Three-dimensional simulation design method and system for precision part model

Abstract

The invention relates to the technical field of computer science, in particular to a three-dimensional simulation design method and system for a precision part model. The method comprises the following steps: acquiring design demand data of the precision part, and constructing a three-dimensional geometric model based on the design demand data of the precision part to obtain three-dimensional geometric model data of the precision part; obtaining material characteristic parameters of the precision part, and constructing a material characteristic model by utilizing the material characteristic parameters of the precision part to obtain material characteristic model data of the precision part; and constructing a multi-physics field coupling model by utilizing the three-dimensional geometric model data of the precision part and the material characteristic model data of the precision part to obtain the multi-physics field coupling model of the precision part. According to the method, the accuracy and reliability of precise part design are improved through precise three-dimensional geometric model construction, multi-physics field coupling analysis, optimization design adjustment, virtual test and model optimization and simulation report generation, and the actual production risk is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer science and technology, and in particular to a three-dimensional simulation design method and system for a precision part model. Background Art

[0002] Traditional precision parts design methods usually fail to fully consider the performance of parts under a variety of complex working conditions. In real-world use environments, precision parts are not only affected by a single physical force field, but are often faced with the coupling of multiple physical fields, which leads to a gap between design and actual performance. Relying on traditional design methods, it is often difficult to fully predict the actual performance of parts under complex working conditions, and even some potential design defects may be missed, resulting in insufficient reliability and safety of the final product. Traditional design and verification methods usually rely on a large amount of prototyping and physical testing, which is not only time-consuming and labor-intensive, but also costly. In the traditional design process, multiple iterations of physical tests are usually required to verify whether the design meets the expected requirements, and each test consumes a lot of resources and time. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a three-dimensional simulation design method and system for precision part models to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional simulation design method for a precision part model comprises the following steps:

[0005] Step S1: obtaining precision part design requirement data, constructing a three-dimensional geometric model based on the precision part design requirement data, and obtaining precision part three-dimensional geometric model data; obtaining precision part material characteristic parameters, constructing a material characteristic model using the precision part material characteristic parameters, and obtaining precision part material characteristic model data;

[0006] Step S2: constructing a multi-physics field coupling model using the three-dimensional geometric model data of the precision parts and the material property model data of the precision parts to obtain a multi-physics field coupling model of the precision parts;

[0007] Step S3: numerically solving the multi-physics field coupling model of the precision part to obtain multi-physics field simulation result data; performing multi-physics field analysis on the multi-physics field simulation result data, and adjusting the precision part design parameters based on the multi-physics field analysis results to obtain the precision part design parameters;

[0008] Step S4: Perform virtual testing on the design parameters of the precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data, optimize the parameters of the multi-physics field coupling model of the precision parts based on the comparative analysis results, and obtain a calibrated multi-physics field coupling model of the precision parts;

[0009] Step S5: performing reliability and safety margin evaluation based on the calibrated multi-physics field coupling model of the precision part, and optimizing the design parameters of the precision part based on the reliability and safety margin evaluation results to obtain optimized design parameters of the precision part;

[0010] Step S6: Use the calibrated precision part multi-physics field coupling model to simulate the optimization design parameters of the precision part and generate a precision part simulation analysis report.

[0011] The present invention lays a solid foundation for subsequent multi-physics coupling analysis by modeling the geometry and material properties of precision parts in detail. On this basis, the multi-physics coupling model is simulated and calculated using a numerical solution method to obtain simulation result data of precision parts under actual working conditions. Compared with physical prototype testing in traditional methods, numerical simulation can more efficiently predict the performance of parts, saving a lot of time and cost required for prototype production and physical testing. Through virtual testing and comparative analysis, the accuracy of the simulation results can be further verified and the design can be optimized. This process can discover problems in the design at an early stage and make adjustments, thereby avoiding duplication of work and multiple iterations in physical testing and reducing resource consumption. By optimizing the design parameters, precision parts can reduce costs and development cycles while ensuring performance. Through reliability and safety margin evaluation, the stability of the design under extreme working conditions is ensured. Through multiple simulation analysis and calibration, the robustness of the part design can be effectively improved to ensure its efficient operation in practical applications. By generating a precision part simulation analysis report, detailed data support is provided, which provides an important basis for product development, production and later maintenance.

[0012] Preferably, the present invention further provides a three-dimensional simulation design system for a precision part model, which is used to execute the above-mentioned three-dimensional simulation design method for a precision part model, and the three-dimensional simulation design system for a precision part model comprises:

[0013] The precision part model creation module is used to construct a three-dimensional geometric model based on the precision part design requirement data to obtain the three-dimensional geometric model data of the precision part; obtain the material characteristic parameters of the precision part, and use the material characteristic parameters of the precision part to construct the material characteristic model to obtain the material characteristic model data of the precision part;

[0014] The multi-physics field coupling module is used to construct a multi-physics field coupling model using the three-dimensional geometric model data of precision parts and the material characteristic model data of precision parts to obtain a multi-physics field coupling model of precision parts;

[0015] The simulation analysis and optimization module is used to numerically solve the multi-physics field coupling model of precision parts to obtain multi-physics field simulation result data; perform multi-physics field analysis on the multi-physics field simulation result data, and adjust the precision part design parameters based on the multi-physics field analysis results to obtain the precision part design parameters;

[0016] The virtual verification and calibration module is used to perform virtual testing on the design parameters of precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data. Based on the comparative analysis results, the multi-physics field coupling model of precision parts is optimized to obtain a calibrated multi-physics field coupling model of precision parts.

[0017] Reliability and safety assessment module, which is used to assess the reliability and safety margin based on the multi-physics field coupling model of calibrated precision parts, and optimize the design parameters of precision parts based on the reliability and safety margin assessment results to obtain the optimized design parameters of precision parts;

[0018] The simulation report generation module is used to simulate the optimization design parameters of precision parts by using the calibrated precision part multi-physics field coupling model to generate a precision part simulation analysis report.

[0019] The present invention can accurately express the shape and size of precision parts and ensure the accuracy of design data by constructing a three-dimensional geometric model based on precision parts design demand data. By acquiring and utilizing the material characteristic parameters of precision parts, a material characteristic model is constructed, so that the physical and mechanical properties of the material are accurately simulated, thereby providing accurate basic data for subsequent simulation analysis. This module lays a solid foundation for subsequent multi-physics field coupling models and simulation analysis. Using the three-dimensional geometric model data and material characteristic model data of precision parts, a multi-physics field coupling model is constructed to achieve interaction and coupling between different physical fields. This module can accurately reflect the physical behavior of precision parts under different working conditions, ensure the comprehensiveness and efficiency of simulation results, and help simulate the performance of parts in practical applications. The multi-physics field coupling model is numerically solved to generate simulation result data of precision parts, which can accurately predict the performance of parts under different conditions. By performing multi-physics field analysis on the simulation results, problems in the design can be discovered in time, and then the design parameters of precision parts can be adjusted. This process can effectively improve the design quality of parts and ensure that their performance meets the needs of practical applications. Compare and analyze the virtual test and multi-physics simulation result data to verify the accuracy of the simulation model, and optimize the parameters of the multi-physics coupling model based on the analysis results. This module can improve the prediction accuracy of the model, ensure the reliability of the design, and provide a scientific basis for subsequent optimization work. This module provides a powerful verification method for the design of precision parts, ensuring the correctness and rationality of the design. Based on the calibrated multi-physics coupling model, the reliability and safety margin of precision parts are evaluated. Through the safety margin calculation and reliability analysis under extreme conditions, the risks of precision parts in actual use can be evaluated, and the design parameters can be optimized based on the evaluation results. This module helps ensure that the designed parts have high safety and stability during use. Use the optimized design parameters to simulate the precision parts and generate a detailed simulation analysis report. The report will provide detailed analysis results of the performance and various technical indicators of the precision parts after the optimized design, providing engineers and decision makers with accurate design basis. This module provides a result summary and evaluation tool for the entire design process, allowing the design team to clearly evaluate the effect of design optimization and room for improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0021] Figure 1 A schematic diagram of the steps of the three-dimensional simulation design method for a precision parts model of the present invention;

[0022] Figure 2 for Figure 1Detailed step flow diagram of step S1;

[0023] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0024] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0026] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve this, please refer to Figures 1 to 3 The present invention provides a three-dimensional simulation design method for a precision part model, the method comprising the following steps:

[0028] Step S1: obtaining precision part design requirement data, constructing a three-dimensional geometric model based on the precision part design requirement data, and obtaining precision part three-dimensional geometric model data; obtaining precision part material characteristic parameters, constructing a material characteristic model using the precision part material characteristic parameters, and obtaining precision part material characteristic model data;

[0029] Step S2: constructing a multi-physics field coupling model using the three-dimensional geometric model data of the precision parts and the material property model data of the precision parts to obtain a multi-physics field coupling model of the precision parts;

[0030] Step S3: numerically solving the multi-physics field coupling model of the precision part to obtain multi-physics field simulation result data; performing multi-physics field analysis on the multi-physics field simulation result data, and adjusting the precision part design parameters based on the multi-physics field analysis results to obtain the precision part design parameters;

[0031] Step S4: Perform virtual testing on the design parameters of the precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data, optimize the parameters of the multi-physics field coupling model of the precision parts based on the comparative analysis results, and obtain a calibrated multi-physics field coupling model of the precision parts;

[0032] Step S5: performing reliability and safety margin evaluation based on the calibrated precision part multi-physics field coupling model, and optimizing the precision part design parameters based on the reliability and safety margin evaluation results to obtain the precision part optimized design parameters;

[0033] Step S6: Use the calibrated precision part multi-physics field coupling model to simulate the optimization design parameters of the precision part and generate a precision part simulation analysis report.

[0034] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a three-dimensional simulation design method for a precision part model of the present invention. In this example, the three-dimensional simulation design method for a precision part model includes the following steps:

[0035] Step S1: obtaining precision part design requirement data, constructing a three-dimensional geometric model based on the precision part design requirement data, and obtaining precision part three-dimensional geometric model data; obtaining precision part material characteristic parameters, constructing a material characteristic model using the precision part material characteristic parameters, and obtaining precision part material characteristic model data;

[0036] The embodiment of the present invention obtains the design requirement data of precision parts, specifically including the detailed collection of geometric dimensions, structural characteristics and functional requirements in the design requirements through high-precision data acquisition equipment, and the structured processing of the requirement data in combination with computer-aided design (CAD) tools to ensure the accuracy and completeness of the data. Based on the acquired design requirement data, the CAD tool is used to construct a three-dimensional geometric model according to the geometric features of the precision parts, and the three-dimensional geometric model is gradually built by accurately drawing the physical structure of each geometric feature of the part, and the corresponding three-dimensional geometric model data of the precision parts is generated. The material characteristic parameters of the precision parts are obtained, and the parameters of the materials applicable to the precision parts are measured or consulted through professional material testing equipment. The data acquisition tool is used to uniformly record and digitize them, and the finite element analysis (FEA) tool is used to build a material characteristic model. The acquired material characteristic parameters are input into the model for data integration to form complete precision part material characteristic model data.

[0037] Step S2: constructing a multi-physics field coupling model using the three-dimensional geometric model data of the precision parts and the material property model data of the precision parts to obtain a multi-physics field coupling model of the precision parts;

[0038] The embodiment of the present invention imports the three-dimensional geometric model data into a multi-physics simulation analysis platform, such as COMSOL Multiphysics or ANSYS Multiphysics, through a data interface. After the import is completed, the material property model data is further introduced to accurately associate the material property parameters with each part of the geometric model to ensure the reasonable distribution of the material properties in the three-dimensional model. In the multi-physics simulation analysis platform, the multi-physics coupling relationship to be considered is set according to the actual use scenario of the precision parts, such as thermal-mechanical coupling, thermal-electric coupling or fluid-solid coupling, which is specifically determined according to the functional requirements of the precision parts. By defining boundary conditions and initial conditions in the simulation platform, such as external ambient temperature, applied mechanical load, electromagnetic field strength or fluid pressure, the multi-physics action conditions are fully described. After the condition setting is completed, the finite element meshing of the three-dimensional geometric model is performed using the automatic meshing tool built into the platform to ensure that all parts of the model can participate in the coupling calculation, and at the same time ensure that the mesh quality meets the accuracy requirements of the simulation analysis. After meshing, the solver module in the platform is called to complete the construction of the multi-physics coupling model to obtain a multi-physics coupling model of precision parts.

[0039] Step S3: numerically solving the multi-physics field coupling model of the precision part to obtain multi-physics field simulation result data; performing multi-physics field analysis on the multi-physics field simulation result data, and adjusting the precision part design parameters based on the multi-physics field analysis results to obtain the precision part design parameters;

[0040] The embodiment of the present invention numerically solves the constructed multi-physics coupling model of precision parts, imports the model data into the multi-physics numerical analysis platform, for example, using the finite element analysis tool COMSOL Multiphysics or ANSYS Multiphysics, and performs calculations by calling the built-in high-precision solver of the platform. In the solution process, according to the preset multi-physics action conditions, including but not limited to applied loads, temperature distribution, electromagnetic field strength and fluid pressure, the variables in the coupling model are iteratively solved to obtain a stable numerical solution. The solution process needs to monitor the convergence in real time to ensure the accuracy of the calculation results. If it is found that the convergence conditions are not met, it is necessary to adjust the grid division or improve the boundary conditions and then re-solve. After completing the numerical solution, the system generates multi-physics simulation result data and stores the results in the form of multi-dimensional data. Subsequently, based on the generated simulation result data, multi-physics analysis is performed through a numerical analysis tool, and a data visualization module is used to generate an intuitive field distribution diagram, a parameter trend diagram and a numerical refinement table of a specific area, and a detailed interpretation of the physical laws and parameter change laws reflected in the simulation results. Combined with the analysis results, the parameters related to the precision part design are extracted, and based on the numerical range of the parameters, the change trend and the deviation from the design target, the precision part design parameters are adjusted item by item to ensure that the design parameters meet the actual functional and performance requirements, and the adjusted precision part design parameters are generated at the same time.

[0041] Step S4: Perform virtual testing on the design parameters of the precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data, optimize the parameters of the multi-physics field coupling model of the precision parts based on the comparative analysis results, and obtain a calibrated multi-physics field coupling model of the precision parts;

[0042] The embodiment of the present invention performs virtual testing based on the adjusted design parameter data of precision parts. The specific operation includes importing the design parameter data into a virtual testing platform, such as HyperWorks or ANSYS Workbench, and performing loading simulation and performance verification through the built-in simulation module of the platform. During the virtual testing process, test conditions that meet the use scenarios of precision parts are set, such as load effects, temperature changes, electromagnetic field interference or fluid dynamic conditions under actual working conditions, to ensure that the test environment is consistent with the actual conditions. After the virtual test is completed, the test result data is extracted, and a test report is generated through a data processing tool, and the report includes a stress distribution diagram, a displacement field, a thermal field change, an electromagnetic field distribution or a flow field characteristic. The virtual test results are compared and analyzed item by item with the multi-physics field simulation result data generated previously, and statistical analysis tools are used to compare the numerical range, distribution law and peak value difference of physical quantities, focusing on identifying areas and parameters with large differences. Based on the comparative analysis results, a parameter optimization tool, such as a design optimization module or a script program, is used to automatically adjust the relevant parameters of the multi-physics field coupling model, recalculate the optimal value range of each parameter, ensure that the model parameters are highly matched with the test results, and finally form the calibration precision part multi-physics field coupling model data.

[0043] Step S5: performing reliability and safety margin evaluation based on the calibrated precision part multi-physics field coupling model, and optimizing the precision part design parameters based on the reliability and safety margin evaluation results to obtain the precision part optimized design parameters;

[0044] The embodiment of the present invention imports the calibrated multi-physics coupling model into a reliability analysis platform, such as HyperWorks Reliability Analysis or ANSYS Reliability Engineering module, to perform multi-condition load analysis and failure probability calculation on the model. Set the working conditions for reliability analysis, such as extreme temperature variation range, maximum external load, long-term stress accumulation or environmental electromagnetic interference, and generate multiple sets of loading conditions through random variable distribution method. Use Monte Carlo simulation method or Latin hypercube sampling technology to simulate the multi-physics coupling model multiple times to calculate the failure probability and reliability index of precision parts under different working conditions. Generate reliability distribution diagram and safety margin assessment data, analyze the stress over-limit risk, fatigue life and material damage probability of key areas in the model, and identify design weaknesses. After completing the reliability assessment, the assessment results are correlated with the current design parameters, and design optimization tools, such as design exploration module or optimization algorithm library, are used to gradually adjust the design parameters to improve the reliability index. Through multiple rounds of iterative optimization, the final precision parts optimization design parameters are determined.

[0045] Step S6: Use the calibrated precision part multi-physics field coupling model to simulate the optimization design parameters of the precision part and generate a precision part simulation analysis report.

[0046] The embodiment of the present invention imports the calibrated multi-physics coupling model and optimized design parameters into a simulation analysis platform, such as COMSOL Multiphysics, ANSYS Workbench or Abaqus, to ensure that all parameters and boundary conditions are accurately configured. During the simulation process, according to the actual working environment of the precision parts, the boundary conditions and load conditions of the multi-physics field interaction, such as mechanical load, thermal load, electromagnetic field distribution or fluid dynamic parameters, are set to ensure that these conditions cover the full life cycle working range of the parts. Using the high-precision numerical solver built into the platform, multiple iterative calculations are performed to gradually generate result data involving different physical fields. Through the data processing module, the multi-physics field results of the simulation calculation are visualized, and charts and reports are generated to fully display the spatial distribution characteristics of the simulation results. After the simulation is completed, the simulation data and visualization charts are comprehensively sorted out, and a precision parts simulation analysis report is written, which describes in detail the calculation results of each physical field and the response law to the design parameters.

[0047] The present invention lays a solid foundation for subsequent multi-physics coupling analysis by modeling the geometry and material properties of precision parts in detail. On this basis, the multi-physics coupling model is simulated and calculated using a numerical solution method to obtain simulation result data of precision parts under actual working conditions. Compared with physical prototype testing in traditional methods, numerical simulation can more efficiently predict the performance of parts, saving a lot of time and cost required for prototype production and physical testing. Through virtual testing and comparative analysis, the accuracy of the simulation results can be further verified and the design can be optimized. This process can discover problems in the design at an early stage and make adjustments, thereby avoiding duplication of work and multiple iterations in physical testing and reducing resource consumption. By optimizing the design parameters, precision parts can reduce costs and development cycles while ensuring performance. Through reliability and safety margin evaluation, the stability of the design under extreme working conditions is ensured. Through multiple simulation analysis and calibration, the robustness of the part design can be effectively improved to ensure its efficient operation in practical applications. By generating a precision part simulation analysis report, detailed data support is provided, which provides an important basis for product development, production and later maintenance.

[0048] Preferably, step S1 comprises the following steps:

[0049] Step S11: obtaining precision part design requirement data, using the precision part design requirement data to perform preliminary geometric structure design, and obtaining preliminary three-dimensional geometric model data;

[0050] Step S12: performing parameter verification on the preliminary three-dimensional geometric model data, and adjusting the preliminary three-dimensional geometric model data based on the parameter verification result to obtain the three-dimensional geometric model data of the precision part;

[0051] Step S13: obtaining material characteristic parameters of precision parts, performing nonlinear characteristic analysis on the material characteristic parameters of precision parts, and obtaining material nonlinear characteristic analysis data;

[0052] Step S14: constructing a material property model using the material nonlinear property analysis data to obtain precision parts material property model data.

[0053] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0054] Step S11: obtaining precision part design requirement data, using the precision part design requirement data to perform preliminary geometric structure design, and obtaining preliminary three-dimensional geometric model data;

[0055] The embodiment of the present invention first collects design requirement data related to precision parts, which should include functional requirements, size requirements, environmental adaptability requirements, load conditions and other specific design constraints of the parts. These data can be obtained through technical documents, engineering requirements tables or design specifications, and need to be sorted and classified using data analysis tools such as Excel or MATLAB to ensure the integrity and availability of the data. After the design requirement data is clear, a three-dimensional modeling tool such as SolidWorks, CATIA or Creo is used to gradually perform preliminary geometric structure design according to the design requirements. The specific operation includes creating a three-dimensional sketch of the part, defining the position and size of key geometric features through sketch constraints, and then using the modeling function to build the preliminary three-dimensional structure of the part. In the modeling process, key parameters and boundary conditions are set strictly according to the design requirements, and the preliminary design is monitored to see whether it meets the predetermined design specifications through the real-time verification function to obtain preliminary three-dimensional geometric model data.

[0056] Step S12: performing parameter verification on the preliminary three-dimensional geometric model data, and adjusting the preliminary three-dimensional geometric model data based on the parameter verification result to obtain the three-dimensional geometric model data of the precision part;

[0057] After the generation of the preliminary three-dimensional geometric model data is completed, the embodiment of the present invention uses a parameter verification tool to verify the key design parameters of the model. The preliminary three-dimensional geometric model data is imported into a verification platform, such as ANSYS, SolidWorks Simulation or HyperMesh, and the geometric analysis module therein is used to check the size of the model to ensure that each parameter meets the requirements in the design requirement data. During the verification process, the key parts of the model are analyzed by setting clear thresholds and boundary conditions, such as whether the aperture of the part meets the assembly requirements, whether the wall thickness meets the strength requirements, and whether the shape dimensions meet the assembly clearance requirements. After completing the geometric verification, the preliminary three-dimensional geometric model data is adjusted based on the verification results. The adjustment process needs to clarify the correction steps and adjustment range, such as changing specific geometric dimensions by modifying the constraints in the sketch, or optimizing the local structure. For adjustments involving multiple parameter linkages, a parametric design tool is used to establish a parameter association relationship, and global synchronous updates are achieved by inputting correction values. During the adjustment process, real-time verification can be combined with the model preview function to ensure that the adjusted geometric data meets the verification requirements. After the adjustment is completed, the three-dimensional geometric model data of the precision part is obtained.

[0058] Step S13: obtaining material characteristic parameters of precision parts, performing nonlinear characteristic analysis on the material characteristic parameters of precision parts, and obtaining material nonlinear characteristic analysis data;

[0059] The embodiment of the present invention obtains material characteristic parameters of precision parts, which include but are not limited to the strength, elastic modulus, plasticity, and hardness of the material. The method of obtaining these parameters can be through experimental measurement or extraction from an existing material database. Subsequently, nonlinear characteristic analysis is performed based on these material characteristic parameters. This analysis focuses on the nonlinear response characteristics of the material under different load conditions, such as the behavior changes of the material under high stress or high temperature environment. The finite element analysis (FEA) numerical simulation method is adopted to predict the mechanical response of the material under complex loading conditions by establishing a physical model. Through this process, the nonlinear characteristic analysis data of the material is obtained.

[0060] Step S14: constructing a material property model using the material nonlinear property analysis data to obtain precision parts material property model data.

[0061] The nonlinear characteristic analysis data of the material in the embodiment of the present invention is used to construct a material characteristic model for precision parts. The core of this process is to convert the nonlinear response data of the material into a mathematical model that can be used for subsequent analysis and design. Use appropriate mathematical methods, such as nonlinear regression analysis or machine learning algorithms, to fit the response data of the material under different loading conditions to form a characteristic model that describes the behavior of the material. When constructing a material characteristic model, the focus is to ensure that the model can accurately reflect the mechanical properties of the material under complex working conditions, especially under extreme loads or special environments. According to the nonlinear characteristics of the material, the constructed model needs to be able to be dynamically updated and adjusted to adapt to different design requirements and working conditions. In the process of constructing the material characteristic model, it is also necessary to combine the microstructural characteristics of the material to further improve the accuracy of the model. Through this process, the material characteristic model data of precision parts is obtained.

[0062] The present invention obtains the design requirement data of precision parts and performs preliminary geometric structure design, thereby ensuring that the basic model in the initial stage of design meets the design goals and specification requirements, laying the foundation for subsequent refined design. Parameter verification of the preliminary three-dimensional geometric model can identify and correct potential problems in the model, ensure that the three-dimensional geometric model of the precision parts meets the accuracy and design requirements, and provide more accurate model data for subsequent analysis. By obtaining the material characteristic parameters of the precision parts and performing nonlinear characteristic analysis, the behavior of the material under various conditions can be understood in detail, ensuring that the material characteristics can be accurately reflected in the model, and enhancing the authenticity and reliability of the simulation results. The material characteristic model is constructed using the material nonlinear characteristic analysis data to ensure that the behavior of the precision part material under a variety of physical field conditions can be scientifically and accurately described, providing high-quality input data for subsequent multi-physical field simulations.

[0063] Preferably, step S2 comprises the following steps:

[0064] Step S21: performing multi-physical field factor analysis based on precision parts design requirement data to obtain multi-physical field analysis requirement data;

[0065] Step S22: constructing a multi-physics control equation according to the multi-physics analysis requirement data, and defining a multi-physics coupling relationship to obtain a multi-physics coupling equation;

[0066] Step S23: Adaptively meshing the three-dimensional geometric model data of the precision part to obtain the three-dimensional mesh model data of the precision part;

[0067] Step S24: Utilizing the precision part three-dimensional mesh model data and the precision part material property model data to configure the multi-physics field coupling model parameters, and obtaining the multi-physics field coupling model;

[0068] Step S25: Perform preliminary simulation calculations on the multi-physics field coupling model and verify the model stability. Based on the stability verification results, optimize the model to obtain a multi-physics field coupling model for precision parts.

[0069] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0070] Step S21: performing multi-physical field factor analysis based on precision parts design requirement data to obtain multi-physical field analysis requirement data;

[0071] The embodiment of the present invention is based on the precision parts design requirement data, and uses numerical analysis tools to comprehensively analyze the multi-physical field factors that affect the performance of precision parts, clarify the multi-physical field effects of precision parts in the use scenarios, and construct a multi-physical field factor classification standard based on the constraints and performance indicators in the design requirement data; adopts a numerical statistical method to perform layered processing on the design requirement data, screens out the correlation between key performance parameters and multi-physical field factors, uses a correlation analysis tool to calculate the interaction intensity and action priority between multi-physical field factors, and organizes the obtained key factor result data into multi-physical field analysis requirement data; combines with three-dimensional design tools to construct a multi-physical field effect distribution map, marks the distribution area, action range and coupling point of each multi-physical field factor, and stores the distribution map data in a standardized format to ensure the clear source of each parameter in the subsequent construction of multi-physical field control equations, thereby achieving the integrity and operability of the multi-physical field analysis requirement data.

[0072] Step S22: constructing a multi-physics control equation according to the multi-physics analysis requirement data, and defining a multi-physics coupling relationship to obtain a multi-physics coupling equation;

[0073] The embodiment of the present invention determines the scope of action and key control parameters of each physical field according to the multi-physical field analysis requirement data, and defines the basic control equations of each physical field item by item using the multi-physical field modeling tool; for the control equation of each physical field, according to the parameter values ​​in the precision part design requirements and the actual action conditions of the physical field, the control variables and boundary conditions are clarified, for example, the change relationship of the temperature field is described by the Fourier heat conduction equation, the stress field distribution is described by the Newtonian mechanics equation, and the electromagnetic field distribution is described by the Maxwell equations; the control equations of each physical field are converted into a discrete expression form by a numerical analysis method, and the time and space variables of the discrete equations are decomposed by the Laplace transform technology to ensure the solution efficiency of the equation group; in combination with the interaction between the multi-physical field factors, the coupling relationship between different physical fields is defined by numerical fitting and coupling coefficient calculation, for example, the thermal expansion effect is calculated by thermal-structural coupling, and the interaction of electromagnetic effects is calculated by electro-magnetic coupling; the defined control equations and coupling relationships are checked for consistency to ensure the consistency and conflict-free nature of the boundary conditions, initial conditions and coupling parameters, and generate multi-physical field coupling equations containing all physical field control equations and their coupling relationships.

[0074] Step S23: Adaptively meshing the three-dimensional geometric model data of the precision part to obtain the three-dimensional mesh model data of the precision part;

[0075] According to the embodiment of the present invention, the three-dimensional geometric model data of the precision part is analyzed according to the multi-physics coupling equation, and the geometric features, key boundaries and their correlation with the multi-physics analysis of the model are determined; the mesh generation tool is used to perform preliminary mesh division, and the geometric model area is divided into a limited number of mesh units, wherein, according to the key areas defined in the multi-physics analysis requirement data, the structured mesh method is preferentially used to ensure the boundary accuracy, and the other areas are unstructured meshes to improve the calculation efficiency; in order to improve the stability and accuracy of the numerical solution, the divided mesh model is quality tested, including the unit shape, mesh density distribution and boundary alignment accuracy, and the mesh quality is verified using mesh quality evaluation indicators such as the Schuyler condition number and the unit shape factor; on the basis of qualified detection, the mesh is adaptively optimized according to the gradient change degree and the key parameter change area involved in the multi-physics control equation, and the local encryption method is used to increase the mesh density in the high gradient area, and the mesh smoothing algorithm is used to adjust the overall mesh layout to ensure the continuity and smoothness between the mesh units; the optimized three-dimensional mesh model data is exported, and a final check is performed to obtain the three-dimensional mesh model data of the precision part.

[0076] Step S24: Utilizing the precision part three-dimensional mesh model data and the precision part material property model data to configure the multi-physics field coupling model parameters, and obtaining the multi-physics field coupling model;

[0077] The embodiment of the present invention uses the three-dimensional grid model data of precision parts and the material property model data of precision parts to define the boundary conditions of the grid model, and determines the physical field variables on each boundary according to the multi-physical field analysis requirement data, such as temperature, stress, electric field strength or fluid pressure, and associates these physical field variables with the corresponding boundary units; uses the material property parameters in the material database to allocate the elastic modulus, thermal conductivity, density and other related parameters in the material property model data to the grid units to ensure the consistency of material properties in space; on this basis, for each coupling item involved in the multi-physical field control equation, the coupling parameters are set in turn, such as the thermal expansion coefficient in the thermal-mechanical coupling or the inductance coefficient in the electro-magnetic coupling, and these parameters are input into the coupling equation configuration module in the multi-physical field simulation platform; performs a preliminary check on all the grids and parameters after configuration, and uses the built-in verification tool of the simulation software to detect whether the boundary conditions, material parameters and coupling relationship definitions are complete to ensure that the input is correct; then performs preliminary calculations, outputs the distribution of each physical field variable, and compares it with the expected range to confirm the rationality of the configuration; finally, obtains the multi-physical field coupling model export.

[0078] Step S25: Perform preliminary simulation calculations on the multi-physics field coupling model and verify the model stability. Based on the stability verification results, optimize the model to obtain a multi-physics field coupling model for precision parts.

[0079] The embodiment of the present invention utilizes a multi-physics coupling model and performs preliminary simulation calculations through a multi-physics simulation platform. During the simulation process, the spatial distribution of each physical field variable is gradually solved strictly according to the multi-physics coupling equation and related boundary conditions; after the preliminary simulation is completed, the output physical field data is compared with the experimental measurement data or the theoretical calculation value, and the deviation range of the result is analyzed; on the basis of the deviation analysis, the coupling parameters in the model are optimized through the parameter adjustment function, such as correcting the thermal expansion coefficient in the thermal-mechanical coupling or the fluid dynamic coefficient in the fluid-structure coupling; after completing the parameter optimization, the optimized coupling model is simulated again, and stability verification indicators such as the convergence curves of the temperature field and the stress field, the stress change rate at the key position or the flow field stability are extracted; according to these indicators, it is judged whether the stability of the model meets the preset standards. If so, the optimized multi-physics coupling model is exported and saved in a standardized format for use in subsequent steps; if the stability standard is not met, the grid division density, the setting of the initial conditions or boundary conditions of the physical field are further adjusted, and the simulation and verification process is repeated until the model meets the stability requirements and the output is completed to obtain a multi-physics coupling model for precision parts.

[0080] The present invention can identify the key physical field factors that affect the performance of parts by performing multi-physics field factor analysis based on the precision parts design requirement data, provide an important basis for the establishment of subsequent simulation models, and ensure the comprehensiveness and accuracy of the analysis. According to the multi-physics field analysis requirement data, the control equation is constructed, and the multi-physics field coupling relationship is defined to ensure that the interaction and relationship between the physical fields in the model are accurately described, so that the simulation can truly reflect the complex engineering actual situation. By adaptively meshing the three-dimensional geometric model data of the precision parts, it is ensured that the mesh refinement degree matches the model complexity, the accuracy and efficiency of the simulation calculation are enhanced, and a reasonable balance is made in the use of computing resources. The parameter configuration of the multi-physics field coupling model is performed in combination with the three-dimensional mesh model data of the precision parts and the material property model data, ensuring that all physical fields and their interactions are accurately reflected in the simulation process, thereby providing high-quality model input for the multi-physics field simulation. By performing preliminary simulation calculations on the multi-physics field coupling model and performing stability verification, potential problems in the model can be found, and the stability and accuracy of the model can be improved through optimization and adjustment, providing a more reliable basis for the final simulation analysis results.

[0081] Preferably, step S3 comprises the following steps:

[0082] Step S31: performing characteristic analysis on the multi-physics field coupling model of precision parts, and screening the solution algorithm based on the characteristic analysis results to obtain the model numerical solution configuration data;

[0083] The embodiment of the present invention utilizes a multi-physics field simulation analysis platform, based on a multi-physics field coupling model of precision parts, selectively loads material characteristic parameters, three-dimensional geometric mesh data and boundary conditions, and performs model characteristic analysis. The characteristic analysis includes the degree of interaction between coupled fields, sensitivity analysis of variable changes, and evaluation of the impact of boundary conditions on solution stability. A variable sensitivity analysis tool is used to extract the change trend data of key variables and generate a variable sensitivity matrix. Based on the characteristic analysis results, the algorithm performance database in the solver module is used to compare and screen according to calculation accuracy, convergence speed and resource occupancy, and numerical solution algorithms that meet the design requirements of precision parts are preferentially selected, such as using the finite element method to solve the stress field distribution, and using the finite volume method to solve the flow field distribution, and adapting the corresponding time integration method and nonlinear solution iteration strategy according to different algorithms. After completing the algorithm screening, specific solver parameters are set, such as the maximum number of iterations, convergence error threshold and time step range, in combination with the complexity of the boundary conditions and the requirements of the variable coupling characteristics, and the model numerical solution configuration data is output through the simulation platform.

[0084] Step S32: numerically solving the multi-physics field coupling model of the precision parts using the model numerical solution configuration data to obtain multi-physics field simulation result data;

[0085] The embodiment of the present invention loads the model numerical solution configuration data through the multi-physics field simulation platform, and verifies the configuration data item by item, including the accuracy of boundary conditions, the integrity of solver parameters and the standardization of numerical formats; after importing the multi-physics field coupling model of precision parts, the numerical solution environment of the model is initialized, and the global parameters related to the time step in the solution domain are set, such as the solution time range and the step interval; the numerical solver is started, and the multi-physics field coupling calculation is performed with a preset solution algorithm. During the process, the physical field variables are iteratively calculated step by step, and the changing trends of key variables are recorded in real time; in order to ensure the stability of the numerical calculation, the residual error of each iteration is monitored in combination with the iterative error monitoring module. The residual value is compared with the convergence threshold. If the residual value is lower than the convergence threshold, the next time iteration is entered. If it exceeds the threshold, the time step is dynamically modified or the initial iteration value is recalculated according to the preset adjustment rules. During the solution process, the physical variable distribution data of key positions, such as the surface temperature distribution of parts, the internal stress concentration area and the fluid pressure field distribution, are recorded through the output monitoring function, and these data are stored as standardized simulation result files. After completing the iterative calculation of all time steps, the post-processing module is used to perform preliminary processing on the simulation results, including extracting multi-physical field variable distribution diagrams, generating curve diagrams and exporting main physical field data files, and finally obtaining multi-physical field simulation result data.

[0086] Step S33: extracting and analyzing the key performance of precision parts from the multi-physics field simulation result data to obtain multi-physics field analysis results;

[0087] The embodiment of the present invention imports the calculated multi-physics field simulation result data through the multi-physics field simulation result data management module, checks the integrity and format consistency of the simulation data, and ensures that the data file contains the distribution information of the key variables of the multi-physics field; uses the simulation post-processing tool to analyze the temperature field distribution, extracts the maximum temperature on the surface and inside of the part, the temperature gradient change range and the dynamic change curve of the temperature over time, and generates the temperature distribution contour map and the hot spot area map; for the stress field data, based on the accurately solved stress tensor distribution, calculates the equivalent stress value, the maximum principal stress value and the stress concentration area of ​​the key parts of the part, and uses the ... and generates the temperature distribution contour map and the hot spot area map. The physical module draws the stress field distribution cloud map and outputs the local stress curve; the flow field data is analyzed in layers to extract the fluid pressure distribution, velocity field distribution and turbulent kinetic energy characteristics, generate the flow field distribution map and mark the velocity vectors at key positions; the temperature field, stress field and flow field results are comprehensively compared and analyzed, and the multi-physics field variable cross-correlation algorithm is used to identify the areas that cause performance degradation or the coupling phenomena with significant interactions; the key performance indicators in the multi-physics field simulation results are extracted through data reduction technology, such as the deformation caused by thermal stress coupling, the change of mechanical properties caused by fluid action and the local fatigue life estimation value, to obtain the multi-physics field analysis results.

[0088] Step S34: Optimize the precision part design scheme according to the multi-physical field analysis results to obtain precision part design parameters.

[0089] The embodiment of the present invention receives multi-physical field analysis result data through a performance analysis module, classifies the data according to thermal performance, mechanical performance and fluid performance, and normalizes each performance data to ensure the comparability of results of different physical fields; combines the thermal performance results to analyze the thermal stress distribution and thermal deformation characteristics of the parts, and selects the optimal design parameters to reduce the deformation amplitude of the thermally sensitive area; uses the mechanical performance results to evaluate the stress distribution and failure risk of key structural components, and locally adjusts the design parameters of high stress concentration areas to optimize the mechanical performance; based on the fluid performance results, adjusts the flow channel structure parameters to reduce fluid pressure fluctuations and improve fluid flow efficiency; During the optimization design process, a multi-objective optimization algorithm is used to set the weights of different performance indicators. For example, thermal stress reduction and flow field uniformity are used as constraints to iteratively optimize the shape design of the stress concentration area. At the same time, the design variable sensitivity analysis technology is used to evaluate the impact of each design parameter on performance changes, and parameters with high sensitivity are given priority in the adjustment plan. The predicted values ​​of key performance indicators are recalculated and generated through the optimized design parameters, and compared with the predicted values ​​of the original design parameters to determine the degree of improvement of the optimized design. Finally, the adjusted design parameters are organized into a design plan document to obtain the design parameters of precision parts.

[0090] The present invention can identify the key characteristics of the model and its solution requirements by performing characteristic analysis on the multi-physics field coupling model of precision parts. Based on the analysis results, appropriate solution algorithms are screened to ensure that the most suitable algorithm is used for numerical solution, improve the calculation efficiency and simulation accuracy, and provide an effective solution configuration for subsequent steps. By simulating the multi-physics field coupling model of precision parts using the optimized numerical solution configuration, high-quality multi-physics field simulation result data can be obtained, which provides an accurate basis for analysis and optimization design and can reflect the performance of parts under actual working conditions. By extracting and analyzing the key performance of precision parts from the multi-physics field simulation result data, the key performance indicators of parts under various working conditions can be identified, providing quantitative data support for subsequent design evaluation and optimization, and ensuring that the focus of the analysis is on the key aspects of part performance. The precision part design scheme is optimized according to the multi-physics field analysis results, and the part performance is improved by adjusting the design parameters to ensure that the parts can meet the design requirements in actual applications, thereby improving the reliability and efficiency of the part design.

[0091] Preferably, step S31 includes the following steps:

[0092] Step S311: Perform static characteristic analysis on the multi-physics field coupling model of precision parts and extract key parameters to obtain preliminary characteristic parameter extraction data;

[0093] The embodiment of the present invention performs static characteristic analysis on the multi-physics field coupling model of precision parts through a characteristic analysis module, loads the model into a static characteristic analysis tool, inputs material property data, boundary condition data and external load condition data in sequence, and calculates the static response characteristics of the model through a finite element analysis method; based on the analysis results, the key characteristic area in the model is selected, and the key characteristic parameters of the model are extracted according to the spatial distribution characteristics of the stress concentration coefficient, the high temperature distribution area and the deformation concentration area; wherein, a partition extraction method is used to gradually process different attribute areas in the multi-physics field, for example, the stress gradient distribution is extracted using the node coordinate data and the element connection relationship, the stress concentration coefficient is calculated in combination with the stress gradient difference inside and outside the area, and the coordinates and size of the stress peak point are extracted; for the thermal distribution characteristics, the temperature gradient and maximum thermal stress data of the thermal distribution are extracted by setting the heat flux density boundary conditions and thermal conductivity parameters; for the deformation characteristics, the total deformation is calculated by the displacement component, and the displacement gradient distribution of the key parts is further analyzed; all the extracted characteristic parameters are classified and stored in the characteristic parameter database to obtain preliminary characteristic parameter extraction data.

[0094] Step S312: performing complexity classification on the multi-physics field coupling model of the precision parts according to the preliminary characteristic parameter extraction data to obtain the complexity classification data of the multi-physics field model;

[0095] The embodiment of the present invention uses preliminary characteristic parameter extraction data to perform complexity classification on the multi-physics field coupling model of precision parts. First, the classification rules for the preliminary characteristic parameters are defined through a classification rule setting module, including but not limited to three types of complexity influencing factors: stress characteristics, thermal characteristics, and deformation characteristics. Then, the preliminary characteristic parameter extraction data is imported into a classification analysis tool, and the data is grouped and processed according to the classification rules. For example, for the stress characteristic group, the order of magnitude of the stress concentration coefficient, the uniformity of the distribution area, and the distribution of the extreme stress points are classified to set three complexity levels: high, medium, and low. For the thermal characteristic group, the temperature gradient is analyzed to obtain the three complexity levels of high, medium, and low. The uniformity of degree distribution, the proportion of high-temperature areas and the numerical difference of extreme thermal stress are graded; for the deformation characteristic group, the maximum deformation, the displacement gradient change rate and the nonlinear characteristics of the deformation distribution in the key area are calculated to make the grade; then, the complexity level of each characteristic group is comprehensively scored according to the weight factor through the complexity summary module. For example, weight factors are set for stress characteristics, thermal characteristics and deformation characteristics respectively, and the comprehensive complexity score is calculated by the linear weighted method; finally, according to the comprehensive complexity score, the multi-physics field coupling model is divided into three categories of high complexity, medium complexity and low complexity to generate the complexity classification data of the multi-physics field model.

[0096] Step S313: Screening the solution algorithm based on the preliminary characteristic parameter extraction data and the multi-physics field model complexity classification data to obtain the solution algorithm candidate solution list data;

[0097] The embodiment of the present invention performs solution algorithm screening based on preliminary characteristic parameter extraction data and multi-physics field model complexity classification data. First, the preliminary characteristic parameter extraction data and complexity classification data are input into the algorithm screening system, and a preset algorithm library is called according to the numerical range of the characteristic parameters and the model complexity level; the algorithm library includes explicit finite element method, implicit finite element method, iterative solution method, direct solution method and other multi-physics field coupling algorithms, among which, for high-complexity models, multi-order iterative algorithms are preferentially screened to improve solution accuracy, for medium-complexity models, implicit algorithms are preferentially screened to balance solution efficiency and accuracy, and for low-complexity models, direct solution methods are preferentially screened to improve calculation speed; in the screening process, according to the key values ​​involved in the preliminary characteristic parameters, the screening results are further optimized through built-in judgment rules, for example, for models with significant nonlinear behavior, nonlinear coupling algorithms are preferentially selected, and for models involving significant changes in temperature gradients, temperature field coupling solution algorithms are preferentially selected; a list of candidate solution algorithm solutions is generated through multiple rounds of screening, and the list is output in a tabular form, including the name, applicable scope, computational efficiency and expected error range of each algorithm.

[0098] Step S314: Perform performance simulation evaluation on the candidate solution list data of the solution algorithm to obtain solution algorithm performance evaluation data;

[0099] When the embodiment of the present invention performs performance simulation evaluation on the list data of candidate solutions for the solution algorithm, it first simulates each solution algorithm using a predefined performance simulation environment, and the evaluation environment is set based on the complexity, boundary conditions and physical properties of the model; each algorithm is run multiple times in the simulation process according to the multi-physics coupling characteristics of precision parts, wherein appropriate solution accuracy and convergence standards are set for different solution algorithms. During each simulation, the calculation time, memory consumption, error range and convergence performance of each algorithm are recorded, focusing on evaluating the algorithm's computational stability when processing large-scale grid models and error transmission under high-precision conditions; the simulation results are compared with the actual numerical solution through an error analysis method to obtain the specific performance data of each algorithm under the multi-physics coupling model, including calculation accuracy, speed, resource consumption and applicable scenarios. Finally, the solution algorithm performance evaluation data is generated based on the performance simulation evaluation data.

[0100] Step S315: configuring model numerical solution parameters based on the solution algorithm performance evaluation data to obtain model numerical solution configuration data.

[0101] The embodiment of the present invention is based on the performance evaluation data of the solution algorithm. First, the calculation accuracy, calculation time and convergence performance of each solution algorithm are quantitatively analyzed, and the complexity and physical characteristics of the multi-physical field coupling model are combined to determine the appropriate numerical solution parameter configuration. On this basis, specific numerical solution accuracy requirements are set for each solution algorithm. In addition, for algorithms with high computing resource consumption, the memory occupancy and parallel computing configuration are adjusted to ensure that the calculation accuracy requirements are met while ensuring the calculation efficiency. According to the physical properties and boundary conditions of the model, the boundary layer processing, flow field decoupling, and nonlinear solution strategy in the numerical solution process are further optimized to ensure that the coupling characteristics of each physical field are accurately reflected. Finally, the configuration data is summarized to form detailed numerical solution parameter configuration data.

[0102] The present invention can deeply understand the behavior and performance of parts under static loads, extract key parameters, and provide basic data support for subsequent optimization and simulation by performing static characteristic analysis on the multi-physics field coupling model of precision parts. The preliminary characteristic parameter extraction data provides detailed model features for selecting a suitable solution algorithm. By grading the complexity of the model according to the preliminary characteristic parameter extraction data, the computational difficulty and accuracy requirements of the multi-physics field model can be clarified, thereby providing hierarchical guidance for subsequent calculation and solution processes. This step helps to improve computational efficiency and reduce unnecessary resource consumption. Based on the preliminary characteristic parameter extraction data and the model complexity classification data, a variety of solution algorithm candidate solutions are screened out to ensure that the best solution algorithm can be selected according to the characteristics of the specific model. This screening step helps to improve the accuracy and speed of the solution and ensure that the simulation results meet the design requirements. Performance simulation evaluation of the solution algorithm candidate solutions can further verify the applicability and performance differences of each solution algorithm, and provide decision support for the final selection of the optimal algorithm. This evaluation process ensures that the selected algorithm has high efficiency and accuracy, and is adapted to a variety of working conditions and model complexity. The model numerical solution parameters are configured according to the performance evaluation data of the solution algorithm to ensure that all parameters in the model solution process are reasonably set, further optimizing the efficiency and accuracy of the simulation calculation and providing high-quality calculation results for subsequent simulation analysis.

[0103] Preferably, step S4 comprises the following steps:

[0104] Step S41: Designing a virtual test plan according to the precision part design parameters and preset usage scenario parameters to obtain a precision part virtual test plan;

[0105] The embodiment of the present invention determines the goal and scope of virtual testing based on the design parameters of the precision parts and the preset usage scenario parameters. The multi-physics field coupling model of the precision parts is used to evaluate its performance under different usage environments to reflect the performance indicators under actual working conditions. In the test plan design process, the boundary conditions, physical field interactions, loading conditions and working time of the virtual test are set to ensure that the plan can accurately reflect the performance of the parts in actual use. During the design process, the calculation parameters between the physical fields in the simulation process are defined in detail, and the test plan is verified to ensure that the coverage and simulation accuracy of the plan meet the requirements. According to the virtual test plan, efficient numerical calculation tools are used to calculate the pre-test data, and finally a virtual test plan for precision parts is formed.

[0106] Step S42: using a precision part virtual test solution to perform a virtual test on the precision part design parameters to obtain design parameter virtual test result data;

[0107] According to the virtual testing scheme for precision parts, the embodiment of the present invention first sets the working conditions of the parts under different preset usage scenarios, and inputs these conditions into the multi-physics field coupling model of the precision parts. When the design parameters are virtually tested, the geometric model of the precision parts and the physical field coupling model are used to perform multi-physics field simulation. Each test sets simulation scenarios under different working environments by determining boundary conditions, initial conditions and physical properties, and performs stability analysis and response calculation. Using numerical simulation technology, the response of the design parameters under specific working conditions is calculated through finite element analysis (FEA) to obtain various performance indicators. By solving the multi-physics field coupling relationship, the virtual test result data of the design parameters is obtained.

[0108] Step S43: Compare and analyze the design parameter virtual test result data with the multi-physics field simulation result data to obtain a test simulation comparison analysis report;

[0109] The embodiment of the present invention conducts a detailed comparative analysis of the virtual test result data of the precision part design parameters and the multi-physics field simulation result data. First, by comparing the key performance indicators of the test results and the simulation results, and comparing the numerical differences between the actual test and the simulation, the behavior of the design parameters in different scenarios is analyzed. During the comparative analysis, special attention is paid to the deviation between the test data and the simulation data under specific boundary conditions. The error analysis method is used to compare the deviations in the data and identify situations where the model assumptions are inaccurate or the parameters are improperly set. The virtual test results of the design parameters are further classified through statistical analysis, and trend graphs and error distribution graphs are drawn in combination with the simulation data to visualize the performance differences in different scenarios and obtain a test simulation comparative analysis report.

[0110] Step S44: Optimize the parameters of the multi-physics field coupling model of precision parts using the test simulation comparison analysis report to obtain a calibrated multi-physics field coupling model of precision parts.

[0111] The embodiment of the present invention calibrates various performance parameters of the multi-physics field coupling model of precision parts according to the test simulation comparison analysis report. According to the error range and deviation indicated in the test simulation comparison analysis report, the deficiencies and inconsistencies in the model are identified, especially in the setting of boundary conditions, physical field coupling relationships, or material properties. Then, the input parameters of the multi-physics field coupling model of precision parts are adjusted, including but not limited to the mechanical properties of the material, the thermal conductivity coefficient, slight changes in the geometric shape, and the external environmental conditions, to ensure that these adjustments are consistent with the experimental data. Further, through an optimization algorithm, such as a genetic algorithm or a least squares method, the parameters are automatically adjusted and the error between the simulation and experimental data is minimized. Finally, the optimized parameter configuration will update the multi-physics field coupling model of precision parts to obtain a calibrated multi-physics field coupling model of precision parts.

[0112] The present invention can ensure that the virtual test process can fully cover the performance of the parts in the actual working environment by designing a virtual test scheme according to the precision part design parameters and the preset usage scenario parameters. This design stage provides a scientific test framework for subsequent testing and simulation analysis to ensure the representativeness and accuracy of the test. The design parameters are virtually tested using the precision part virtual test scheme, and the performance of the design scheme can be evaluated without actually manufacturing the parts. Through this test process, the performance data of the design parameters under different working conditions can be quickly obtained, avoiding the high cost and time consumption caused by physical testing. Comparing and analyzing the design parameter virtual test result data with the multi-physics field simulation result data is helpful to evaluate the consistency between the virtual test and the simulation results, and identify potential design problems or model errors. This comparative analysis stage not only verifies the accuracy of the simulation model, but also provides a data basis for subsequent model optimization. Based on the test simulation comparative analysis report, the multi-physics field coupling model of precision parts is optimized, and the model parameters can be adjusted and calibrated according to the actual test and simulation results to improve the accuracy and reliability of the model. Through this optimization step, it can be ensured that the simulation model is more in line with the actual working conditions and the credibility of the design is improved.

[0113] Preferably, step S43 includes the following steps:

[0114] Step S431: extracting key performance indicators from the design parameter virtual test result data and the multi-physics field simulation result data to obtain a key performance indicator data set;

[0115] The embodiment of the present invention extracts key performance indicators from the virtual test result data of design parameters and the multi-physics field simulation result data. The virtual test result data of design parameters is quantitatively analyzed based on the physical quantities set in the test scheme to extract the performance indicators corresponding to each test data. For the multi-physics field simulation result data, various indicators are calculated respectively according to various physical fields set in the simulation model to obtain simulation data indicators compared with the virtual test data. For each type of indicator, data processing tools, such as statistical analysis methods or regression analysis, are used to further organize and summarize to obtain a key performance indicator data set.

[0116] Step S432: performing difference analysis based on the key performance indicator data set, and quantifying the difference analysis results to obtain indicator difference analysis report data;

[0117] The embodiment of the present invention performs a difference analysis on the virtual test result data of the design parameters and the multi-physics field simulation result data based on the key performance indicator data set. The difference analysis first measures the difference between the virtual test and the simulation results by calculating the relative error of each performance indicator. For example, by calculating the error between the maximum displacement in the virtual test result of the design parameter and the maximum displacement in the multi-physics field simulation result, the difference value of each test and simulation indicator is obtained. During the difference analysis process, if nonlinear data or the interaction of multiple physical fields is encountered, multiple regression analysis or variance analysis method is used to further reveal the influence of different factors on the performance indicators. All difference analysis results are expressed in a quantitative manner, specifically by numerically representing the size of the difference value and its statistical significance. Then, the difference analysis results are quantified to further form indicator difference analysis report data, and finally obtain complete indicator difference analysis report data.

[0118] Step S433: performing adaptability evaluation on the multi-physics field coupling model of precision parts according to the index difference analysis report data to obtain model adaptability evaluation result data;

[0119] According to the index difference analysis report data, the embodiment of the present invention conducts an in-depth evaluation of the difference between the virtual test results of the design parameters and the multi-physics field simulation results, focusing on the mismatched parts in the key performance indicators. The parameters of the multi-physics field coupling model are used to compare the differences in the analysis report to evaluate the adaptability of the model under different operating conditions and physical field coupling. The adaptability evaluation verifies the conformity between the model output and the actual measurement data by analyzing the performance of the model in the actual application scenario. In order to quantitatively evaluate the adaptability of the model, the residual analysis method is used to calculate the residual value between the simulation results and the test data, and the chi-square test or F test is used to verify the adaptability. If the evaluation results show that there is a large incompatibility, further analyze which physical fields or design parameter deviations affect the accuracy of the model, and adjust the model according to the difference analysis report. For example, for the displacement difference in structural analysis, analyze whether the simulation results do not match the virtual test due to inaccurate assumptions of material properties, boundary conditions or loading methods. Through these analyses, the model adaptability evaluation result data is obtained.

[0120] Step S434: Integrate the key performance indicator data set, indicator difference analysis report data and model adaptability evaluation result data to obtain a test simulation comparison analysis report.

[0121] After completing the adaptability assessment of the multi-physics field coupling model of precision parts, the embodiment of the present invention integrates the key performance indicator data set, the indicator difference analysis report data, and the model adaptability assessment result data. Each data item is sorted according to the corresponding indicator category to ensure the consistency of the data and facilitate comparison. Then, the weighted average method is used to synthesize the data. During the synthesis process, different weight values ​​are set according to the importance of each indicator. The weights are set by field experts according to engineering requirements and the accuracy of test simulation results. At this time, the integrated data is statistically processed by statistical analysis methods to clarify which key performance indicators have a significant impact on the adaptability of the model. Finally, a test simulation comparative analysis report is compiled, which will include the quantitative comparison results of each indicator and suggestions for adjustment of each model, especially for the parts that perform poorly in the adaptability assessment, pointing out the reasons and giving optimization directions.

[0122] The present invention can extract key parameters from a large amount of simulation and test data by extracting key performance indicators from the virtual test result data of the design parameters and the multi-physics field simulation result data respectively. These indicators provide an accurate data basis for subsequent analysis and optimization, and help evaluate the actual performance of the design scheme. Based on the extracted key performance indicator data set, difference analysis is performed, and the difference analysis results are quantified, which can clearly identify the difference between virtual testing and simulation simulation, provide an objective basis, reveal the potential errors and deviations of the design or simulation model, and thus provide a direction for further model optimization. According to the indicator difference analysis report, the adaptability evaluation of the multi-physics field coupling model of precision parts is carried out, which helps to evaluate the adaptability and accuracy of the simulation model under different working conditions. Through this evaluation, it can be judged whether the model needs to be adjusted to better meet the design requirements and actual performance, thereby enhancing the reliability of the simulation model. The key performance indicator data set, the indicator difference analysis report data and the model adaptability evaluation result data are integrated to obtain a test simulation comparison analysis report, which helps to fully display the association and difference between the test and simulation data, and provides a comprehensive view to support design decisions and subsequent optimization.

[0123] Preferably, step S5 comprises the following steps:

[0124] Step S51: designing a model reliability evaluation index based on the precision parts design requirement data to obtain model reliability evaluation index data;

[0125] The embodiment of the present invention extracts the main factors affecting the reliability of the model by systematically analyzing the working environment and usage scenarios of the precision parts based on the precision parts design requirement data. On this basis, the model reliability evaluation index is designed, and the index includes but is not limited to the fatigue life, ultimate bearing capacity, and thermal response performance of the parts. Collect all the working condition data that affect the performance of the parts, including but not limited to load conditions, ambient temperature, and operating frequency, and then establish a mathematical model of the relevant parameters through physical experiments or existing materials and engineering data. For each design requirement, a reliability analysis method, such as Monte Carlo simulation or response surface method, is used to conduct a comprehensive reliability analysis to evaluate the performance of the parts under different working conditions. Through the above method, the model reliability evaluation index data is obtained.

[0126] Step S52: using the model reliability evaluation index data to perform reliability analysis on the multi-physics field coupling model of the calibrated precision parts under various working conditions, and obtaining model reliability analysis result data;

[0127] The embodiment of the present invention uses a calibrated multi-physics coupling model of precision parts to carry out reliability analysis under various working conditions based on the model reliability evaluation index data. A plurality of typical working conditions are set in combination with the actual use environment and design requirements of precision parts. These working conditions are substituted into the established multi-physics coupling model through precise numerical calculation and simulation, and a comprehensive analysis of each working condition is carried out in many aspects. The structural response is calculated using the finite element analysis (FEA) method to obtain the distribution of important physical quantities under various working conditions, and compared with the reliability evaluation index data. Through the results under different working conditions, the safety, durability and performance stability of the parts under various usage conditions are evaluated. Finally, the model reliability analysis result data is obtained.

[0128] Step S53: Calculate the safety margin under extreme working conditions based on the model reliability analysis result data to obtain the model safety margin evaluation result data;

[0129] The embodiment of the present invention calculates the safety margin under extreme working conditions based on the model reliability analysis result data. The key reliability indicator data under each working condition is extracted, and the load-bearing capacity of the precision parts under extreme working conditions is set. Extreme working conditions include but are not limited to the worst temperature, load, and pressure, which can simulate the response of the parts under maximum working intensity or external environmental limits. Through the known design and material performance data, combined with the finite element analysis method, the behavior of precision parts under these extreme conditions is accurately simulated and calculated. The calculation results involve fatigue damage of parts under extreme working conditions. By comparing these calculation results with the safety standards of the parts, the safety margin formula is used for calculation to obtain the model safety margin evaluation result data of the parts under extreme working conditions.

[0130] Step S54: optimizing the design parameters of the precision parts according to the model reliability analysis result data and the model safety margin assessment result data to obtain the optimized design parameters of the precision parts.

[0131] The embodiment of the present invention optimizes the design parameters of precision parts according to the model reliability analysis result data and the model safety margin assessment result data. The optimization process involves a comprehensive evaluation of the performance data of the parts under various working conditions to identify potential weak links in the design, such as stress concentration areas, excessive deformation or potential material fatigue problems. Based on these analysis results, combined with the functional requirements and use environment of the precision parts, adjustments are made to the geometry, material selection and structural design of the parts. For example, the key dimensions of the parts are finely adjusted, the wall thickness is optimized, the support area is strengthened or the connection method is improved to reduce the risk of stress concentration. According to the safety margin assessment results, the maximum load-bearing capacity of the parts is reasonably improved to ensure that there is sufficient safety margin under extreme working conditions. This process uses numerical optimization methods, such as gradient descent or genetic algorithms, to automatically adjust the design parameters, and gradually iterates the performance under different working conditions until the optimization goal is reached. Through the above adjustments, new design parameters are generated, and finally the optimized design parameters of the precision parts are obtained.

[0132] The present invention designs a model reliability evaluation index based on precision parts design demand data, which helps to systematically define and quantify the key factors affecting part performance. This design provides a clear direction for subsequent reliability analysis, ensures that the evaluation process can focus on the most important design parameters, and improves the accuracy of the evaluation. The reliability analysis of the multi-physics field coupling model of the calibrated precision parts under various working conditions using the model reliability evaluation index data helps to comprehensively evaluate the performance of the design under different working environments. This analysis can identify potential failure risks and provide data support for the optimization design scheme to ensure that the parts can operate stably under actual working conditions. The safety margin calculation under extreme working conditions based on the model reliability analysis result data helps to evaluate the safety of the parts under extreme conditions. This step ensures that the design parameters can meet the needs of high-intensity application scenarios by considering the extreme working conditions, and enhances the robustness and reliability of the design. According to the model reliability analysis result data and the model safety margin evaluation result data, the design parameters of the precision parts are optimized, and the design can be optimized after fully considering reliability and safety, and the performance and durability of the design can be improved. The optimized design parameters can better meet the actual use requirements, improve product quality and reduce the failure rate.

[0133] Preferably, step S6 comprises the following steps:

[0134] Step S61: setting simulation working conditions and boundary conditions based on the optimization design parameters of the precision parts to obtain simulation working condition data;

[0135] The embodiment of the present invention is based on the optimization design parameters of precision parts, and the simulation working conditions and boundary conditions are set through the selected engineering simulation software (. After the optimization design parameters are input, the specific working conditions of the precision parts in the working environment are defined to ensure that the working condition settings can fully reflect the actual use scenarios. The fixed support points, motion constraints, and contact conditions of the parts are set through the boundary condition setting module provided by the software. For each boundary condition, its numerical range and action position are accurately set to ensure consistency with the design requirements. According to the optimization design parameters, the physical properties related to the parts are set, and the environmental factors of the boundary conditions are adjusted according to the material property model. In this process, the surface and interior of the parts are discretized using numerical grid generation technology to ensure the accuracy of the simulation, and the key parts are refined through grid encryption. After completing the setting of the working conditions and boundary conditions, complete simulation working condition data is generated.

[0136] Step S62: Based on the simulation working condition data, the multi-physics field coupling model of the calibrated precision parts is used to simulate the optimization design parameters of the precision parts to obtain simulation result data;

[0137] The embodiment of the present invention uses the optimized design parameters of precision parts and the calibrated multi-physics coupling model of precision parts for simulation based on the simulation working condition data. This process is carried out through an engineering simulation platform. After inputting the optimized design parameters and the simulation working condition data, the system performs numerical calculations. In the calculation, the geometric model, material property model, boundary conditions and working conditions of the precision parts are combined to perform multi-physics coupling analysis. The coupling model includes the interaction of multiple physical fields and accurately simulates the response of precision parts under different working conditions. By setting the relevant simulation calculation parameters and performing numerical solution, the simulation result data is obtained.

[0138] Step S63: verifying the simulation result data, and performing design target difference analysis on the simulation result data based on the verification result to obtain design optimization suggestion data;

[0139] The embodiment of the present invention is verified based on the comparison between the simulation result data and the actual test data. The verification process matches the simulation model results with the existing experimental data or historical data to confirm whether the simulation accurately reflects the actual performance of the precision parts under different working conditions. If there is a significant difference between the simulation results and the test data, it is necessary to analyze the source of the difference. Subsequently, a design target difference analysis is performed to obtain the main factors affecting the performance of the precision parts by comparing the difference between the actual test data and the design target in the simulation results. For example, if there is a large deviation between some parameters in the simulation results and the target value, it is necessary to further explore whether it is due to unreasonable boundary condition setting, geometric design parameters or material selection. During the difference analysis process, numerical analysis methods, such as error sensitivity analysis or sensitivity analysis, are used to quantitatively evaluate the impact of each design parameter on the performance and obtain optimization suggestions. These optimization suggestions are described in detail for the design parameters that need to be adjusted so as to improve the design to better meet the predetermined performance requirements. Finally, based on the difference analysis results, design optimization suggestion data is formed.

[0140] Step S64: Integrate the simulation result data and the design optimization suggestion data to obtain a precision parts simulation analysis report.

[0141] The embodiment of the present invention comprehensively organizes the simulation result data and the design optimization suggestion data to ensure that all data are complete and the correlation between them is accurately represented. The simulation result data includes the performance of the parts under different working conditions, while the design optimization suggestion data includes the optimization direction and parameter adjustment suggestions obtained based on the design target difference analysis. The simulation result data is classified and processed, and the relevant data is archived by category according to different simulation working conditions, such as sorting by working temperature range, load condition or material type. Then, the simulation data is marked according to the design optimization suggestion to clarify which data represents the shortcomings in the current design and which data corresponds to the design parameters recommended for improvement. The integration process is carried out by establishing a unified data framework or database, and using tools such as tables or professional database management systems to link the simulation result data and the optimization suggestion data to the same work platform. In the process of data integration, special attention is paid to the matching degree between the simulation results and the optimization suggestions to ensure that the final precision parts simulation analysis report can accurately reflect the design changes before and after optimization. Finally, a precision parts simulation analysis report is obtained, and the report content includes a detailed analysis of the simulation results, a feasibility description of the design optimization suggestions, and the expected performance indicators of the adjusted design.

[0142] The present invention can accurately simulate the performance of parts under actual working conditions by setting simulation working conditions and boundary conditions based on the optimization design parameters of precision parts. The set working conditions and boundary conditions ensure that the results of the simulation are highly realistic, provide a reliable basis for subsequent simulation analysis, and ensure the scientificity and pertinence of the optimization of design parameters. In the simulation process, based on the simulation working condition data and the optimization design parameters of precision parts, the multi-physics field coupling model of the calibrated precision parts is used to perform simulation, and accurate simulation result data can be obtained. This simulation result helps to evaluate the performance of precision parts under different conditions and provides specific data support for further optimization. The simulation results are verified and the design target difference analysis is performed to ensure the accuracy of the simulation results and the fit of the design targets. Through the difference analysis, potential deviations or inconsistencies in the design can be found, thereby providing detailed suggestions for design optimization. This step ensures the consistency of the simulation analysis with the actual design requirements and improves the reliability of the optimization scheme. The simulation result data and the design optimization suggestion data are integrated to generate a precision part simulation analysis report. This report provides the design team with detailed analysis results and optimization directions to help make final design decisions, ensure that the performance of precision parts meets expectations, and provide a basis for decision-making in the production process.

[0143] Preferably, the present invention further provides a three-dimensional simulation design system for a precision part model, which is used to execute the above-mentioned three-dimensional simulation design method for a precision part model, and the three-dimensional simulation design system for a precision part model comprises:

[0144] The precision part model creation module is used to construct a three-dimensional geometric model based on the precision part design requirement data to obtain the three-dimensional geometric model data of the precision part; obtain the material characteristic parameters of the precision part, and use the material characteristic parameters of the precision part to construct the material characteristic model to obtain the material characteristic model data of the precision part;

[0145] The multi-physics coupling module is used to construct a multi-physics coupling model using the three-dimensional geometric model data of precision parts and the material characteristic model data of precision parts to obtain a multi-physics coupling model of precision parts;

[0146] The simulation analysis and optimization module is used to numerically solve the multi-physics field coupling model of precision parts to obtain multi-physics field simulation result data; perform multi-physics field analysis on the multi-physics field simulation result data, and adjust the precision part design parameters based on the multi-physics field analysis results to obtain the precision part design parameters;

[0147] The virtual verification and calibration module is used to perform virtual testing on the design parameters of precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data. Based on the comparative analysis results, the multi-physics field coupling model of precision parts is optimized to obtain a calibrated multi-physics field coupling model of precision parts.

[0148] Reliability and safety assessment module, which is used to assess the reliability and safety margin based on the multi-physics field coupling model of calibrated precision parts, and optimize the design parameters of precision parts based on the reliability and safety margin assessment results to obtain the optimized design parameters of precision parts;

[0149] The simulation report generation module is used to simulate the optimization design parameters of precision parts by using the calibrated precision part multi-physics field coupling model to generate a precision part simulation analysis report.

[0150] The present invention can accurately express the shape and size of precision parts and ensure the accuracy of design data by constructing a three-dimensional geometric model based on precision parts design demand data. By acquiring and utilizing the material characteristic parameters of precision parts, a material characteristic model is constructed, so that the physical and mechanical properties of the material are accurately simulated, thereby providing accurate basic data for subsequent simulation analysis. This module lays a solid foundation for subsequent multi-physics field coupling models and simulation analysis. Using the three-dimensional geometric model data and material characteristic model data of precision parts, a multi-physics field coupling model is constructed to achieve interaction and coupling between different physical fields. This module can accurately reflect the physical behavior of precision parts under different working conditions, ensure the comprehensiveness and efficiency of simulation results, and help simulate the performance of parts in practical applications. The multi-physics field coupling model is numerically solved to generate simulation result data of precision parts, which can accurately predict the performance of parts under different conditions. By performing multi-physics field analysis on the simulation results, problems in the design can be discovered in time, and then the design parameters of precision parts can be adjusted. This process can effectively improve the design quality of parts and ensure that their performance meets the needs of practical applications. Compare and analyze the virtual test and multi-physics simulation result data to verify the accuracy of the simulation model, and optimize the parameters of the multi-physics coupling model based on the analysis results. This module can improve the prediction accuracy of the model, ensure the reliability of the design, and provide a scientific basis for subsequent optimization work. This module provides a powerful verification method for the design of precision parts, ensuring the correctness and rationality of the design. Based on the calibrated multi-physics coupling model, the reliability and safety margin of precision parts are evaluated. Through the safety margin calculation and reliability analysis under extreme conditions, the risks of precision parts in actual use can be evaluated, and the design parameters can be optimized based on the evaluation results. This module helps ensure that the designed parts have high safety and stability during use. Use the optimized design parameters to simulate the precision parts and generate a detailed simulation analysis report. The report will provide detailed analysis results of the performance and various technical indicators of the precision parts after the optimized design, providing engineers and decision makers with accurate design basis. This module provides a result summary and evaluation tool for the entire design process, allowing the design team to clearly evaluate the effect of design optimization and room for improvement.

[0151] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited by the above description, so it is intended to include all changes within the meaning and scope of the equivalent elements of the application documents in the present invention.

[0152] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A three-dimensional simulation design method for a precision part model, characterized in that: The following steps are involved: Step S1: obtaining precision part design requirement data, constructing a three-dimensional geometric model based on the precision part design requirement data, and obtaining the precision part three-dimensional geometric model data; Obtaining material characteristic parameters of precision parts, using the material characteristic parameters of precision parts to construct a material characteristic model, and obtaining material characteristic model data of precision parts; Step S2: constructing a multi-physics field coupling model using the three-dimensional geometric model data of the precision parts and the material property model data of the precision parts to obtain a multi-physics field coupling model of the precision parts; Step S3: numerically solving the multi-physics field coupling model of precision parts to obtain multi-physics field simulation result data; Perform multi-physics field analysis on multi-physics field simulation result data, and adjust precision part design parameters based on the multi-physics field analysis results to obtain precision part design parameters; Step S4: Perform virtual testing on the design parameters of the precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data, optimize the parameters of the multi-physics field coupling model of the precision parts based on the comparative analysis results, and obtain a calibrated multi-physics field coupling model of the precision parts; Step S5: performing reliability and safety margin evaluation based on the calibrated precision part multi-physics field coupling model, and optimizing the precision part design parameters based on the reliability and safety margin evaluation results to obtain the precision part optimized design parameters; Step S6: Use the calibrated precision part multi-physics field coupling model to simulate the optimization design parameters of the precision part and generate a precision part simulation analysis report.

2. The three-dimensional simulation design method for a precision part model according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining precision part design requirement data, using the precision part design requirement data to perform preliminary geometric structure design, and obtaining preliminary three-dimensional geometric model data; Step S12: performing parameter verification on the preliminary three-dimensional geometric model data, and adjusting the preliminary three-dimensional geometric model data based on the parameter verification result to obtain the three-dimensional geometric model data of the precision part; Step S13: obtaining material characteristic parameters of precision parts, performing nonlinear characteristic analysis on the material characteristic parameters of precision parts, and obtaining material nonlinear characteristic analysis data; Step S14: constructing a material property model using the material nonlinear property analysis data to obtain precision parts material property model data.

3. The three-dimensional simulation design method for a precision part model according to claim 2, characterized in that: Step S2 includes the following steps: Step S21: performing multi-physical field factor analysis based on precision parts design requirement data to obtain multi-physical field analysis requirement data; Step S22: constructing a multi-physics control equation according to the multi-physics analysis requirement data, and defining a multi-physics coupling relationship to obtain a multi-physics coupling equation; Step S23: Adaptively meshing the three-dimensional geometric model data of the precision part to obtain the three-dimensional mesh model data of the precision part; Step S24: Utilizing the precision part three-dimensional mesh model data and the precision part material property model data to configure the multi-physics field coupling model parameters, and obtaining the multi-physics field coupling model; Step S25: Perform preliminary simulation calculations on the multi-physics field coupling model and verify the model stability. Based on the stability verification results, optimize the model to obtain a multi-physics field coupling model for precision parts.

4. The three-dimensional simulation design method for a precision part model according to claim 3, characterized in that: Step S3 includes the following steps: Step S31: performing characteristic analysis on the multi-physics field coupling model of precision parts, and screening the solution algorithm based on the characteristic analysis results to obtain the model numerical solution configuration data; Step S32: numerically solving the multi-physics field coupling model of the precision parts using the model numerical solution configuration data to obtain multi-physics field simulation result data; Step S33: extracting and analyzing the key performance of precision parts from the multi-physics field simulation result data to obtain multi-physics field analysis results; Step S34: Optimize the precision part design scheme according to the multi-physical field analysis results to obtain precision part design parameters.

5. The three-dimensional simulation design method for a precision part model according to claim 4, characterized in that: Step S31 includes the following steps: Step S311: Perform static characteristic analysis on the multi-physics field coupling model of precision parts and extract key parameters to obtain preliminary characteristic parameter extraction data; Step S312: performing complexity classification on the multi-physics field coupling model of the precision parts according to the preliminary characteristic parameter extraction data to obtain the complexity classification data of the multi-physics field model; Step S313: Screening the solution algorithm based on the preliminary characteristic parameter extraction data and the multi-physics field model complexity classification data to obtain the solution algorithm candidate solution list data; Step S314: Perform performance simulation evaluation on the candidate solution list data of the solution algorithm to obtain solution algorithm performance evaluation data; Step S315: configuring model numerical solution parameters based on the solution algorithm performance evaluation data to obtain model numerical solution configuration data.

6. The three-dimensional simulation design method for a precision part model according to claim 5, characterized in that: Step S4 includes the following steps: Step S41: Designing a virtual test plan according to the precision part design parameters and preset usage scenario parameters to obtain a precision part virtual test plan; Step S42: using a precision part virtual test solution to perform a virtual test on the precision part design parameters to obtain design parameter virtual test result data; Step S43: Compare and analyze the design parameter virtual test result data with the multi-physics field simulation result data to obtain a test simulation comparison analysis report; Step S44: Optimize the parameters of the multi-physics field coupling model of precision parts using the test simulation comparison analysis report to obtain a calibrated multi-physics field coupling model of precision parts.

7. The three-dimensional simulation design method for a precision part model according to claim 6, characterized in that: Step S43 includes the following steps: Step S431: extracting key performance indicators from the design parameter virtual test result data and the multi-physics field simulation result data to obtain a key performance indicator data set; Step S432: performing difference analysis based on the key performance indicator data set, and quantifying the difference analysis results to obtain indicator difference analysis report data; Step S433: performing adaptability evaluation on the multi-physics field coupling model of precision parts according to the index difference analysis report data to obtain model adaptability evaluation result data; Step S434: Integrate the key performance indicator data set, indicator difference analysis report data and model adaptability evaluation result data to obtain a test simulation comparison analysis report.

8. The three-dimensional simulation design method for a precision part model according to claim 7, characterized in that: Step S5 includes the following steps: Step S51: designing a model reliability evaluation index based on the precision parts design requirement data to obtain model reliability evaluation index data; Step S52: using the model reliability evaluation index data to perform reliability analysis on the multi-physics field coupling model of the calibrated precision parts under various working conditions, and obtaining model reliability analysis result data; Step S53: Calculate the safety margin under extreme working conditions based on the model reliability analysis result data to obtain the model safety margin evaluation result data; Step S54: optimizing the design parameters of the precision parts according to the model reliability analysis result data and the model safety margin assessment result data to obtain the optimized design parameters of the precision parts.

9. The three-dimensional simulation design method for a precision part model according to claim 8, characterized in that: Step S6 includes the following steps: Step S61: setting simulation working conditions and boundary conditions based on the optimization design parameters of the precision parts to obtain simulation working condition data; Step S62: Based on the simulation working condition data, the multi-physics field coupling model of the calibrated precision parts is used to simulate the optimization design parameters of the precision parts to obtain simulation result data; Step S63: verifying the simulation result data, and performing design target difference analysis on the simulation result data based on the verification result to obtain design optimization suggestion data; Step S64: Integrate the simulation result data and the design optimization suggestion data to obtain a precision parts simulation analysis report.

10. A three-dimensional simulation design system for precision parts models, characterized in that: For executing the three-dimensional simulation design method for a precision part model according to claim 1, the three-dimensional simulation design system for a precision part model comprises: The precision part model creation module is used to construct a three-dimensional geometric model based on the precision part design requirement data to obtain the three-dimensional geometric model data of the precision part; obtain the material characteristic parameters of the precision part, and use the material characteristic parameters of the precision part to construct the material characteristic model to obtain the material characteristic model data of the precision part; The multi-physics coupling module is used to construct a multi-physics coupling model using the three-dimensional geometric model data of precision parts and the material characteristic model data of precision parts to obtain a multi-physics coupling model of precision parts; The simulation analysis and optimization module is used to numerically solve the multi-physics field coupling model of precision parts to obtain multi-physics field simulation result data; perform multi-physics field analysis on the multi-physics field simulation result data, and adjust the precision part design parameters based on the multi-physics field analysis results to obtain the precision part design parameters; The virtual verification and calibration module is used to perform virtual testing on the design parameters of precision parts, and compare and analyze the virtual test results with the multi-physics field simulation result data. Based on the comparative analysis results, the multi-physics field coupling model of precision parts is optimized to obtain a calibrated multi-physics field coupling model of precision parts. Reliability and safety assessment module, which is used to assess the reliability and safety margin based on the multi-physics field coupling model of calibrated precision parts, and optimize the design parameters of precision parts based on the reliability and safety margin assessment results to obtain the optimized design parameters of precision parts; The simulation report generation module is used to simulate the optimization design parameters of precision parts by using the calibrated precision part multi-physics field coupling model to generate a precision part simulation analysis report.

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