; Select Siemens MindSpher as the digital twin platform according to the set constraints and generate a parameter report; Siemens MindSphere was chosen because it is a cloud-based IoT operating platform that can connect production equipment in the physical world with digital models in the virtual environment in real time, enabling comprehensive monitoring and optimization of the production process. The parameter report includes the main design objectives and constraints of the mold; Based on the parameter report, CATIAV5 was selected as the 3D modeling tool; CATIAV5 is a high-end CAD software widely used in aerospace, automobile manufacturing and other fields, with powerful 3D modeling functions and high-precision geometric analysis capabilities; Create the main structural components of the mold according to the specific shape and size of the cover; The main structural components of the mold include the upper mold, the lower mold, the limit block and the guide column; According to the forming process requirements of the cover, determine the shape and size of the upper and lower dies. For example, if the cover needs to be deep drawn, the shape of the upper die should fully match the outer contour of the cover, and the shape of the lower die should fully match the inner contour of the cover. At the same time, the material flow path and forming depth should be taken into account. Use CATIAV5's "Extrude", "Revolve" or "Sweep" and other modeling tools to create the base of the upper and lower dies (the thickness of the base should be set according to the minimum thickness constraint of the mold. Usually, the thickness of the upper die ranges from 10mm to 30mm, and the thickness of the lower die ranges from 20mm to 50mm. The specific value depends on the size of the cover), and add forming features one by one. For example, for complex curved surface covers, it may be necessary to add multiple drawing ribs, bosses, grooves and other features to ensure that the material can flow smoothly during the forming process and obtain the ideal shape.

[0024] In order to ensure that the cover has good surface quality after forming, the surface finish of the upper and lower molds needs to meet certain standards, and the surface roughness should be controlled below Ra0.8μm. The surface treatment function in CATIAV5 can be used to grind and polish the surfaces of the upper and lower molds to ensure that they meet the design requirements; According to the motion trajectory and stamping stroke of the mold, determine the number and position of the limit blocks. The limit blocks should be installed at the four corners of the mold to ensure that the mold will not deviate or collide during the stamping process. Its geometric shape should be designed according to the structural characteristics of the mold, and the height should be slightly higher than the maximum stroke of the upper and lower molds. The width and thickness should be adjusted according to the force conditions and installation space of the mold to ensure sufficient rigidity and strength; The material of the stop block should have high hardness and wear resistance to prevent wear or damage during long-term use. Common stop block materials include high-hardness steel (SKD11), wear-resistant plastic (nylon), etc. Select the appropriate material according to the actual working conditions, and ensure that its thermal expansion coefficient matches the main material of the mold to avoid dimensional deviation caused by thermal expansion and contraction; In order to ensure that the limit block is installed firmly and reliably, it is usually fixed to the mold by bolt connection. When designing the limit block, sufficient mounting holes or welding areas should be reserved to ensure that it can be tightly combined with the mold body; The guide column is used to ensure that the upper and lower dies are precisely aligned during the stamping process to avoid forming quality problems caused by offset (determined according to the structural characteristics of the die and the movement mode of the stamping machine). It is usually fixed to the die by interference fit. When designing the guide column, the maintainability of the guide column should be considered to facilitate replacement or repair when necessary; After creation, export the 3D model of the mold; According to the specific application requirements, select the appropriate file format (STEP) to export the 3D model of the mold. Before exporting, use the CATIAV5 inspection tool to check the integrity of the 3D model of the mold (continuity of geometric shapes, accuracy of dimensions, and assembly relationships between components).

[0025] S2. Apply load conditions and boundary conditions to the three-dimensional model of the mold to obtain a working condition data report.

[0026] The following steps are included: Select ANSYS Workbench as the simulation tool and COMSOL Multiphysics as the auxiliary simulation platform; The reason for choosing ANSYS Workbench here is that it has a built-in advanced nonlinear solver that can handle complex geometric shapes and nonlinear material properties. Especially for complex structures such as stamping dies that involve large deformation and contact problems, the nonlinear solver can provide more accurate stress and strain distribution. COMSOL Multiphysics is specially used for multi-physics coupling analysis, which can handle the interaction between multiple physical fields and provide detailed multi-physics coupling simulation results. Input the 3D model of the mold into ANSYS Workbench; Open ANSYS Workbench, select Geometry, click the Import Geometry button, select the exported 3D model file and load the model, which will be displayed in the workspace. After importing the model, use the Check Geometry function to check whether the model has geometric defects (such as self-intersection, gap, overlap). If problems are found, use the Repair Geometry function to repair them. At the same time, the model can also be appropriately simplified, for example, removing minor features that do not affect stress distribution (such as threaded holes and chamfers); According to the specific properties of the mold material, the load conditions and boundary conditions of the mold are applied and analyzed to generate a preliminary working condition data report; The loading conditions of the mold include punching force (affecting the stress distribution of the mold), supporting force (used to simulate the supporting effect of the lower mold on the cover to prevent the cover from excessive deformation), friction force (affecting the flow and forming quality of the material) and temperature field (affecting the mechanical properties of the material); The boundary conditions of the mold include fixed ends (limiting the movement and rotation of the mold, located on the base or support structure of the mold, limiting the three translational degrees of freedom and three rotational degrees of freedom of the mold), free ends (simulating the situation that some parts of the mold can move freely during the stamping process, located on the edge or suspended part of the mold, allowing the mold to deform within a certain range) and sliding supports (simulating the situation that some parts of the mold slide in a specific direction during the stamping process, located on the guide column or limit block of the mold, allowing the mold to slide relative to each other in the direction perpendicular to the contact surface); Select the corresponding material model according to the actual material of the mold. ANSYS Workbench provides a rich material library. Engineers can directly select materials from the library, or manually input mechanical performance parameters such as elastic modulus, Poisson's ratio, yield strength, etc. for the material. For nonlinear materials, stress-strain curves can be defined to ensure that the simulation results are closer to the actual situation. After applying the load conditions and boundary conditions, the three-dimensional model of the mold needs to be meshed (to improve the accuracy of the model). For complex geometric shapes and stress concentration areas, local mesh refinement can be performed. In ANSYS Workbench, select Mesh to set the mesh type (such as tetrahedron, hexahedron), size and density. After the settings are completed, click the Solve button to start the simulation calculation. ANSYS Workbench will calculate the physical quantities such as stress, strain, displacement, etc. of the mold under different working conditions based on the applied load conditions and boundary conditions, and generate a preliminary working condition data report; According to the preliminary working condition data report and the specific working environment of the mold, define the simulation conditions of multi-physics coupling in COMSOL Multiphysics and perform multi-physics coupling simulation to generate a multi-physics coupling analysis report; The simulation conditions for multi-physics coupling here include: Thermal-mechanical coupling: that is, considering the impact of temperature changes on mold structure stress. For example, high temperature may cause material expansion, which in turn affects the accuracy and life of the mold. Electromagnetic-mechanical coupling: If the mold involves electromagnetic effects (such as electromagnetic forming process), analyze the influence of electromagnetic field on mold deformation; Fluid-Structure Coupling: If there is fluid involved (such as coolant), analyze the impact of fluid flow on the mold structure (for example, the flow path and speed of the coolant will affect the cooling efficiency and uniformity of the mold); Use COMSOL's adaptive meshing feature to refine the mesh according to the model complexity and the importance of key areas (such as increasing the mesh density in stress concentration areas and places with drastic temperature changes), and select a suitable iterative solver and adjust the solver parameters to improve computational efficiency and accuracy; After the solver parameters are set, start the simulation calculation, view the stress distribution and temperature field of the current mold through the COMSOL graphical interface, and check the convergence of the simulation results. If it does not converge, adjust the grid and solver parameters and rerun until all physical fields reach a stable state; Extract key data (such as electromagnetic field strength, fluid flow path, etc.) from simulation results, organize the extracted data into text descriptions, and generate multi-physics field coupling analysis reports; In this step, the concept of multi-physics coupling is introduced because the analysis of a single physical field cannot fully reflect the performance of the mold in the actual working environment. For example, only considering mechanical stress may ignore the impact of temperature changes on material properties, thus leading to design defects. Through multi-physics coupling analysis, we can better understand the interaction between different physical phenomena, thereby optimizing the design and manufacturing process of the mold. Finally, virtual simulation is used to discover problems in advance, reducing the number of trial and error and the cost of physical tests, and shortening the mold development cycle. Integrate the preliminary operating data report and multi-physics coupling analysis report to form the final operating data report; The final working condition data report includes the punching force distribution diagram, support force distribution diagram, friction force distribution diagram, temperature field distribution diagram and the results of multi-physics field coupling analysis; The final operating data report in this step provides a more comprehensive design basis, covering all information from basic mechanical analysis to complex multi-physics field coupling analysis, helping designers make more scientific and reasonable decisions and ensure the optimality of mold design.

[0027] S3. Optimize the mold 3D model based on the working condition data report.

[0028] The following steps are included: Altair OptiStruct was chosen as the topology optimization tool; Altair OptiStruct was chosen here because it has powerful topology optimization capabilities and can be seamlessly integrated with other simulation tools such as ANSYS Workbench, which can truly reflect the behavior of the mold under actual working conditions; Input the final working condition data report into Altair OptiStruct; Export the load case data report from ANSYS Workbench, open Altair OptiStruct, select LoadCase, click Import LoadCase, select the load case data file and use Altair OptiStruct's Check LoadCase function to check the completeness and accuracy of the load case data. If any problems are found, such as mismatched load conditions or missing boundary conditions, you can make corresponding modifications; Based on the main structural components of the mold, set the mesh refinement parameters and adjust the material distribution of the mold to generate the optimized structure of the mold; In order to improve the optimization efficiency, the mesh can be refined in some key areas, while keeping a coarser mesh in other areas. For example, for stress concentration areas such as the contact area between the upper and lower molds, the installation position of the limit block and the guide column, a smaller mesh size can be set to ensure the simulation accuracy of these areas, while for other parts of the mold, a larger mesh size can be set to reduce the consumption of computing resources; After the mesh refinement parameters are divided; Using Altair OptiStruct’s Topology Optimization function, set the initial conditions for material distribution. Users can select different material models (such as elastic materials, plastic materials, and composite materials) and define mechanical performance parameters such as material density, elastic modulus, and Poisson’s ratio based on the actual material properties of the mold to ensure that the optimized structure meets manufacturing requirements. After completing the material distribution settings, click the RunOptimization button to start the topology optimization calculation. AltairOptiStruct will automatically adjust the material distribution according to the applied load conditions and constraints to generate an optimized mold structure. During the optimization process, users can view the optimization progress and results in real time through the graphical interface; The optimized structure not only meets the design goals (such as reducing weight, improving stiffness, and reducing stress), but also complies with all constraints (such as minimum thickness and maximum stress). In addition, the optimized structure also has good manufacturability, which is convenient for subsequent processing and manufacturing. Perform additive manufacturing compatibility checks on the optimized structure of the mold and obtain inspection results (overhang structure, internal cavity, minimum feature size, and material selection); Import the optimized mold structure into the additive manufacturing software (Netfabb); During the additive manufacturing inspection process, overhanging structures (i.e., free hanging parts without support) are prone to printing failures. Use Netfabb's Overhang Analysis function to check whether there are overhanging structures in the optimized structure. Internal cavities affect printing speed and material usage; Use the InternalStructureAnalysis function to check whether there are internal cavities in the optimized structure; Too small feature size (such as elongated holes and thin-walled structures) can easily lead to reduced printing quality. Use Netfabb's FeatureSizeAnalysis function to check whether there are too small feature sizes in the optimized structure. The choice of materials directly affects the printing quality and cost. According to the requirements of the mechanical properties of the optimized structure, select the appropriate additive manufacturing materials (such as metal powder, plastic particles). Common additive manufacturing materials include aluminum alloy, titanium alloy, stainless steel, nylon, etc. After all checks are completed, an additive manufacturing compatibility check result report is generated; Modify the optimized structure of the mold according to the inspection results to obtain an optimized three-dimensional model of the mold; If the inspection results show that there is a suspended structure, you can add a support structure (such as a triangle support or a grid support) to the optimized structure to reduce the height or angle of the suspended part. Users can choose the appropriate support method based on actual needs. If the inspection results show that there are internal cavities, you can decide whether to keep or fill them according to actual needs. For internal cavities that do not need to be kept, use the FillTool function to fill them with solid structures to improve printing efficiency. For internal cavities that need to be kept, use the LatticeTool function to generate a lightweight lattice structure (saving materials and maintaining structural strength); If the inspection results show that there are too small feature sizes, you can increase their sizes appropriately to ensure the feasibility of the printing process. For complex features such as elongated holes and thin-walled structures, you need to gradually increase their sizes using a gradual transition method to avoid manufacturing difficulties caused by sudden changes. If it is found that the material selection is inappropriate, the mechanical properties and thermal stability of the material can be re-evaluated and a more suitable material can be selected; After all modifications are completed, export the optimized mold 3D model to a standard format such as STL or IGES.

[0029] S4. Based on the optimized mold 3D model, create a digital twin model and build a virtual production environment. This includes the following steps: The optimized mold 3D model is input into Siemens MindSphere for adjustment to create a digital twin model; Open the Siemens MindSphere platform, select Asset Modeling, and click the Import Asset button to import the optimized mold 3D model into Siemens MindSphere; Adjust the thickness of each part of the mold according to actual production requirements. For example, for stress concentration areas (such as the edge where the upper mold contacts the cover), the thickness can be appropriately increased to improve rigidity, while for non-critical areas (such as the supporting part of the lower mold), the thickness can be appropriately reduced to reduce weight. The thickness adjustment should follow the minimum thickness constraint to ensure that the mold will not be excessively deformed or broken when bearing load; Add chamfers or fillets to the sharp edges of the mold to avoid cracks or damage caused by stress concentration. The size of the chamfers and fillets should be reasonably set according to the stress conditions and manufacturing process of the mold to ensure that the forming quality is not affected and the durability of the mold is improved; Adjust the mounting holes and fixing points of the mold according to the requirements of the actual production equipment to ensure that these positions can perfectly match the interfaces of the punching machine, robot and other equipment to avoid failures or deviations caused by improper installation; After completing all adjustments, click the CreateDigitalTwin button to create the digital twin model; Use digital twin models to build a virtual production environment; Build production equipment and set up production processes in a virtual production environment; Production equipment includes punching machines, conveyor belts, robots, and testing equipment; The production process includes stamping process, conveyor belt transportation, robot operation and testing process; Import the floor plan of the actual production workshop (workshop size, equipment location, channels) as the basis for the virtual factory layout; According to the layout of the actual production workshop, the FactoryLayout tool in the digital twin model is used to place production equipment in the virtual environment. Users can place the equipment in the appropriate position and adjust its direction and angle by dragging and dropping. At the same time, the connection method with other equipment is set for each production equipment. For example, a material transmission interface needs to be set between the conveyor belt and the stamping machine, an operation point for grabbing and placing materials needs to be set between the robot and the stamping machine, and a detection point needs to be set between the detection equipment and the conveyor belt. Plan the material transportation path to ensure that the material can flow smoothly in the virtual environment, for example, from the loading area to the punching machine, then to the conveyor belt, and finally to the unloading area. At the same time, in order to simulate the safety measures in actual production, you can add safety areas and protective facilities in the virtual environment, such as setting up safety fences and emergency stop buttons; In order to ensure that the virtual production equipment can operate normally in the virtual environment, detailed equipment parameters must be set for each device, including the die change time of the punching machine, stroke control, conveyor belt transportation speed, carrying capacity, start and stop time, fault detection, robot movement trajectory, gripping force (grasping material strength), detection equipment accuracy, frequency; After completing the equipment configuration and parameter setting, define the specific production process of the virtual production line (loading - stamping - transmission - robot operation - detection) according to actual production needs, and set the production rhythm for the virtual production line to ensure that each process can be completed within the specified time. For example, set the stamping time of the stamping machine to 10 seconds, the transportation time of the conveyor belt to 5 seconds, the robot operation time to 8 seconds, and the detection time to 3 seconds.

[0030] S5. Debug the mold in the virtual production environment and obtain the simulation results.

[0031] The following steps are included: Introduce simulation parameters into the virtual production environment and combine them into simulation vectors; This step ensures comprehensive coverage of different working conditions by introducing simulation vectors, and can more accurately simulate various situations in actual production; The load conditions and boundary conditions of the mold are combined into a working condition combination matrix and normalized at the same time; The normalization process is performed to unify different types of working conditions to the same scale; The normalized working condition combination matrix in this step can be directly used for subsequent scoring calculations, which simplifies complex mathematical operations and improves simulation efficiency; The simulation parameters include mold preheating time (usually between 30 seconds and 5 minutes, depending on the size and material of the mold), cooling efficiency (controlled by coolant flow, temperature and nozzle position), cover thickness (usually between 0.5 mm and 2.0 mm, depending on product design requirements), and lubricant type (such as oil-based lubricant, water-based lubricant, solid lubricant). Combining the simulation vector and the working condition combination matrix, the influence scores of different working condition combinations on the forming quality of the cover are calculated, and the expression is: ; in, It indicates the influence score of different working condition combinations on the forming quality of the cover. It represents the upper limit of simulation time and reflects the influence of working condition combination on the whole production cycle. Indicates the number of working condition combinations, each of which consists of different load conditions (such as punching force, support force, friction force, etc.) and boundary conditions (such as fixed end, free end, sliding support, etc.). Indicates The forming quality index of each working condition combination includes the surface quality, dimensional accuracy, material flow uniformity and other aspects of the cover. The forming quality index of each working condition combination is obtained through simulation, which reflects the forming effect under the working condition combination. Its role is to combine with the influence weight of the working condition combination to quantify the specific contribution of different working condition combinations to the forming quality. Represents the normalized The working condition combination matrix, Represents the simulation vector The weight function of the impact on the operating condition combination matrix, the simulation vector The introduction of makes the formula able to dynamically reflect the changes of different working condition combinations during the simulation process, and the weight function The weights of the impact of these changes on the forming quality are further adjusted to ensure that the scoring results can accurately reflect the dynamic changes in actual production. For example, some working condition combinations may perform well at high temperatures but poorly at low temperatures. The weight function can dynamically adjust its impact weight according to the temperature changes. Represents a very small positive number, usually taking a very small value (such as ten to the negative sixth power), its function is to prevent the denominator from being zero; Set a threshold for the quality of the panel forming (determined according to the quality standards, production costs and efficiency required by customers), and determine whether the standard for virtual commissioning is met based on the impact of different working conditions on the quality of the panel forming within the threshold. When the impact score of different working condition combinations on the forming quality of the cover is greater than or equal to the forming quality threshold, the forming quality under this working condition combination is considered qualified and the virtual debugging stage can be entered; When the impact score of different working condition combinations on the forming quality of the cover is less than the forming quality threshold, the forming quality under this working condition combination is considered unqualified. A genetic algorithm (GA) is used to randomly generate multiple initial working condition combinations by simulating the biological evolution process, and the simulation variables are gradually optimized through selection, crossover, mutation and other operations. After each adjustment, the simulation is re-run to calculate the new forming quality score. , and compare it with the previous score. If the new score is higher than the previous score, keep the current working condition combination; otherwise, continue to adjust the simulation variables until the optimal working condition combination is found; When the standard for virtual debugging is met, virtual simulation is started to perform virtual debugging and obtain simulation results; Load the optimal operating condition combination into the virtual simulation environment, initialize all production equipment in the virtual production line and start the simulation of the virtual production line. The virtual production line will simulate each process in the actual production process according to the set production process and rhythm. Users can view the operating status of the production equipment in real time through the virtual interface, observe the flow of materials to check whether the operation of each process meets the requirements. During the simulation, users can optimize the production parameters according to the actual situation. For example, if it is found that the "transmission" process takes too long, the time of the process can be shortened by adjusting the equipment parameters or optimizing the logistics path.

[0032] S6. Perform monitoring and remote maintenance based on the simulation results to obtain real-time production data and monitoring results. This includes the following steps: Combining the simulation results with the actual production needs of the mold, a variety of sensors are equipped on the actual production line and installed on the production equipment to collect real-time production data; A variety of sensors including temperature sensors, force sensors, displacement sensors, vibration sensors, and visual sensors (to monitor the appearance quality of the cover); According to the simulation results, determine the best installation position for each sensor. For example, the temperature sensor should be installed in the high temperature area of ​​the mold, the force sensor should be installed in the part with greater force, the displacement sensor should be installed in the part prone to deformation, the vibration sensor should be installed in the part prone to vibration, and the visual sensor should be installed at the inspection station; Developed a web-based remote monitoring platform based on real-time production data and the Siemens MindSphere digital twin platform; Integrate the real-time data of production with the Siemens MindSphere digital twin platform (synchronize the virtual environment with the actual production environment), use the API interface to upload the real-time data to the digital twin platform and map it with the parameters in the digital twin model to achieve two-way communication of data. For example, map the data of the temperature sensor to the temperature parameters of the mold, and map the data of the force sensor to the punching force parameters of the stamping machine. When the platform detects a failure in a certain device, it can automatically issue a command to stop the operation of the device. After the integration is complete, a web-based application is developed based on the Siemens MindSphere platform. The development steps include: Design an intuitive and easy-to-use front-end interface so that users can easily view the operating status of the production line. This interface should include: dashboard (displaying real-time production data, such as temperature, force, displacement, vibration), chart (displaying the trend changes of real-time data in the form of a bar chart), equipment status: displaying the operating status of the equipment in a color-coded form (such as normal is green, warning is yellow, and fault is red), alarm list (displaying current alarm information, including alarm time, alarm type, and alarm level), operation panel (providing remote operation functions, such as starting and stopping equipment and adjusting operating parameters) Develop backend logic, process user requests and return data to the frontend interface, query real-time production data from the database and format it into a format that can be parsed by the frontend, receive control instructions issued by users through the Web interface, and forward them to production equipment; In order to facilitate users to monitor the production line anytime and anywhere, the web application should also support mobile access. Mobile applications can be developed through responsive design to ensure that users can use it smoothly on mobile devices such as mobile phones and tablets; Use the remote monitoring platform to monitor the operating status of the production line and obtain the monitoring results; The user logs in to the remote monitoring platform through a browser or mobile application, enters the user name and password for identity authentication, and selects the specific production line or equipment to be monitored (quickly switch through the drop-down menu or navigation bar) for real-time monitoring. During the monitoring process, the user can customize the alarm rules according to actual needs. For example, when the temperature exceeds 80 degrees Celsius, a warning is triggered; when the punching force exceeds 1000 Newtons, a serious alarm is triggered; when the displacement exceeds 5 mm, an emergency alarm is triggered, etc. The alarm rules can be flexibly configured for different devices and parameters. When an alarm is triggered, the alarm information is sent to the user via SMS and email.

[0033] S7. Adjust the molding parameters of the mold based on real-time data and monitoring results and generate a final design report.

[0034] The following steps are included: Based on real-time data and monitoring results, convolutional neural network is selected as the model architecture to build a prediction model; The reason for choosing convolutional neural network here is that one of its core ideas is "local perception", that is, each neuron is only connected to the local area (receptive field) of the input data, rather than to the entire input data. This local connection method enables the convolutional neural network to capture local features in the input data, such as edges, textures, and shapes, and can automatically learn complex patterns in the data without the need for manual feature design; Use the prediction model to predict the production performance of the mold in the future and dynamically adjust the molding parameters of the mold; The molding parameters of the mold include punching speed parameters, punching force parameters, temperature parameters, friction parameters, displacement parameters and preheating time parameters; Data is collected from sensors once a second and processed using a sliding window mechanism. For example, if the data from the past 5 minutes is used to predict the trend of the next 5 minutes, a sliding window containing the most recent 300 time points (assuming the sampling frequency is 1 second) will be formed; Extract useful features from the sliding window (mean: the average value of parameters such as temperature, force, displacement, etc. in the past 5 minutes, standard deviation: the standard deviation of the parameters in the past 5 minutes, rate of change: the difference between adjacent time points, frequency domain features: use fast Fourier transform (FFT) to extract frequency domain features, such as main frequency components), and input them into the prediction model (deployed on the edge computing device of the industrial gateway); After receiving the feature value, the prediction model will perform forward propagation, calculate the activation value of the neuron layer by layer, and finally output the prediction result (AI prediction value). For example, the model may output the predicted value of temperature, force, displacement and other parameters at each time point in the next 5 minutes. For discrete tasks such as fault detection, the model outputs the probability of a fault occurring at a certain time point in the future. For example, the model may output that the probability of a fault occurring in the next 5 minutes is 0.8, indicating that there is an 80% probability of a fault occurring. Based on the prediction results, a built-in rule engine automatically makes control decisions. For example, when the predicted temperature will exceed 80°C, it will decide to reduce the punching force to reduce heat generation. When the predicted punching force will exceed 1000kN, it will decide to reduce the punching speed to avoid mold damage. When the predicted vibration exceeds 0.8, it will decide to suspend the production line for equipment inspection. Based on the control decision, the PLC (Programmable Logic Controller) sends instructions to the production equipment to reduce the stamping speed. For example, a new speed setting command can be sent to the stamping machine via the Modbus protocol to monitor the response of the production equipment in real time to ensure that the stamping speed has been successfully reduced to the target value. If the equipment fails to respond in time, a warning is issued and an attempt is made to resend the instruction; Sending instructions to the cooling center via SCADA (Supervisory Control and Data Acquisition) to increase the cooling time or cooling intensity. For example, when the temperature is predicted to rise to 90°C in the next 5 minutes, it may be decided to increase the flow of cooling water from 10 liters per minute to 15 liters. When the temperature returns to normal, the cooling time or cooling intensity is automatically restored to the original setting. For example, when the temperature drops below 70°C, the cooling time will gradually return from 10 seconds to 5 seconds. Optimize the production process according to the adjusted mold molding parameters and generate the final design report; The final design report includes the mold geometry, selected materials, and molding parameters; During the dynamic adjustment process, the forming parameters after each adjustment are continuously monitored and recorded, and the output of each shift, day, and week is counted to determine the impact of the adjustment on production efficiency. For example, whether the adjusted stamping speed increases the output per unit time, or whether it reduces downtime; Use a surface roughness tester to test the surface quality of the cover to ensure that the adjusted molding parameters will not cause surface scratches, dents, etc. For example, whether the adjusted punching force ensures that the surface finish of the cover meets the standard; The optimization of the production process (excessive punching force) can be explained from the following: The reasons for excessive punching force include unreasonable structural design of the mold (the gap between the concave and convex molds of the mold is too large or too small), the material used is too strong or not ductile enough, insufficient lubrication between the mold and the material, and mold wear, etc. Optimization measures: According to the material thickness and forming requirements, adjust the gap between the concave and convex dies (between 5% and 10% of the material thickness) to ensure that the material can flow smoothly while avoiding excessive resistance; For complex cover parts, the segmented forming process (dividing the entire forming process into multiple steps and completing it step by step) can be considered, which can reduce the force required for each stamping and reduce the load on the mold. For example, preliminary stretching is performed first, followed by fine trimming, and finally final forming. Choose high-strength, wear-resistant mold materials, such as high-speed steel or cemented carbide, to improve the durability and impact resistance of the mold. In addition, the mold surface can be quenched or coated to enhance its wear resistance and anti-adhesion properties; By optimizing the acceleration and deceleration curve of the punching machine, the punching process can be made smoother. For example, the impact force generated during the punching process can be reduced by using a gradual acceleration and deceleration method. Specifically, the machine can be accelerated slowly at the beginning of the punching process and gradually decelerated when approaching the forming point to avoid sudden impact. Installing a buffer device (such as a hydraulic buffer or a pneumatic buffer) on the punching machine can absorb part of the impact energy during the punching process and reduce the peak value of the punching force. The buffer device can be adjusted according to actual needs to ensure that the punching force is always within a safe range; Select appropriate lubricants according to the material characteristics and forming process. For example, graphite lubricants can be selected for metal materials, and special plastic lubricants can be selected for plastic materials. In addition to conventional smear lubrication, automatic lubrication can also be used to evenly distribute the lubricant between the mold and the material. For example, lubrication nozzles can be installed at key parts of the mold to spray the lubricant into the forming area through compressed air; Clean the mold surface regularly to remove residual lubricant, metal chips and other impurities to ensure good contact between the mold and the material. The cleaning frequency can be adjusted according to production volume and material characteristics. It is generally recommended to clean once after each shift.

[0035] This embodiment also provides a computer device, which is suitable for the design method of automobile cover mold, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the design method of automobile cover mold proposed in the above embodiment.

[0036] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0037] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for designing a mold for an automobile cover part as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0038] In summary, the present invention uses: working condition data, optimizes the three-dimensional model of the mold, and creates a digital twin model to build a virtual production environment. The construction of a virtual production environment allows engineers to perform various simulation and debugging operations without interfering with actual production, discover potential problems in advance and optimize them. More importantly, through real-time monitoring and remote maintenance functions, the operating status of the production line can be grasped at any time, the molding parameters of the mold can be adjusted in time, and the stability and efficiency of the production process can be ensured. The use of digital twin technology realizes the intelligent management of the entire life cycle of the mold, significantly improving production efficiency and product quality.

[0039] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the file encryption method are provided.

[0040] In order to verify the effectiveness of a new automobile panel mold design method, a common car door was selected as a panel sample in the experiment.

[0041] In this case, the goal was to create a mold that was both light and strong while ensuring that the manufacturing cost remained at a reasonable level. To this end, CATIAV5 was selected as the 3D modeling tool, and specific constraints were set, such as a minimum thickness of 10 mm, a maximum stress of no more than 200 MPa, a surface finish of Ra0.8 μm, and a fatigue life of more than 10 6 cycles and the coefficient of thermal expansion is less than 12x10 -6 / °C.

[0042] Next, ANSYS Workbench was used to simulate and analyze the 3D model, taking into account load conditions such as punching force (up to 1000 kN), support force, friction force, and temperature field. Boundary conditions included fixed end, free end, and sliding support.

[0043] Subsequently, a digital twin model was created, and a complete virtual production environment was built on the Siemens MindSphere platform, including key components such as stamping machines, conveyor belts, robots, and inspection equipment.

[0044] In order to evaluate the impact of different working condition combinations on the forming quality of the cover, the experiment introduced multiple simulation parameters, such as preheating time, cooling efficiency, cover thickness and lubricant type, and combined them with the working conditions to form simulation vectors and working condition combination matrices. By calculating the impact score, the best working condition combination was determined, and multiple virtual debugging was carried out based on this, and finally a satisfactory simulation result was obtained.

[0045] Finally, a variety of sensors were deployed in the actual production line to collect real-time data, including information on temperature, force, displacement, vibration, and vision. A web-based application remote monitoring platform was developed to monitor the status of the production line and provide intelligent alarm functions. The molding parameters were dynamically adjusted through the convolutional neural network prediction model, achieving continuous optimization of the production process and completing the preparation of the final design report.

[0046] The details are shown in Table 1 below: Table 1 Design method of automobile panel mold From this table, we can see that: The maximum stress of the comparison mold is 195MPa, while the maximum stress of the mold optimized by the new design method is reduced to 148MPa, a decrease of 24.1%. This significant reduction shows that the optimized mold can maintain a lower internal stress level when subjected to the same load. Lower stress means that the mold structure is more stable and less prone to permanent deformation or fatigue damage, thereby extending the service life of the mold.

[0047] The weight of the comparison mold is 460kg, while the weight of the optimized mold is reduced to 345kg, a reduction of 25.0%. The significant weight reduction not only reduces the cost of transportation and installation, but also improves the convenience of operation and the flexibility of the production line. Lighter molds help save energy and reduce emissions, which is very consistent with the environmental protection requirements of modern manufacturing.

[0048] The production cycle of the comparison mold is 87 minutes, while the optimized mold shortens this time to 59 minutes, a reduction of 32.2%. The shortened production cycle directly improves production efficiency, increases production capacity and market response speed. This improvement helps to reduce the manufacturing cost of each unit product and enhance the competitiveness of the enterprise.

[0049] The material cost of the comparison mold is 7900 yuan, while the cost of the optimized mold is reduced to 5800 yuan, a reduction of 26.6%. The significant reduction in material cost is due to the use of topology optimization technology, which reduces unnecessary material usage and ensures structural strength. This optimization not only saves the purchase cost of raw materials, but also reduces processing and handling costs, which is a very important economic advantage for large-scale production and long-term operation.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A design method for an automobile panel mold, characterized in that: include, Create a 3D model of the mold; Apply load conditions and boundary conditions to the three-dimensional model of the mold to obtain a working condition data report; Optimize the mold 3D model based on the working condition data report; Based on the optimized mold 3D model, create a digital twin model and build a virtual production environment; Debug the mold in the virtual production environment and obtain simulation results; Monitor and remotely maintain according to simulation results to obtain real-time production data and monitoring results; Adjust the molding parameters of the mold based on real-time data and monitoring results, and generate a final design report.

2. The method for designing a mold for an automobile cover according to claim 1, characterized in that: Creating a 3D model of the mold includes the following steps: Select weight reduction, stiffness improvement and manufacturing cost reduction as the main design objectives of the mold; Set constraints based on the main design goals of the mold; The constraints include minimum thickness, maximum stress, surface finish, fatigue life, and thermal expansion coefficient of the mold; Select Siemens MindSpher as the digital twin platform according to the set constraints and generate a parameter report; The parameter report includes the main design objectives and constraints of the mold; Based on the parametric report, CATIA V5 was selected as the 3D modeling tool; Create the main structural components of the mold according to the specific shape and size of the cover; The main structural components of the mold include an upper mold, a lower mold, a limit block and a guide column; After creation, export the 3D model of the mold.

3. The method for designing a mold for an automobile cover according to claim 2, characterized in that: Apply load conditions and boundary conditions to the three-dimensional model of the mold and obtain the working condition data report, including the following steps: Select ANSYS Workbench as the simulation tool and COMSOL Multiphysics as the auxiliary simulation platform; Input the 3D model of the mold into ANSYS Workbench; According to the specific properties of the mold material, the load conditions and boundary conditions of the mold are applied and analyzed to generate a preliminary working condition data report; The load conditions of the mold include punching force, supporting force, friction force and temperature field; The boundary conditions of the mold include a fixed end, a free end and a sliding support; According to the preliminary working condition data report and the specific working environment of the mold, define the simulation conditions of multi-physics coupling in COMSOL Multiphysics and perform multi-physics coupling simulation to generate a multi-physics coupling analysis report; Integrate the preliminary operating data report and multi-physics field coupling analysis report to form the final operating data report; The final working condition data report includes a punching force distribution diagram, a supporting force distribution diagram, a friction force distribution diagram, a temperature field distribution diagram, and the results of multi-physical field coupling analysis.

4. The method for designing a mold for an automobile cover according to claim 3, characterized in that: Based on the working condition data report, the mold 3D model is optimized, including the following steps: Altair OptiStruct was chosen as the topology optimization tool; Input the final working condition data report into Altair OptiStruct; Based on the main structural components of the mold, set the mesh refinement parameters and adjust the material distribution of the mold to generate the optimized structure of the mold; Perform additive manufacturing compatibility check on the optimized structure of the mold and obtain the inspection results; The optimized structure of the mold is modified according to the inspection results to obtain an optimized three-dimensional model of the mold.

5. The method for designing a mold for an automobile cover according to claim 4, characterized in that: Based on the optimized mold 3D model, create a digital twin model and build a virtual production environment, including the following steps: The optimized mold 3D model is input into Siemens MindSphere for adjustment to create a digital twin model; Use digital twin models to build a virtual production environment; Build production equipment and set up production processes in a virtual production environment; The production equipment includes a punching machine, a conveyor belt, a robot and a testing equipment; The production process includes stamping process, conveyor belt transportation, robot operation and testing process.

6. The method for designing a mold for an automobile cover according to claim 5, characterized in that: Debugging the mold in the virtual production environment and obtaining the simulation results include the following steps: Introduce simulation parameters into the virtual production environment and combine them into simulation vectors; The load conditions and boundary conditions of the mold are combined into a working condition combination matrix and normalized at the same time; The simulation parameters include mold preheating time, cooling efficiency, cover thickness, and lubricant type; Combining the simulation vector and the working condition combination matrix, the influence scores of different working condition combinations on the forming quality of the cover are calculated, and the expression is: ； in, It indicates the influence score of different working condition combinations on the forming quality of the cover. represents the upper limit of simulation time, represents the number of operating condition combinations, Indicates The forming quality index of each working condition combination, Represents the normalized The working condition combination matrix, Represents the simulation vector The weight function of the impact on the operating condition combination matrix, Represents a very small positive number; Set a threshold for the quality of the panel forming, and determine whether the standard for virtual commissioning is met based on the impact of different working conditions on the quality of the panel forming within the threshold. When the standard for virtual debugging is met, virtual simulation is started to perform virtual debugging and obtain simulation results.

7. The method for designing a mold for an automobile cover according to claim 6, characterized in that: According to the simulation results, monitoring and remote maintenance are carried out to obtain real-time production data and monitoring results, including the following steps: Combining the simulation results with the actual production needs of the mold, a variety of sensors are equipped on the actual production line and installed on the production equipment to collect real-time production data; The multiple sensors include temperature sensors, force sensors, displacement sensors, vibration sensors and visual sensors; Developed a web-based remote monitoring platform based on real-time production data and the Siemens MindSphere digital twin platform; The remote monitoring platform is used to monitor the operating status of the production line and obtain the monitoring results.

8. The method for designing a mold for an automobile panel according to claim 7, characterized in that: Adjust the molding parameters of the mold based on real-time data and monitoring results, and generate the final design report, including the following steps: Based on real-time data and monitoring results, convolutional neural network is selected as the model architecture to build a prediction model; Use the prediction model to predict the production performance of the mold in the future and dynamically adjust the molding parameters of the mold; The molding parameters of the mold include punching speed parameters, punching force parameters, temperature parameters, friction parameters, displacement parameters and preheating time parameters; Optimize the production process according to the adjusted mold molding parameters and generate the final design report; The final design report includes the mold geometry, selected materials and molding parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for designing a mold for an automobile cover part according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for designing a mold for an automobile cover part according to any one of claims 1 to 8 are implemented.

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