A full-automatic process simulation method for multi-factor influence and multi-target optimization of product design

Abstract

The application discloses a kind of full-automatic process simulation method for product design, multiple-factor influence, multiple-target optimization, comprising:1, the data interaction between Creo Parametric and Ansys is established;2, parameterized model is established in Creo Parametric;3, parameterized model is imported into Ansys, meshing, case setting and result processing are carried out, and visual result is obtained;4, based on visual result, DOE experimental design is carried out in Desigin-Expert, and initial sample space is generated;5, initial sample space is imported into the parameter set of Ansys, parameterized simulation is carried out, and initial sample space target value is obtained;6, initial sample space target value is imported into Design-Expert, and target function between multiple factors and multiple targets is fitted;7, based on multiple-target optimization algorithm and weight setting, multiple-target optimization is carried out, and multiple-target optimization result is obtained;8, multiple-target optimization result is returned to Ansys and simulation verification is carried out.The application realizes the automation and process simulation under different structure parameters, and saves simulation time.

Description

Technical Field

[0001] This invention belongs to the field of multi-objective optimization simulation technology, specifically a fully automated process-oriented simulation method for multi-factor influence and multi-objective optimization in product design. Background Technology

[0002] With economic and social development, the demand for a high-quality life places increasingly complex and multi-dimensional design requirements on products. Products must not only meet basic usage needs but also be durable, low-noise, low-pollution, healthy, and comfortable. Therefore, the design process cannot solely pursue performance and efficiency; rather, it must balance performance and user experience. Making trade-offs across multiple dimensions and objectives is precisely the problem that multi-objective optimization aims to solve.

[0003] Because multi-objective optimization requires a large amount of experimental data with varying parameters as samples for modeling the objective function, and experimental methods are costly and time-consuming, simulation methods are widely used for multi-objective optimization problems. A complete simulation study mainly includes the following steps: geometric model establishment → geometric model processing → mesh generation → simulation case setup → simulation calculation → simulation result processing. In the multi-objective optimization process, an influencing factor needs to be considered within the parameter range. n The calculation of each simulation case, and x Each influencing factor needs to be considered. O ( n x Calculation of ) simulation cases.

[0004] To address this, researchers have proposed several multi-objective optimization algorithms to fit high-precision objective functions with fewer samples, thereby quickly and accurately finding the optimal solution within the parameter range. Although the emergence of multi-objective optimization algorithms has greatly reduced the number of samples required, in actual simulation calculations, the establishment of geometric models, mesh generation, case settings, and result processing under different structural parameters still require manual operations such as case setting, file import and export, and data collection and organization, resulting in a large amount of repetitive work. In addition, during product design, it is often difficult to complete the multi-objective optimization objectives at once, requiring multiple changes to the parameter range and topology design.

[0005] Therefore, it is necessary to propose a simulation method suitable for multi-parameter multi-objective optimization, which can achieve fully automated and streamlined simulation calculations under the premise of completing a complete simulation case. Summary of the Invention

[0006] The purpose of this invention is to provide a fully automated, process-oriented simulation method for multi-factor influence and multi-objective optimization in product design, which realizes automated and process-oriented simulation under different structural parameters and saves simulation time.

[0007] This invention is achieved through the following technical solution: A fully automated, process-oriented simulation method for multi-factor influence and multi-objective optimization in product design includes the following steps: Step 1: Establish data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys; Step 2: Build a parametric model in Creo Parametric; Step 3: Import the parametric model into Ansys, perform the complete simulation process of mesh generation, case setting, and result processing, and obtain the visualization results; Step 4: Based on the visualization results, design DOE experiments in Designin-Expert to generate the initial sample space; Step 5: Import the initial sample space into the Ansys parameter set, perform parametric simulation, and obtain the target values ​​of the initial sample space; Step 6: Import the initial sample space target values ​​into Design-Expert, and fit the objective function between multiple factors and multiple objectives in Design-Expert; Step 7: Based on the multi-objective optimization algorithm and weight settings, perform multi-objective optimization to obtain the multi-objective optimization results; Step 8: Return the multi-objective optimization results to Ansys for simulation verification. The process is as follows: Step 8.1: Import the multi-objective optimization results into the Ansys parameter set; Step 8.2: Update the selected design point and calculate the simulation target value of the multi-objective optimization result under the combination of influencing factor parameters; Step 8.3: Compare whether the difference between the Ansys simulation value and the Design-Expert prediction value of the multi-objective optimization results is lower than the error limit. If yes, proceed to step 8.4. If no, increase the number of Ansys simulation samples and return to step 6. Step 8.4: Determine whether the multi-objective optimization results meet the initially set optimization objective values: If yes, save the parameter set and simulation results in the multi-objective optimization design to Excel and end the entire simulation calculation; if no, determine whether the sample space has been completely explored. If the sample space is not fully explored, expand the sample space, return to step 5, import the expanded sample space into the Ansys parameter set for parametric simulation to obtain the corresponding target. If the sample space has been fully explored, the multiphysics diagram is manually analyzed based on the simulation results to identify structures that can be improved, and the topology is improved. Then, the process returns to step 2.

[0008] Furthermore, the process of step 1 is as follows: Step 1.1: Locate the WBPlugInPE.dat file in the Ansys installation directory, open it with Notepad, modify the paths after "EXEC_FILE", "TEXT_DIR", "exec_path" and "text_path" in the file to point to the corresponding location of the Ansys installation file on this computer, and save the file. Step 1.2: Locate the config.pro file in the Creo Parametric installation directory, open it with Notepad, and type PROTKDAT D:\ANSYS2025\ANSYS Inc\v252\aisol\CADIntegration\ProE\ProEPages\config\WBPlugInPE.dat at the end of the file. The path after PROTKDAT should point to the WBPlugInPE.dat file in the Ansys installation directory. Step 1.3: Open a blank Creo Parametric and check if the "Ansys" tab exists to the right of the "Home" tab. If it exists, it means that the data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys has been successfully established. If it does not exist, check and update the settings in steps 1.1 and 1.2, and re-execute steps 1.1 to 1.2 until the "Ansys" tab exists.

[0009] Furthermore, the process of step 2 is as follows: Step 2.1: Create a new blank part. In the "Tools" tab, set the parameter variables in "[] ​​Parameters", including variable name, type and value. The variable name is prefixed with "DS_". Step 2.2: Draw the geometric model of the product and associate the dimensions that need to be parameterized with the set parameter variables; Step 2.3: In the "Preparation" sub-tab under the "File" tab, click "Settings" in the "Model Properties" settings box and check "Interpret dimensions" and "Convert absolute precision values ​​when changing model units" in the pop-up tab. Return to the Model Properties tab and modify the precision to the minimum precision. Step 2.4: Save the current model file and keep the current model file in the "enabled" state in Creo Parametric.

[0010] Furthermore, step 3 is as follows: Step 3.1: Import the parametric model into the model design module DesignModeler, SpaceClaim, or Discovery, and perform geometric operations, including inspection, repair, dicing, Boolean operations, shared topology, and naming selection, to obtain a geometrically preprocessed parametric model. Step 3.2: Import the parametric model of the geometric preprocessing into the mesh generation module Fluent meshing or Meshing, perform mesh generation to obtain the processed geometric structure, and perform mesh processing operations on it, including setting the mesh size, local refinement, setting periodic boundaries and adding flow boundary layers to obtain the processed network; Step 3.3: Import the processed network into Fluent and set up the case, including selecting the physical model, setting boundary conditions, input parameters, output parameters and report files to obtain the Fluent case. Step 3.4: Import the Fluent example into CFD-Post, perform result processing including setting variable cloud plots, and obtain visualization results.

[0011] Furthermore, the geometric operations performed in step 3.1 include inspection, repair, dicing, Boolean operations, shared topology, and naming selection.

[0012] Furthermore, the mesh processing operation in step 3.2 includes setting the mesh size, local densification, setting periodic boundaries, and adding a flow boundary layer.

[0013] Furthermore, step 4 is as follows: Step 4.1: Based on the visualization results, formulate design requirements and select CentralComposite, Box-Behnken, Optimal, or Definitive Screen from Design-Expert as the specific experimental design method; Step 4.2: Set the number of influencing factors, and set the name, unit, and upper and lower limits for each influencing factor; Step 4.3: Set the number of targets and set the name and unit for each target; Step 4.4: Generate the initial sample space.

[0014] Furthermore, step 5 is as follows: Step 5.1: Import the sample space into the Ansys parameter set; Step 5.2: Update all design points. For each design point's parameter combination, Creo Parametric and Ansys sequentially perform model building, mesh generation, case setting, and result processing, and then return the calculation results to the parameter set to obtain the initial sample space target value.

[0015] Furthermore, step 6 is as follows: Step 6.1: Import the initial sample space target values ​​into Design-Expert; Step 6.2: Select the transformation method with the prediction accuracy closest to 1 from No Transform, Square Root, Nature Log, Bas 10 Log, Inverse Square Root, Inverse, Power, Logit, and Arcsine Square Root, and perform linear regression fitting on the functional relationship between each target and all influencing factors. Step 6.3: From the Modified, Design Model, Mean, Linear, 2FI, Quadratic, Cubic, Quartic, Fifth, and Sixth models, select the objective function model with the prediction accuracy closest to 1 to describe the relationship between each objective and all influencing factors. Step 6.4: Calculate the coefficients in the objective function model to obtain the objective function; Step 6.5: Perform ANOVA analysis on the objective function model to evaluate the model's fitting accuracy; Step 6.6: Based on the ANOVA variance analysis results, analyze the interaction between influencing factors and the target.

[0016] Furthermore, step 7 is as follows: Step 7.1: Set upper and lower limits, targets, upper and lower weights, and importance levels for each influencing factor and target; Step 7.2: Using the hill-climbing algorithm as a multi-objective optimization algorithm, solve the objective function to obtain the multi-objective optimization results under the limited objectives.

[0017] The present invention has the following beneficial technical effects: The fully automated simulation platform for multi-objective optimization established in this invention can automatically complete subsequent parametric simulation and multi-objective optimization after a single model partitioning and case design. This is attributed to: the professional model design software CreoParametric ensuring adaptability to complex models; the professional performance prediction simulation software Ansys guaranteeing the accuracy of calculations; Design-Expert providing directional guidance for multi-objective optimization; and Excel improving the convenience of result retrieval. Therefore, compared with traditional simulation methods, this invention can effectively reduce the large number of repetitive operations that need to be performed manually during the establishment of geometric models, mesh generation, case setting, and result processing under different structural parameters, including settings, file import / export, and data collection and organization. This significantly saves simulation time and labor costs, and has the advantages of strong applicability and high convenience. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention; Figure 2 A flowchart illustrating the data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys for this invention. Figure 3 This is a flowchart illustrating the parametric model creation process in Creo Parametric according to the present invention. Figure 4 This is a flowchart showing the complete simulation process setup for this invention in Ansys, including model import, mesh generation, case setup, and result processing. Figure 5 This is a flowchart illustrating the generation of the initial sample space for DOE experimental design in Desigin-Expert according to the present invention; Figure 6 This is a flowchart illustrating how the present invention imports the sample space into the parameter set of Ansys for parametric simulation to obtain the corresponding target; Figure 7 This is a flowchart illustrating how the present invention fits the objective function between multiple factors and multiple objectives in Design-Expert; Figure 8 This is a flowchart illustrating the process of obtaining optimization results through multi-objective optimization based on a multi-objective optimization algorithm and weight settings, as described in this invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0020] See Figure 1 A fully automated, process-oriented simulation method for multi-factor influence and multi-objective optimization in product design includes the following steps: Step 1, as follows Figure 2 As shown, the data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys is established through the following process: Step 1.1: Locate the WBPlugInPE.dat file in the Ansys installation directory (\ANSYS Inc\v252\aisol\CADIntegration\ProE\ProEPages\config), open it with Notepad, modify the paths after "EXEC_FILE", "TEXT_DIR", "exec_path" and "text_path" to point to the corresponding locations of the Ansys files installed on this computer, and save the file. Here, v252 is the Ansys installation version. Step 1.2: Locate the config.pro file in the Creo Parametric installation directory (\Creo 11.0.0.0\Common Files\text), open it with Notepad, and type PROTKDAT D:\ANSYS2025\ANSYS Inc\v252\aisol\CADIntegration\ProE\ProEPages\config\WBPlugInPE.dat at the end of the file. The path after PROTKDAT should point to the WBPlugInPE.dat file in the Ansys installation directory. Step 1.3: Open a blank Creo Parametric and check if the "Ansys" tab exists to the right of the "Home" tab. If it exists, it means that the data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys has been successfully established. If it does not exist, check and update the settings in Step 1.1 and Step 1.2, and re-execute Step 1.1~1.2 until the "Ansys" tab exists. Step 2, see Figure 3 The process of building a parametric model in Creo Parametric is as follows: Step 2.1: Create a new blank part. In the "Tools" tab, set the parameter variables in "[] ​​Parameters", including variable name, type and value. The variable name must be prefixed with "DS_" so that Ansys can recognize it correctly. Step 2.2: Draw the geometric model of the product, and associate the dimensions that need to be parameterized, including sketching, extruding bosses, and sweeping, with the set parameter variables; Step 2.3: In the "Prepare" sub-tab under the "File" tab, set the unit to millimeters kilograms seconds (mmKs) in the "Model Properties" section. Click "Set" and in the pop-up tab, check "Interpret dimensions (e.g., 1 becomes 1mm)" and "Convert absolute precision values ​​when changing model units". Return to the Model Properties tab and modify the precision to the minimum precision 1e. -4 ; Step 2.4: Save the current model file and keep the current model file in the "enabled" state in Creo Parametric; Step 3, as follows Figure 4 As shown, the complete simulation process of importing the parametric model into Ansys, performing mesh generation, case settings, and result processing, and obtaining the visualization results is as follows: Step 3.1: Import the parametric model into the model design module DesignModeler, SpaceClaim, or Discovery, and perform geometric operations, including inspection, repair, dicing, Boolean operations, shared topology, and naming selection, to obtain a geometrically preprocessed parametric model. Step 3.2: Import the parametric model of the geometric preprocessing into the mesh generation module Fluent meshing or Meshing, perform mesh generation to obtain the processed geometric structure, and perform mesh processing operations on it, including setting the mesh size, local refinement, setting periodic boundaries and adding flow boundary layers to obtain the processed network; Step 3.3: Import the processed network into Fluent and set up the case, including selecting the physical model, setting boundary conditions, input parameters, output parameters and report files to obtain the Fluent case. Step 3.4: Import the Fluent example into CFD-Post and process the results, mainly including setting up variable contour plots to obtain visualization results; Step 4, as follows Figure 5 As shown, based on the visualization results, DOE experiments were designed in Designin-Expert to generate the initial sample space. The process is as follows: Step 4.1: Based on the visualization results, formulate design requirements and select CentralComposite, Box-Behnken, Optimal (Custom), or Definitive Screen (DSD) from Design-Expert as the specific experimental design method; Step 4.2: Set the number of influencing factors, and set the name, unit, and upper and lower limits for each influencing factor; Step 4.3: Set the number of targets and set the name and unit for each target; Step 4.4: Generate the initial sample space; Step 5, as follows Figure 6 As shown, the initial sample space is imported into the Ansys parameter set, and parametric simulation is performed to obtain the target values ​​of the initial sample space. The process is as follows: Step 5.1: Import the sample space into the Ansys parameter set; Step 5.2: Update all design points. For each design point's parameter combination, Creo Parametric and Ansys sequentially perform model building, mesh generation, case setting, and result processing, and then return the calculation results to the parameter set to obtain the initial sample space target value. Step 6, as follows Figure 7 As shown, the initial sample space target values ​​are imported into Design-Expert, and the objective function between multiple factors and multiple objectives is fitted in Design-Expert. The process is as follows: Step 6.1: Import the initial sample space target values ​​into Design-Expert; Step 6.2: Select the transformation method with the prediction accuracy closest to 1 from No Transform, Square Root, Nature Log, Bas 10 Log, Inverse Square Root, Inverse, Power, Logit, and Arcsine Square Root, and perform linear regression fitting on the functional relationship between each target and all influencing factors. Step 6.3: From the Modified, Design Model, Mean, Linear, 2FI, Quadratic, Cubic, Quartic, Fifth, and Sixth models, select the objective function model with the prediction accuracy closest to 1 to describe the relationship between each objective and all influencing factors. Step 6.4: Calculate the coefficients in the objective function model to obtain the objective function; Step 6.5: Perform ANOVA analysis on the objective function model to evaluate the model's fitting accuracy; Step 6.6: Based on the ANOVA variance analysis results, analyze the interaction relationship between influencing factors and the target; Step 7, as follows Figure 8 As shown, based on the multi-objective optimization algorithm and weight settings, multi-objective optimization is performed to obtain the multi-objective optimization results. The process is as follows: Step 7.1: Set upper and lower limits, targets, upper and lower weights, and importance levels for each influencing factor and target; Step 7.2: Using the hill-climbing algorithm as a multi-objective optimization algorithm, solve the objective function to obtain the multi-objective optimization results under the limited objectives; Step 8: Return the multi-objective optimization results to Ansys for simulation verification. The process is as follows: Step 8.1: Import the multi-objective optimization results into the Ansys parameter set; Step 8.2: Update the selected design point and calculate the simulation target value of the multi-objective optimization result under the combination of influencing factor parameters; Step 8.3: Compare whether the difference between the Ansys simulation value and the Design-Expert prediction value of the multi-objective optimization results is lower than the error limit. If yes, proceed to step 8.4. If no, increase the number of Ansys simulation samples, return to step 6, and proceed to steps 7 to 8 in sequence. Step 8.4: Determine whether the multi-objective optimization results meet the initially set optimization objective values: 1) If the multi-objective optimization results have been verified by simulation in Ansys to meet the initially set optimization target value, then the entire multi-objective optimization design calculation is completed. Save the parameter set and simulation results of the entire multi-objective optimization design to Excel and end the entire simulation calculation. 2) If the multi-objective optimization results do not meet the initially set optimization objective values ​​after simulation verification in Ansys, determine whether the sample space has been completely explored: 2.1) If the sample space is not fully explored, expand the sample space, return to step 5, import the expanded sample space into the parameter set of Ansys for parametric simulation to obtain the corresponding target, and execute steps 6 to 8 in sequence. 2.2) If the sample space has been fully explored, it means that the product under the current topology cannot achieve the initially set optimization target value. Based on the simulation results, the multiphysics diagram is manually analyzed to find the structure that can be improved and the topology is improved. Then, return to step 2 and execute steps 3 to 8 in sequence.

Claims

1. A fully automated, process-oriented simulation method for multi-factor influence and multi-objective optimization in product design, characterized in that, Includes the following steps: Step 1: Establish data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys; Step 2: Build a parametric model in Creo Parametric; Step 3: Import the parametric model into Ansys, perform the complete simulation process of mesh generation, case setting, and result processing, and obtain the visualization results; Step 4: Based on the visualization results, design DOE experiments in Designin-Expert to generate the initial sample space; Step 5: Import the initial sample space into the Ansys parameter set, perform parametric simulation, and obtain the target values ​​of the initial sample space; Step 6: Import the initial sample space target values ​​into Design-Expert, and fit the objective function between multiple factors and multiple objectives in Design-Expert; Step 7: Based on the multi-objective optimization algorithm and weight settings, perform multi-objective optimization to obtain the multi-objective optimization results; Step 8: Return the multi-objective optimization results to Ansys for simulation verification. The process is as follows: Step 8.1: Import the multi-objective optimization results into the Ansys parameter set; Step 8.2: Update the selected design point and calculate the simulation target value of the multi-objective optimization result under the combination of influencing factor parameters; Step 8.3: Compare whether the difference between the Ansys simulation value and the Design-Expert prediction value of the multi-objective optimization results is lower than the error limit. If yes, proceed to step 8.

4. If no, increase the number of Ansys simulation samples and return to step 6. Step 8.4: Determine whether the multi-objective optimization results meet the initially set optimization objective values: If yes, save the parameter set and simulation results in the multi-objective optimization design to Excel and end the entire simulation calculation; if no, determine whether the sample space has been completely explored. If the sample space is not fully explored, expand the sample space, return to step 5, import the expanded sample space into the Ansys parameter set for parametric simulation to obtain the corresponding target. If the sample space has been fully explored, the multiphysics diagram is manually analyzed based on the simulation results to identify structures that can be improved, and the topology is improved. Then, the process returns to step 2.

2. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 1 is as follows: Step 1.1: Locate the WBPlugInPE.dat file in the Ansys installation directory, open it with Notepad, modify the paths after "EXEC_FILE", "TEXT_DIR", "exec_path" and "text_path" in the file to point to the corresponding locations of the Ansys files installed on this computer, and save the file. Step 1.2: Locate the config.pro file in the Creo Parametric installation directory, open it with Notepad, and type PROTKDAT D:\ANSYS2025\ANSYS Inc\v252\aisol\CADIntegration\ProE\ProEPages\config\WBPlugInPE.dat at the end of the file. The path after PROTKDAT should point to the WBPlugInPE.dat file in the Ansys installation directory. Step 1.3: Open a blank Creo Parametric and check if the "Ansys" tab exists to the right of the "Home" tab. If it exists, it means that the data interaction between the model design software Creo Parametric and the performance prediction simulation software Ansys has been successfully established. If it does not exist, check and update the settings in steps 1.1 and 1.2, and re-execute steps 1.1 to 1.2 until the "Ansys" tab exists.

3. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 2 is as follows: Step 2.1: Create a new blank part. In the "Tools" tab, set the parameter variables in "[] ​​Parameters", including variable name, type and value. The variable name is prefixed with "DS_". Step 2.2: Draw the geometric model of the product and associate the dimensions that need to be parameterized with the set parameter variables; Step 2.3: In the "File" tab, under the "Prepare" sub-tab, in the "Model Properties" settings sheet, click "Settings" and in the pop-up tab, check "Interpret Dimensions" and "Convert Absolute Precision Values ​​When Changing Model Units". Return to the Model Properties tab and modify the precision to the minimum precision. Step 2.4: Save the current model file and keep the current model file in the "enabled" state in Creo Parametric.

4. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 3 is as follows: Step 3.1: Import the parametric model into the model design module DesignModeler, SpaceClaim, or Discovery, and perform geometric operations, including inspection, repair, dicing, Boolean operations, shared topology, and naming selection, to obtain a geometrically preprocessed parametric model. Step 3.2: Import the parametric model of the geometric preprocessing into the mesh generation module Fluent meshing or Meshing, perform mesh generation to obtain the processed geometric structure, and perform mesh processing operations on it, including setting the mesh size, local refinement, setting periodic boundaries and adding flow boundary layers to obtain the processed network; Step 3.3: Import the processed network into Fluent and set up the case, including selecting the physical model, setting boundary conditions, input parameters, output parameters and report files to obtain the Fluent case. Step 3.4: Import the Fluent example into CFD-Post, perform result processing including setting variable cloud plots, and obtain visualization results.

5. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 4, characterized in that, The geometric operations performed in step 3.1 include inspection, repair, dicing, Boolean operations, shared topology, and naming selection.

6. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 4, characterized in that, The mesh processing operations in step 3.2 include setting the mesh size, local densification, setting periodic boundaries, and adding a flow boundary layer.

7. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 4 is as follows: Step 4.1: Based on the visualization results, formulate design requirements and select CentralComposite, Box-Behnken, Optimal, or Definitive Screen from Design-Expert as the specific experimental design method; Step 4.2: Set the number of influencing factors, and set the name, unit, and upper and lower limits for each influencing factor; Step 4.3: Set the number of targets and set the name and unit for each target; Step 4.4: Generate the initial sample space.

8. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 5 is as follows: Step 5.1: Import the sample space into the Ansys parameter set; Step 5.2: Update all design points. For each design point's parameter combination, Creo Parametric and Ansys sequentially perform model building, mesh generation, case setting, and result processing, and then return the calculation results to the parameter set to obtain the initial sample space target value.

9. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 6 is as follows: Step 6.1: Import the initial sample space target values ​​into Design-Expert; Step 6.2: Select the transformation method with the prediction accuracy closest to 1 from No Transform, Square Root, Nature Log, Bas 10 Log, Inverse SquareRoot, Inverse, Power, Logit, and Arcsine Square Root, and perform linear regression fitting on the functional relationship between each target and all influencing factors; Step 6.3: From the Modified, Design Model, Mean, Linear, 2FI, Quadratic, Cubic, Quartic, Fifth, and Sixth models, select the objective function model with the prediction accuracy closest to 1 to describe the relationship between each objective and all influencing factors. Step 6.4: Calculate the coefficients in the objective function model to obtain the objective function; Step 6.5: Perform ANOVA analysis on the objective function model to evaluate the model's fitting accuracy; Step 6.6: Based on the ANOVA variance analysis results, analyze the interaction between influencing factors and the target.

10. The fully automated process simulation method for multi-factor influence and multi-objective optimization in product design according to claim 1, characterized in that, The process of step 7 is as follows: Step 7.1: Set upper and lower limits, targets, upper and lower weights, and importance levels for each influencing factor and target; Step 7.2: Using the hill-climbing algorithm as a multi-objective optimization algorithm, solve the objective function to obtain the multi-objective optimization results under the limited objectives.

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