A design method for automotive panel dies

Abstract

The present invention discloses a design method for an automotive panel die, which relates to the technical field of die design. The method includes applying load conditions and boundary conditions to the three-dimensional model of the die to obtain a working condition data report. Based on the working condition data report, the three-dimensional model of the die is optimized and a digital twin model is created, and a virtual production environment is built. The construction of the virtual production environment enables engineers to carry out various simulation and debugging operations without interfering with actual production, discover potential problems in advance and optimize them. More importantly, by using the real-time monitoring and remote maintenance functions, the operation status of the production line can be grasped at any time, and the forming parameters of the die can be adjusted in time to ensure the stability and efficiency of the production process. The present invention realizes the intelligent management of the whole life cycle of the die through digital twin technology, significantly improving the production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of die design, and particularly to a design method for automotive panel dies. Background Art

[0002] As an important part of the appearance of an automobile, the manufacturing quality of automotive panels directly affects the aesthetics and performance of the whole vehicle. The traditional design method for panel dies mainly relies on engineers to design according to manual calculations, and then gradually optimize the die structure and process parameters through trial die, debugging and other means. With the rapid development of the automotive industry, the traditional design method has gradually revealed problems such as low efficiency, high cost, and difficulty in meeting complex geometric shapes and high-precision requirements.

[0003] The existing digital twin technology has insufficient application depth and is difficult to achieve intelligent design of the whole process. Most applications are limited to virtual simulation and fault diagnosis, and do not truly achieve intelligent design of the whole process from design to production. In addition, when applying load conditions and boundary conditions, it often relies on simplified assumptions and cannot truly reflect the complex stress conditions of the die 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 automotive panel dies, which solves the problem of insufficient application depth of digital twin in the design of automotive panel dies and the difficulty in achieving intelligent design of the whole process.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a design method for automotive panel dies, which includes creating a three-dimensional model of the die;

[0008] Applying load conditions and boundary conditions to the three-dimensional model of the die to obtain a working condition data report;

[0009] Optimizing the three-dimensional model of the die based on the working condition data report;

[0010] Based on the optimized three-dimensional model of the die, creating a digital twin model and building a virtual production environment;

[0011] Debugging the die in the virtual production environment to obtain simulation results;

[0012] Monitoring and remotely maintaining according to the simulation results to obtain real-time production data and monitoring results;

[0013] Adjusting the forming parameters of the die based on the real-time data and monitoring results to generate a final design report.

[0014] As a preferred solution of the design method of the automotive panel die described in the present invention, wherein: creating a three-dimensional model of the die, including the following steps,

[0015] Select reducing weight, increasing stiffness, and reducing manufacturing cost as the main design objectives of the die;

[0016] Set constraint conditions according to the main design objectives of the die;

[0017] The constraint conditions include the minimum thickness, maximum stress, surface finish, fatigue life, and thermal expansion coefficient of the die;

[0018] Select Siemens MindSphere as the digital twin platform according to the set constraint conditions and generate a parameter report;

[0019] The parameter report includes the main design objectives and constraint conditions of the die;

[0020] Based on the parameter report, select CATIA V5 as the three-dimensional modeling tool;

[0021] Create the main structural components of the die according to the specific shape and size of the panel;

[0022] The main structural components of the die include the upper die, lower die, limit block, and guide pillar;

[0023] After creation, export the three-dimensional model of the die.

[0024] As a preferred solution of the design method of the automotive panel die described in the present invention, wherein: applying load conditions and boundary conditions to the three-dimensional model of the die to obtain a working condition data report, including the following steps,

[0025] Select ANSYS Workbench as the simulation tool and COMSOL Multiphysics as the auxiliary simulation platform;

[0026] Input the three-dimensional model of the die into ANSYS Workbench;

[0027] According to the specific properties of the die material, apply the load conditions and boundary conditions of the die and conduct an analysis to generate a preliminary working condition data report;

[0028] The load conditions of the die include punching force, supporting force, friction force, and temperature field;

[0029] The boundary conditions of the die include fixed end, free end, and sliding support;

[0030] Define the simulation conditions for multi-physics coupling in COMSOL Multiphysics and perform multi-physics coupling simulation according to the preliminary working condition data report and the specific working environment of the die, and generate a multi-physics coupling analysis report;

[0031] Integrate the preliminary working condition data report and the multi-physics coupling analysis report to form the final working condition data report;

[0032] The final working condition data report includes the punching force distribution map, the supporting force distribution map, the friction force distribution map, the temperature field distribution map, and the results of multi-physics coupling analysis.

[0033] As a preferred solution of the design method of the automotive panel die of the present invention, wherein: based on the working condition data, optimize the three-dimensional model of the die, including the following steps,

[0034] Select Altair OptiStruct as the topology optimization tool;

[0035] Input the final working condition data report into Altair OptiStruct;

[0036] Based on the main structural components of the die, set the mesh refinement parameters and adjust the material distribution of the die to generate the optimized structure of the die;

[0037] Check the additive manufacturing compatibility of the optimized structure of the die to obtain the inspection result;

[0038] Modify the optimized structure of the die according to the inspection result to obtain the optimized three-dimensional model of the die.

[0039] As a preferred solution of the design method of the automotive panel die of the present invention, wherein: based on the optimized three-dimensional model of the die, create a digital twin model and build a virtual production environment, including the following steps,

[0040] Input the optimized three-dimensional model of the die into Siemens MindSphere for adjustment to create a digital twin model;

[0041] Use the digital twin model to build a virtual production environment;

[0042] Set up production equipment and production processes in the virtual production environment;

[0043] The production equipment includes a punching machine, a conveyor belt, a robot, and a detection device;

[0044] The production process includes a punching process, conveyor belt transportation, robot operation, and a detection process.

[0045] As a preferred solution of the design method of the automotive panel die described in the present invention, wherein: the die is debugged in a virtual production environment to obtain simulation results, including the following steps,

[0046] Introduce simulation parameters in the virtual production environment and combine them into a simulation vector;

[0047] Combine the load conditions and boundary conditions of the die into a working condition combination matrix and perform normalization at the same time;

[0048] The simulation parameters include the preheating time of the die, the cooling efficiency, the thickness of the panel, and the type of lubricant;

[0049] Combined with the simulation vector and the working condition combination matrix, calculate the influence score of different working condition combinations on the forming quality of the panel. The expression is:

[0050] ;

[0051] Wherein, represents the influence score of different working condition combinations on the forming quality of the panel, represents the upper limit of the simulation time, represents the number of working condition combinations, represents the th forming quality index of the working condition combination, represents the th normalized working condition combination matrix, represents the simulation vector weight function of the influence of the working condition combination matrix, represents a very small positive number;

[0052] Set the threshold of the forming quality of the panel, and determine whether the standard for virtual debugging is reached according to the interval within the threshold of the influence score of different working condition combinations on the forming quality of the panel;

[0053] When the standard for virtual debugging is reached, start virtual simulation for virtual debugging to obtain simulation results.

[0054] As a preferred solution of the design method of the automotive panel die described in the present invention, wherein: monitor and remotely maintain according to the simulation results to obtain real-time production data and monitoring results, including the following steps,

[0055] Combine the simulation results and the actual production requirements of the die, and equip a variety of sensors on the actual production line and install them on the production equipment to collect real-time production data;

[0056] The variety of sensors include temperature sensors, force sensors, displacement sensors, vibration sensors, and vision sensors;

[0057] Based on the real-time production data and the Siemens MindSphere digital twin platform, a remote monitoring platform based on a web application is developed;

[0058] The operating status of the production line is monitored using the remote monitoring platform to obtain monitoring results.

[0059] As a preferred solution of the design method of the automotive panel die described in the present invention, wherein: based on the real-time data and the monitoring results, the forming parameters of the die are adjusted to generate a final design report, including the following steps,

[0060] Based on the real-time data and the monitoring results, a convolutional neural network is selected as the model architecture to construct a prediction model;

[0061] The production performance of the die in a future period is predicted using the prediction model, and the forming parameters of the die are dynamically adjusted;

[0062] The forming parameters of the die include stamping speed parameters, punching force parameters, temperature parameters, friction force parameters, displacement parameters, and preheating time parameters;

[0063] The production process is optimized according to the adjusted forming parameters of the die, and a final design report is generated;

[0064] The final design report includes the geometry of the die, the selected materials, and the forming parameters.

[0065] In a second aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the design method of the automotive panel die described in the first aspect of the present invention is implemented.

[0066] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by the processor, any step of the design method of the automotive panel die described in the first aspect of the present invention is implemented.

[0067] The beneficial effects of the present invention are as follows: based on the working condition data report, the three-dimensional model of the die is optimized while creating a digital twin model, and a virtual production environment is built. The construction of the virtual production environment enables engineers to perform various simulation and debugging operations without interfering with the actual production, discover potential problems in advance and optimize them. More importantly, through the real-time monitoring and remote maintenance functions, the operating status of the production line can be grasped at any time, the forming parameters of the die can be adjusted in a timely manner, and the stability and efficiency of the production process can be ensured. Through digital twin technology, the intelligent management of the entire life cycle of the die is realized, and the production efficiency and product quality are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0069] Figure 1 It is a flowchart of the design method for the automotive panel die in Embodiment 1;

[0070] Figure 2 It is a determination diagram of the standard for virtual commissioning in Embodiment 1. Detailed Embodiments

[0071] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0072] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0073] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0074] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a design method for an automotive panel die, including the following steps:

[0075] S1. Create a 3D model of the die.

[0076] It includes the following steps,

[0077] Select reducing weight, improving stiffness, and reducing manufacturing costs as the main design objectives of the die;

[0078] By clarifying these design objectives, the performance and economy of the die can be fully considered in the design stage, ensuring that the finally designed die 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;

[0079] Set the constraint conditions according to the main design objectives of the mold;

[0080] The constraint conditions include the minimum thickness of the mold (set based on the stress of the material and the manufacturing process), the maximum stress (set based on the yield strength of the material), the surface finish (the material of the panel, the forming process, and the subsequent painting process need to be considered), the fatigue life (set based on the fatigue strength of the material and the actual working conditions), and the coefficient of thermal expansion (the working temperature range of the mold needs to be considered);

[0081] Setting these constraint conditions fully considers 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 the problem of mold failure caused by unreasonable design, and improving the reliability and service life of the mold;

[0082] Select Siemens MindSphere as the digital twin platform according to the set constraint conditions and generate a parameter report;

[0083] The reason for choosing Siemens MindSphere is that Siemens MindSphere is a cloud-based Internet of Things operation platform that can connect production equipment in the physical world with digital models in the virtual environment in real time to achieve comprehensive monitoring and optimization of the production process;

[0084] The parameter report includes the main design objectives and constraint conditions of the mold;

[0085] Select CATIA V5 as the 3D modeling tool based on the parameter report;

[0086] CATIA V5 is a high-end CAD software widely used in fields such as aerospace and automotive manufacturing, with powerful 3D modeling capabilities and high-precision geometric analysis capabilities;

[0087] Create the main structural components of the mold according to the specific shape and size of the panel;

[0088] The main structural components of the mold include the upper mold, the lower mold, the limit block, and the guide pillar;

[0089] According to the forming process requirements of the panel, determine the shapes and dimensions of the upper die and the lower die. For example, if the panel needs to be drawn formed, the shape of the upper die should exactly match the outer contour of the panel, while the shape of the lower die should exactly match the inner contour of the panel. At the same time, considering the material flow path and forming depth, use modeling tools such as "Extrusion", "Revolution" or "Sweeping" in CATIA V5 to create the base bodies of the upper die and the lower die (the thickness of the base body should be set according to the minimum thickness constraint conditions of the die. Usually, the thickness range of the upper die is 10 mm to 30 mm, and the thickness range of the lower die is 20 mm to 50 mm. The specific values depend on the size of the panel), and add forming features one by one. For example, for complex curved surface panels, multiple drawing beads, bosses, grooves and other features may need to be added to ensure that the material can flow smoothly during the forming process and obtain the ideal shape.

[0090] To ensure that the panel has good surface quality after forming, the surface finish of the upper and lower dies needs to reach a certain standard, and its surface roughness should be controlled below Ra 0.8 μm. The surfaces of the upper and lower dies can be polished through the surface treatment function in CATIA V5 to ensure that they meet the design requirements;

[0091] According to the movement trajectory and stamping stroke of the die, determine the number and positions of the limit blocks. The limit blocks should be installed at the four corners of the die to ensure that the die will not shift or collide during the stamping process. Its geometric shape should be designed according to the structural characteristics of the die, and the height should be slightly higher than the maximum stroke of the upper and lower dies. The width and thickness should be adjusted according to the force-bearing situation and installation space of the die, and it is necessary to ensure sufficient stiffness and strength;

[0092] The materials of the limit blocks should have high hardness and wear resistance to prevent wear or damage during long-term use. Commonly used limit block materials include high-hardness steel (SKD11), wear-resistant plastics (nylon), etc. Select the appropriate materials according to the actual working conditions and ensure that their thermal expansion coefficients match the main body material of the die to avoid dimensional deviations caused by thermal expansion and contraction;

[0093] To ensure the firm and reliable installation of the limit blocks, usually use bolt connection to fix them on the die. When designing the limit blocks, sufficient installation holes or welding areas should be reserved to ensure that they can be closely combined with the main body of the die;

[0094] The guide pillars are used to ensure the precise alignment of the upper and lower dies during the stamping process and avoid forming quality problems caused by deviation (determined according to the structural characteristics of the die and the movement mode of the stamping machine). Usually, they are fixed on the die by interference fit. When designing the guide pillars, the maintainability of the guide pillars should be considered to facilitate replacement or repair when needed;

[0095] After creation, export the 3D model of the mold;

[0096] According to specific application requirements, select an appropriate file format (STEP) to export the 3D model of the mold. Before export, use the inspection tools in CATIA V5 to check the integrity of the 3D model of the mold (continuity of geometric shape, accuracy of dimensions, and assembly relationship between components).

[0097] S2. Apply load conditions and boundary conditions to the 3D model of the mold to obtain a working condition data report.

[0098] It includes the following steps,

[0099] Select ANSYS Workbench as the simulation tool and COMSOL Multiphysics as the auxiliary simulation platform;

[0100] The reason for choosing ANSYS Workbench here is that it has a built-in advanced non-linear solver, which can handle complex geometric shapes and non-linear material properties. Especially for complex structures such as stamping dies involving large deformations and contact problems, the non-linear solver can provide more accurate stress and strain distributions. COMSOL Multiphysics is specifically used for multi-physics coupling analysis, which can handle the interactions between multiple physical fields and provide detailed multi-physics coupling simulation results;

[0101] Input the 3D model of the mold into ANSYS Workbench;

[0102] Open ANSYS Workbench, select Geometry, click the Import Geometry button, select the exported 3D model file and load the model, which is displayed in the working area. After importing the model, use the Check Geometry function to check whether there are geometric defects in the model (such as self-intersection, gaps, overlaps). If problems are found, the Repair Geometry function can be used for repair. At the same time, the model can also be appropriately simplified. For example, remove secondary features that do not affect the stress distribution (such as threaded holes, chamfers);

[0103] According to the specific properties of the mold material, apply the load conditions and boundary conditions of the mold and conduct analysis to generate a preliminary working condition data report;

[0104] The load 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 die on the covering part to prevent excessive deformation of the covering part), friction force (affecting the material flow and forming quality), and temperature field (affecting the mechanical properties of the material);

[0105] The boundary conditions of the die include fixed ends (restricting the movement and rotation of the die, located on the base or support structure of the die, restricting the three translational degrees of freedom and three rotational degrees of freedom of the die), free ends (simulating the situation where certain parts of the die can move freely during the stamping process, located at the edge or overhanging part of the die, allowing the die to deform within a certain range), and sliding supports (simulating the situation where certain parts of the die slide along a specific direction during the stamping process, located on the guide pillars or limit blocks of the die, allowing relative sliding of the die in the direction perpendicular to the contact surface);

[0106] According to the actual material of the die, select the corresponding material model. ANSYS Workbench provides a rich material library. Engineers can directly select materials from the library or manually input mechanical property parameters such as elastic modulus, Poisson's ratio, and yield strength of the material. For non-linear materials, the stress-strain curve can be defined to ensure that the simulation results are closer to the actual situation;

[0107] After applying the load conditions and boundary conditions, it is necessary to mesh the 3D model of the die (to improve the accuracy of the model). For complex geometries and stress concentration areas, local mesh refinement can be carried out. In ANSYS Workbench, select Mesh to set the type (such as tetrahedron, hexahedron), size, and density of the mesh. After setting, click the Solve button to start the simulation calculation. ANSYS Workbench will calculate physical quantities such as stress, strain, and displacement of the die under different working conditions according to the applied load conditions and boundary conditions, and generate a preliminary working condition data report;

[0108] According to the preliminary working condition data report and the specific working environment of the die, 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;

[0109] The simulation conditions of multi-physics coupling here include:

[0110] Thermal-mechanical coupling: That is, considering the influence of temperature change on the structural stress of the die. For example, high temperature may cause material expansion, which in turn affects the accuracy and life of the die;

[0111] Electromagnetic-mechanical coupling: If the die involves electromagnetic effects (such as electromagnetic forming process), analyze the influence of the electromagnetic field on the deformation of the die;

[0112] Fluid-structure coupling: If there is fluid involved (such as coolant), analyze the influence of fluid flow on the die structure (such as the flow path and speed of the coolant will affect the cooling efficiency and uniformity of the die);

[0113] Use the adaptive mesh function of COMSOL to refine the mesh according to the model complexity and the importance of key regions (such as increasing the mesh density in stress concentration regions and where temperature changes are drastic), and select a suitable iterative solver and adjust the solver parameters to improve the calculation efficiency and accuracy;

[0114] After setting the solver parameters, start the simulation calculation. View the stress distribution, temperature field, etc. of the current mold through the graphical interface of COMSOL, and check the convergence of the simulation results. If it does not converge, adjust the mesh and solver parameters and run again until all physical fields reach a stable state;

[0115] Extract key data from the simulation results (such as electromagnetic field intensity, fluid flow path, etc.), organize the extracted data into a written description, and generate a multi-physics coupling analysis report;

[0116] In this step, the reason for introducing the concept of multi-physics coupling is that the analysis of a single physical field cannot comprehensively reflect the performance of the mold in the actual working environment. For example, only considering mechanical stress may ignore the influence of temperature changes on material properties, resulting in design defects. Through multi-physics coupling analysis, the interaction between different physical phenomena can be better understood, thereby optimizing the design and manufacturing process of the mold. Finally, using virtual simulation to detect problems in advance reduces the number of trial-and-error times and the cost of physical tests, and shortens the mold development cycle;

[0117] Integrate the preliminary working condition data report and the multi-physics coupling analysis report to form the final working condition data report;

[0118] 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 coupling analysis;

[0119] In this step, the final working condition data report provides a more comprehensive design basis, covering all information from basic mechanical analysis to complex multi-physics coupling analysis, which helps designers make more scientific and reasonable decisions and ensure the optimality of the mold design.

[0120] S3. Optimize the 3D model of the mold based on the working condition data report.

[0121] It includes the following steps,

[0122] Select Altair OptiStruct as the topology optimization tool;

[0123] The reason for choosing Altair OptiStruct here is that it has powerful topology optimization capabilities and can be seamlessly integrated with other simulation tools such as ANSYS Workbench, and can truly reflect the behavior of the mold under actual working conditions;

[0124] Input the final working condition data report into Altair OptiStruct;

[0125] Export the working condition data report from ANSYS Workbench. Open Altair OptiStruct, select LoadCase, click Import LoadCase, select the working condition data file and use the CheckLoadCase function of Altair OptiStruct to check the integrity and accuracy of the working condition data. If any problems are found, such as mismatched load conditions or missing boundary conditions, corresponding modifications can be made;

[0126] 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;

[0127] To improve the optimization efficiency, mesh refinement can be carried out in some key areas while keeping coarser meshes in other areas. For example, for stress concentration areas such as the contact area between the upper and lower molds, the installation positions of the limit blocks and guide columns, smaller mesh sizes can be set to ensure the simulation accuracy of these areas, while for other parts of the mold, larger mesh sizes can be set to reduce the consumption of computing resources;

[0128] After the mesh refinement parameters are divided;

[0129] Use the Topology Optimization function of Altair OptiStruct to set the initial conditions of the material distribution. The user can select different material models (such as elastic materials, plastic materials, composite materials) and define the mechanical property parameters such as density, elastic modulus, and Poisson's ratio of the material according to the actual material properties of the mold to ensure that the optimized structure meets the manufacturing requirements;

[0130] After completing the material distribution settings, click the Run Optimization button to start the topology optimization calculation. Altair OptiStruct will automatically adjust the material distribution according to the applied load conditions and constraint conditions to generate the optimized mold structure. During the optimization process, the user can view the optimization progress and results in real time through the graphical interface;

[0131] The optimized structure not only meets the design goals (such as reducing weight, increasing stiffness, and reducing stress) while also meeting all the constraint conditions (such as minimum thickness, maximum stress). In addition, the optimized structure also has good manufacturability, facilitating subsequent processing and manufacturing;

[0132] Conduct an additive manufacturing compatibility check on the optimized die structure to obtain the inspection results (such as overhanging structures, internal cavities, minimum feature sizes, and material selection);

[0133] Import the optimized die structure into additive manufacturing software (Netfabb);

[0134] During the additive manufacturing inspection process, overhanging structures (i.e., free-hanging parts without support) are prone to causing printing failures. Use the OverhangAnalysis function of Netfabb to check whether there are overhanging structures in the optimized structure;

[0135] Internal cavities can affect the printing speed and material usage;

[0136] Use the InternalStructureAnalysis function to check whether there are internal cavities in the optimized structure;

[0137] Too small feature sizes (such as slender holes, thin-walled structures) are prone to causing a decline in printing quality. Use the FeatureSizeAnalysis function of Netfabb to check whether there are too small feature sizes in the optimized structure;

[0138] The material selection directly affects the printing quality and cost. According to the mechanical property requirements of the optimized structure, select appropriate additive manufacturing materials (such as metal powders, plastic particles). Commonly used additive manufacturing materials include aluminum alloys, titanium alloys, stainless steels, nylons, etc.;

[0139] After completing all inspections, generate an additive manufacturing compatibility inspection result report;

[0140] Modify the optimized die structure according to the inspection results to obtain an optimized 3D die model;

[0141] If the inspection results show the existence of overhanging structures, support structures (such as triangular supports, grid supports) can be added to the optimized structure to reduce the height or angle of the overhanging part, and users can choose appropriate support methods according to actual needs;

[0142] If the inspection results show the existence of internal cavities, it can be decided whether to retain or fill them according to actual needs. For internal cavities that do not need to be retained, use the FillTool function to fill them into solid structures to improve printing efficiency. For internal cavities that need to be retained, use the LatticeTool function to generate lightweight lattice structures (saving materials while maintaining structural strength);

[0143] If the inspection results show that there are feature sizes that are too small, their sizes can be appropriately increased to ensure the feasibility of the printing process. For complex features such as slender holes and thin-walled structures, a gradual transition method needs to be adopted to gradually increase their sizes to avoid manufacturing difficulties caused by sudden changes;

[0144] 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;

[0145] After all modifications are completed, the optimized 3D model of the mold is exported in a standard format (such as STL or IGES).

[0146] S4. Based on the optimized 3D model of the mold, create a digital twin model and build a virtual production environment. The following steps are included,

[0147] Input the optimized 3D model of the mold into Siemens MindSphere for adjustment to create a digital twin model;

[0148] Open the Siemens MindSphere platform, select Asset Modeling, click the Import Asset button, and import the optimized 3D model of the mold into Siemens MindSphere;

[0149] According to the actual production requirements, adjust the thickness of each part of the mold. For example, for stress concentration areas (such as the edge where the upper mold contacts the covering part), the thickness can be appropriately increased to improve stiffness, while for non-critical areas (such as the support part of the lower mold), the thickness can be appropriately reduced to reduce weight. The adjustment of the thickness should follow the minimum thickness constraint conditions to ensure that the mold will not undergo excessive deformation or fracture when bearing the load;

[0150] Add chamfers or fillets at the sharp edges of the mold to avoid cracks or damage caused by stress concentration. The sizes of the chamfers and fillets should be reasonably set according to the stress conditions and manufacturing processes of the mold to ensure that they do not affect the forming quality and can improve the durability of the mold;

[0151] According to the requirements of the actual production equipment, adjust the positions of the mounting holes and fixing points of the mold to ensure that these positions can perfectly match the interfaces of equipment such as stamping machines and robots, and avoid failures or deviations caused by improper installation;

[0152] After all adjustments are completed, click the Create Digital Twin button to create a digital twin model;

[0153] Build a virtual production environment using the digital twin model;

[0154] Build production equipment and set the production process in the virtual production environment;

[0155] The production equipment includes a stamping machine, a conveyor belt, a robot, and a detection device;

[0156] The production process includes a stamping process, conveyor belt transportation, robot operation, and a detection process;

[0157] Import the floor plan of the actual production workshop (the size of the workshop, equipment location, passageways) as the basis for the virtual factory layout;

[0158] According to the layout of the actual production workshop, use the FactoryLayout tool in the digital twin model to place the production equipment in the virtual environment. The user can, through drag-and-drop operations, place the equipment in the appropriate position, adjust its orientation and angle, and at the same time set the connection method of each production equipment with other equipment. For example, a material transfer interface needs to be set between the conveyor belt and the stamping machine, an operation point for grasping 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 device and the conveyor belt;

[0159] Plan the transportation path of the materials to ensure the smooth flow of materials in the virtual environment. For example, from the loading area to the stamping 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, safety areas and protective facilities can be added in the virtual environment. For example, set up safety fences and emergency stop buttons;

[0160] To ensure the normal operation of the virtual production equipment in the virtual environment, detailed equipment parameters must be set for each equipment, including the die change time, stroke control of the stamping machine, transportation speed, load capacity, start and stop times, fault detection of the conveyor belt, movement trajectory of the robot, grasping force (the force for grasping materials), accuracy, and frequency of the detection device;

[0161] After completing the equipment configuration and parameter setting, define the specific production process of the virtual production line (loading - stamping - conveying - robot operation - detection) according to the actual production requirements, 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.

[0162] S5. Debug the die in the virtual production environment to obtain the simulation results.

[0163] It includes the following steps

[0164] Introduce simulation parameters in the virtual production environment and combine them into a simulation vector;

[0165] This step ensures comprehensive coverage of different working conditions by introducing simulation vectors, enabling more accurate simulation of various situations in actual production;

[0166] Combine the load conditions and boundary conditions of the mold into a working condition combination matrix and perform normalization at the same time;

[0167] Normalization is performed to unify different types of working condition conditions to the same scale;

[0168] In this step, the normalized working condition combination matrix can be directly used for subsequent scoring calculations, simplifying complex mathematical operations and improving the efficiency of simulation;

[0169] The simulation parameters include the preheating time of the mold (usually between 30 seconds and 5 minutes, and the specific value depends on the size and material of the mold), the cooling efficiency (controlled by the flow rate, temperature and nozzle position of the coolant), the thickness of the covering part (usually between 0.5mm and 2.0mm, and the specific value depends on the product design requirements), and the type of lubricant (such as oil-based lubricant, water-based lubricant, solid lubricant);

[0170] Combining the simulation vector and the working condition combination matrix, calculate the influence score of different working condition combinations on the forming quality of the covering part. The expression is:

[0171] ;

[0172] Among them, represents the influence score of different working condition combinations on the forming quality of the covering part, represents the upper limit of the simulation time, reflecting the influence of the working condition combination during the entire production cycle, represents the number of working condition combinations. Each working condition combination consists of different load conditions (such as punching force, supporting force, friction force, etc.) and boundary conditions (such as fixed end, free end, sliding support, etc.), represents the th forming quality index of the working condition combination. The forming quality index includes multiple aspects such as the surface quality, dimensional accuracy, and material flow uniformity of the covering part. The forming quality index of each working condition combination is obtained through simulation, reflecting the forming effect under this working condition combination. Its role is to combine with the influence weight of the working condition combination to further quantify the specific contribution of different working condition combinations to the forming quality; represents the th normalized working condition combination matrix, represents the simulation vector is the weight function of the influence of the simulation vector on the working condition combination matrix. The introduction of the simulation vector makes the formula able to dynamically reflect the changes of different working condition combinations during the simulation process. The weight function Furthermore, the influence weights 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, certain combinations of working conditions may perform well at high temperatures but poorly at low temperatures. The weight function can dynamically adjust its influence weight according to the temperature change. Represents an extremely small positive number, usually taking a very small value (such as 10 to the power of negative six), and its function is to prevent the denominator from being zero.

[0173] Set the threshold of the forming quality of the covering part (determined according to the quality standard required by the customer, production cost, and efficiency). Determine whether the standard for virtual commissioning is met according to the interval within the threshold of the influence score of different combinations of working conditions on the forming quality of the covering part.

[0174] When the influence score of different combinations of working conditions on the forming quality of the covering part is greater than or equal to the threshold of the forming quality, it is considered that the forming quality under this combination of working conditions is qualified and can enter the virtual commissioning stage.

[0175] When the influence score of different combinations of working conditions on the forming quality of the covering part is less than the threshold of the forming quality, it is considered that the forming quality under this combination of working conditions is unqualified. Use the genetic algorithm (GA) to randomly generate multiple initial combinations of working conditions by simulating the process of biological evolution, and gradually optimize and adjust the simulation variables through operations such as selection, crossover, and mutation. After each adjustment, re-run the simulation and calculate the new forming quality score. And compare it with the previous score. If the new score is higher than the previous score, retain the current combination of working conditions; otherwise, continue to adjust the simulation variables until the optimal combination of working conditions is found.

[0176] When the standard for virtual commissioning is reached, start virtual simulation for virtual commissioning to obtain the simulation results.

[0177] Load the optimal combination of working conditions 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. The user can view the running 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 process, the user can optimize the production parameters according to the actual situation. For example, if it is found that the time of the "transfer" process is too long, the time of this process can be shortened by adjusting the equipment parameters or optimizing the logistics path.

[0178] S6. Monitor and remotely maintain according to the simulation results to obtain the real-time data and monitoring results of production. The following steps are included.

[0179] Combined with the simulation results and the actual production requirements 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;

[0180] The variety of sensors include temperature sensors, force sensors, displacement sensors, vibration sensors, and vision sensors (to monitor the appearance quality of the covering parts);

[0181] According to the simulation results, determine the optimal 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 parts with greater stress, the displacement sensor should be installed in the parts prone to deformation, the vibration sensor should be installed in the parts prone to vibration, and the vision sensor should be installed at the inspection station;

[0182] Based on the real-time production data and the Siemens MindSphere digital twin platform, develop a remote monitoring platform based on a web application;

[0183] Integrate the real-time production data with the Siemens MindSphere digital twin platform (to achieve synchronization between the virtual environment and the actual production environment), use the API interface to upload the real-time data to the digital twin platform and map it to the parameters in the digital twin model to achieve two-way data communication. For example, map the data of the temperature sensor to the temperature parameter of the mold, map the data of the force sensor to the punching force parameter of the stamping machine. When the platform detects a fault in a certain device, it can automatically send a command to stop the operation of the device;

[0184] After the integration is completed, develop a Web-based application based on the Siemens MindSphere platform. The development steps include:

[0185] Design an intuitive and easy-to-use front-end interface to enable users to easily view the operating status of the production line. This interface should include: a dashboard (displaying real-time production data such as temperature, force, displacement, vibration), charts (showing the trend changes of real-time data in the form of bar charts), device status: showing the operating status of the device in a color-coded form (such as green for normal, yellow for warning, red for fault), an alarm list (displaying the current alarm information, including alarm time, alarm type, alarm level), and an operation panel (providing functions for remote operation, such as starting and stopping the device, adjusting operating parameters)

[0186] Develop the back-end logic to handle user requests and return data to the front-end interface, query the real-time production data from the database and format it into a format that can be parsed by the front-end, and at the same time receive the control instructions issued by the user through the Web interface and forward them to the production equipment;

[0187] To facilitate users to monitor the production line anytime and anywhere, the Web application should also support mobile access. The mobile application can be developed through responsive design to ensure smooth use on mobile devices such as mobile phones and tablets.

[0188] Use the remote monitoring platform to monitor the operating status of the production line and obtain the monitoring results.

[0189] Users log in to the remote monitoring platform through a browser or mobile application, enter the username and password for authentication, select 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, users 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 pressure exceeds 1000 Newtons, a serious alarm is triggered; when the displacement exceeds 5 millimeters, 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 in the form of text messages and emails.

[0190] S7. Adjust the forming parameters of the mold based on real-time data and monitoring results to generate the final design report.

[0191] It includes the following steps:

[0192] Based on real-time data and monitoring results, select the convolutional neural network as the model architecture and construct a prediction model.

[0193] The reason for choosing the convolutional neural network here is that one of its core ideas is "local perception", that is, each neuron is only connected to a local area (receptive field) of the input data, rather than 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.

[0194] Use the prediction model to predict the production performance of the mold in the next period of time and dynamically adjust the forming parameters of the mold.

[0195] The forming parameters of the mold include stamping speed parameters, punching pressure parameters, temperature parameters, friction force parameters, displacement parameters, and preheating time parameters.

[0196] Collect data from the sensor once per second and process the collected data using the sliding window mechanism. For example, assuming that the data of 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 a sampling frequency of 1 second) will be formed.

[0197] 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 parameters in the past 5 minutes, rate of change: the difference between adjacent time points, frequency domain features: extract frequency domain features using the Fast Fourier Transform (FFT), such as the main frequency components), and input them into the prediction model (deployed on the edge computing device of the industrial gateway);

[0198] After receiving the feature values, the prediction model will perform forward propagation, calculate the activation values of neurons layer by layer, and finally output the prediction results (artificial intelligence prediction values). For example, the model may output the predicted values of parameters such as temperature, force, and displacement 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 future time point. For example, the model may output that the probability of a fault occurring in the next 5 minutes is 0.8, indicating an 80% likelihood of a fault;

[0199] Based on the prediction results, a rule engine is built in to automatically make control decisions. For example, when it is predicted that the temperature will exceed 80°C, it will be decided to reduce the stamping pressure to reduce heat generation. When it is predicted that the stamping pressure will exceed 1000 kN, it will be decided to reduce the stamping speed to avoid die damage. When it is predicted that the vibration exceeds 0.8, it will be decided to pause the production line for equipment inspection;

[0200] Based on the control decision, send instructions to the production equipment through the PLC (Programmable Logic Controller) to reduce the stamping speed. For example, a new speed setting command can be sent to the stamping machine through the Modbus protocol to monitor the response of the production equipment in real time, ensuring that the stamping speed has been successfully reduced to the target value. If the equipment fails to respond in time, a warning will be issued and an attempt will be made to resend the instruction;

[0201] Send instructions to the cooling center through the SCADA (Supervisory Control and Data Acquisition) to increase the cooling time or cooling intensity. For example, when it is predicted that the temperature will rise to 90°C in the next 5 minutes, it may be decided to increase the flow rate of cooling water from 10 liters per minute to 15 liters. When the temperature returns to normal, the cooling time or cooling intensity will be 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;

[0202] Optimize the production process according to the adjusted forming parameters of the mold and generate the final design report;

[0203] The final design report includes the geometry of the mold, the selected materials, and the forming parameters;

[0204] During the dynamic adjustment process, continuously monitor and record the forming parameters after each adjustment, and count the production output for each shift, each day, and each week to judge the impact of the adjustment on production efficiency. For example, whether the stamping speed after adjustment has increased the production output per unit time, or whether it has reduced the downtime;

[0205] Use a surface roughness meter to detect the surface quality of the panel parts to ensure that the adjusted forming parameters will not cause problems such as surface scratches and depressions. For example, whether the stamping pressure after adjustment ensures that the surface finish of the panel parts meets the standards;

[0206] The optimization of the production process (excessive stamping pressure) can be illustrated from the following aspects:

[0207] The reasons for excessive stamping pressure include unreasonable die structure design (too large or too small gap between the male and female dies of the die), too high strength or insufficient ductility of the used material, insufficient lubrication between the die and the material, and die wear, etc.;

[0208] Optimization measures: According to the thickness of the material and the forming requirements, adjust the gap between the male and female dies (between 5% and 10% of the material thickness) to ensure that the material can flow smoothly and avoid excessive resistance;

[0209] For complex panel parts, a segmented forming process can be considered (divide the entire forming process into multiple steps to be completed gradually), which can reduce the force required for each stamping and reduce the load on the die. For example, first perform preliminary stretching, then perform fine trimming, and finally perform final forming;

[0210] Select high-strength and wear-resistant die materials, such as high-speed steel or cemented carbide, etc., to improve the durability and impact resistance of the die. In addition, the die surface can also be quenched or coated to enhance its wear resistance and anti-adhesion;

[0211] By optimizing the acceleration and deceleration curves of the stamping machine, make the stamping process more stable. For example, adopt a progressive acceleration and deceleration method to reduce the impact force generated during the stamping process. Specifically, it can be slowly accelerated at the beginning of stamping and gradually decelerated when approaching the forming point to avoid sudden impact;

[0212] Install a buffer device (such as a hydraulic buffer or a pneumatic buffer) on the stamping machine, which can absorb part of the impact energy during the stamping process and reduce the peak value of the stamping pressure. The buffer device can be adjusted according to actual needs to ensure that the stamping pressure is always within a safe range;

[0213] According to the material characteristics and forming process, select a suitable lubricant. For example, for metal materials, graphite lubricant can be selected, and for plastic materials, special plastic lubricant can be selected;

[0214] In addition to the conventional lubrication method of smearing, automatic lubrication can also be adopted to evenly distribute the lubricant between the mold and the material. For example, lubrication nozzles can be installed at key parts of the mold, and the lubricant can be sprayed into the forming area through compressed air.

[0215] Regularly clean the mold surface to remove residual lubricant, metal chips and other impurities, and ensure good contact between the mold and the material. The cleaning frequency can be adjusted according to the production volume and material characteristics. Generally, it is recommended to clean once after each shift.

[0216] This embodiment also provides a computer device applicable to the design method of automotive panel molds, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the design method of automotive panel molds proposed in the above embodiment.

[0217] This computer device can be a terminal, which 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 this computer device is used to provide computing and control capabilities. The memory of this 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 this computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication) or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0218] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the design method of the automotive panel die as proposed in the above embodiment; 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, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0219] In summary, the present invention: through working condition data, optimizes the three-dimensional model of the die and simultaneously creates a digital twin model, and builds a virtual production environment. The construction of the virtual production environment enables engineers to perform various simulation and debugging operations without interfering with actual production, discover potential problems in advance and optimize them. More importantly, through the real-time monitoring and remote maintenance functions, the operating status of the production line can be mastered at any time, and the forming parameters of the die can be adjusted in a timely manner to ensure the stability and efficiency of the production process. The use of digital twin technology realizes the intelligent management of the entire life cycle of the die, significantly improving production efficiency and product quality.

[0220] Example 2, referring to Table 1, is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the file encryption method is given.

[0221] To verify the effectiveness of a new automotive panel die design method, a common car door is selected as a panel sample for the experiment.

[0222] In this case, the goal is to create a die that is both lightweight and strong, while ensuring that the manufacturing cost is maintained at a reasonable level. For this purpose, CATIA V5 is selected as the three-dimensional modeling tool, and specific constraint conditions are set, such as a minimum thickness of 10 mm, a maximum stress not exceeding 200 MPa, a surface finish reaching Ra 0.8 μm, a fatigue life exceeding 10 6 cycles and a coefficient of thermal expansion less than 12x10 -6 / °C.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] The details are shown in Table 1 below:

[0228] Table 1 Design method of automobile panel mold

[0229]

[0230] From this table, we can see that:

[0231] 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.

[0232] 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.

[0233] The production cycle of the comparison mold is 87 minutes, while the optimized mold has shortened this time to 59 minutes, a reduction of 32.2%. The shortening of the production cycle directly improves production efficiency, increases production capacity and market response speed. This improvement helps to reduce the manufacturing cost per unit product and enhance the competitiveness of the enterprise.

[0234] The material cost of the comparison mold is 7,900 yuan, while the cost of the optimized mold has been reduced to 5,800 yuan, a decrease of 26.6%. The significant reduction in material cost is due to the adoption of topology optimization technology, which reduces unnecessary material usage and ensures structural strength. This optimization not only saves the procurement cost of raw materials, but also reduces the processing and handling costs, which is a very important economic advantage for large-scale production and long-term operation.

[0235] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within 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 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 the final design report; Debug the mold in the virtual production environment and get the simulation results. The following steps are included: 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.

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 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: 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.

7. The method for designing a mold for an automobile cover according to claim 6, 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.

8. 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 7 are implemented.

9. 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 7 are implemented.

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