Parameterized simulation model design method and system for plastic mold

Abstract

The invention relates to the technical field of plastic mold parameterized simulation, in particular to a parameterized simulation model design method and system for a plastic mold. The method comprises the following steps: carrying out raw material fine preparation on a target plastic raw material to obtain a homogeneous dry material; performing multi-field rheological characteristic scanning on the homogenized and dried material, and extracting viscosity model parameters to obtain viscosity model initial parameters; precisely measuring thermodynamic characteristics of the plastic material on the homogenized and dried material, and analyzing crystallization kinetic behaviors to obtain crystallization kinetic model parameters; and carrying out material gene spectrum integration according to the initial parameters of the viscosity model and the parameters of the crystallization kinetic model to obtain a material gene spectrum. According to the method, through performance-driven parameter optimization and intelligent mold blueprint generation based on the material physical property and the microenvironment, the mold manufacturing efficiency and quality are remarkably improved, and the manufacturing cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of parametric simulation of plastic molds, and in particular to a parametric simulation model design method and system for plastic molds. Background Art

[0002] Plastic molds are key tools for the production of plastic products, and their design quality directly affects the final performance, production efficiency and cost of the product. Existing parametric simulation models for plastic molds often treat plastic materials as homogeneous continua, ignoring the complex physical behaviors of plastics during the molding process, such as shear thinning, viscoelasticity, crystallization behavior, phase change, etc., resulting in deviations between simulation results and actual production;

[0003] Existing methods mainly focus on the macroscopic flow behavior of the melt and the overall temperature distribution of the mold, such as filling time, pressure distribution, temperature uniformity, etc. However, there is a lack of refined simulation and analysis of the microenvironment inside the mold cavity, such as local shear rate, cooling rate gradient, crystallinity distribution, molecular orientation distribution, residual stress distribution, etc.

[0004] Because existing methods ignore the complex physical behavior of plastic materials and simulation focuses on macro analysis, they lack insight into the micro environment and are difficult to guide the refined design and process optimization of molds. Summary of the invention

[0005] Based on this, it is necessary to provide a parametric simulation model design method and system for plastic molds to solve at least one of the above technical problems.

[0006] To achieve the above object, a parametric simulation model design method for a plastic mold includes the following steps:

[0007] Step S1: finely prepare the target plastic raw material to obtain a homogeneous dry material; perform multi-field rheological property scanning on the homogeneous dry material, and extract viscosity model parameters to obtain initial viscosity model parameters; accurately measure the thermodynamic properties of the plastic material on the homogeneous dry material, and perform crystallization kinetic behavior analysis to obtain crystallization kinetic model parameters; integrate the material gene spectrum according to the initial viscosity model parameters and the crystallization kinetic model parameters to obtain the material gene spectrum;

[0008] Step S2: constructing a group of physical property correlation control equations according to the material gene spectrum to obtain a group of physical property correlation control equations; obtaining a mold geometry model; setting complete model boundary conditions according to the mold geometry model and the group of physical property correlation control equations to obtain a complete model boundary condition set; constructing a multiphase behavior model according to the complete model boundary condition set to obtain a multiphase behavior model;

[0009] Step S3: parameterizing the mold geometry model to obtain mold geometry model parameters; performing multi-physical field coupling transient solution according to the multi-phase behavior model and the mold geometry model parameters to obtain original field variable time series data; performing micro-environment mapping according to the original field variable time series data to obtain a mold cavity microclimate map;

[0010] Step S4: Obtaining target part performance requirement data, and constructing an optimization objective function to obtain a multi-objective optimization function; using a multi-phase behavior model to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain design performance evaluation data; performing performance topology optimization based on the design performance evaluation data and mold geometry model parameters to obtain a performance topology solution;

[0011] Step S5: parametrically reconstruct the optimized model according to the performance topology solution to obtain an optimized parametric CAD model; generate an intelligent processing strategy for the optimized parametric CAD model according to the mold cavity microclimate map and the material gene spectrum to obtain intelligent processing guidance information; organize and output blueprint information according to the intelligent processing guidance information to obtain an intelligent mold blueprint.

[0012] The present invention constructs a material gene spectrum including viscosity model, thermodynamic parameters, crystallization kinetic parameters and PVT relationship through a series of fine experimental measurements and model fitting, comprehensively and accurately describes the physical properties of the target plastic material, and provides a reliable data basis for subsequent simulation and optimization. Based on the material gene spectrum, a multiphase behavior model including mass, momentum, energy conservation and crystallization kinetic equations is constructed, and an accurate material constitutive relationship is embedded, which realizes the accurate numerical simulation of the plastic melt injection molding process, and provides a reliable tool for subsequent micro-environment analysis and parameter optimization. Through multi-physics field coupling transient solution and micro-variable extraction, a mold cavity microclimate map is constructed, which intuitively displays the micro-environment information such as temperature, pressure, shear rate and crystallinity inside the mold cavity, and provides key data and intuitive reference for subsequent performance evaluation and optimization. Based on the mold cavity microclimate map and multi-objective optimization function, the optimal combination of mold design parameters is found through iterative optimization algorithm, and through topological structure evolution, the performance of the parts is further improved, the design cycle of the mold is shortened, and the development cost is reduced. The optimized mold model, simulation data, material information and intelligent processing strategy are integrated together to generate an intelligent mold blueprint containing rich information, which realizes the seamless connection of mold design, simulation analysis and processing and manufacturing, significantly improves the efficiency and quality of mold manufacturing, and reduces manufacturing costs. Therefore, the present invention provides a parametric simulation model design method for plastic molds, which accurately captures the complex physical behavior of plastic materials by constructing a sophisticated "material gene spectrum" and a multi-physics field coupling model; and deeply reveals the microscopic physical environment inside the mold cavity through refined simulation and construction of a "mold cavity microclimate map", and finally realizes performance-driven parameter optimization and "intelligent mold blueprint" generation based on material properties and microenvironment, thereby overcoming the shortcomings of existing methods of simplified material models and lack of microenvironmental insights. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of the steps of a parametric simulation model design method for plastic molds;

[0014] Figure 2 It is a schematic diagram of the detailed implementation steps of step S1 in the present invention.

[0015] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0016] To achieve this, please refer to Figure 1 to Figure 2 , a parametric simulation model design method for a plastic mold, comprising the following steps:

[0017] Step S1: finely prepare the target plastic raw material to obtain a homogeneous dry material; perform multi-field rheological property scanning on the homogeneous dry material, and extract viscosity model parameters to obtain initial viscosity model parameters; accurately measure the thermodynamic properties of the plastic material on the homogeneous dry material, and perform crystallization kinetic behavior analysis to obtain crystallization kinetic model parameters; integrate the material gene spectrum according to the initial viscosity model parameters and the crystallization kinetic model parameters to obtain the material gene spectrum;

[0018] Step S2: constructing a group of physical property correlation control equations according to the material gene spectrum to obtain a group of physical property correlation control equations; obtaining a mold geometry model; setting complete model boundary conditions according to the mold geometry model and the group of physical property correlation control equations to obtain a complete model boundary condition set; constructing a multiphase behavior model according to the complete model boundary condition set to obtain a multiphase behavior model;

[0019] Step S3: parameterizing the mold geometry model to obtain mold geometry model parameters; performing multi-physical field coupling transient solution according to the multi-phase behavior model and the mold geometry model parameters to obtain original field variable time series data; performing micro-environment mapping according to the original field variable time series data to obtain a mold cavity microclimate map;

[0020] Step S4: Obtaining target part performance requirement data, and constructing an optimization objective function to obtain a multi-objective optimization function; using a multi-phase behavior model to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain design performance evaluation data; performing performance topology optimization based on the design performance evaluation data and mold geometry model parameters to obtain a performance topology solution;

[0021] Step S5: parametrically reconstruct the optimized model according to the performance topology solution to obtain an optimized parametric CAD model; generate an intelligent processing strategy for the optimized parametric CAD model according to the mold cavity microclimate map and the material gene spectrum to obtain intelligent processing guidance information; organize and output blueprint information according to the intelligent processing guidance information to obtain an intelligent mold blueprint.

[0022] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of the steps of the parametric simulation model design method for a plastic mold according to the present invention. In this example, the parametric simulation model design method for a plastic mold includes the following steps:

[0023] Step S1: finely prepare the target plastic raw material to obtain a homogeneous dry material; perform multi-field rheological property scanning on the homogeneous dry material, and extract viscosity model parameters to obtain initial viscosity model parameters; accurately measure the thermodynamic properties of the plastic material on the homogeneous dry material, and perform crystallization kinetic behavior analysis to obtain crystallization kinetic model parameters; integrate the material gene spectrum according to the initial viscosity model parameters and the crystallization kinetic model parameters to obtain the material gene spectrum;

[0024] In the embodiment of the present invention, after the target plastic raw material is accurately weighed, a laser particle size analyzer is used to perform particle size analysis to ensure uniform particle size, and then dry nitrogen is introduced into a vacuum drying oven and the temperature and humidity are controlled to fully dry to obtain a homogeneous dry material. Using a rheometer equipped with different test fixtures, the homogeneous dry material is scanned for multi-field rheological properties in various modes such as steady-state shear and dynamic oscillation, and the viscosity, modulus and other data under different temperatures, pressures and shear rates are recorded to form an original rheological curve set. Using the nonlinear least squares method, a suitable viscosity model is selected to fit the original rheological curve, and the initial parameters of the viscosity model are extracted. At the same time, a differential scanning calorimeter is used to accurately measure the glass transition temperature, melting temperature, specific heat capacity and other thermodynamic parameters of the homogeneous dry material. Combined with the DSC test data, the Avrami or Nakamura model is used to analyze the crystallization kinetic behavior of the plastic material to obtain the crystallization kinetic model parameters. Finally, the initial parameters of the viscosity model and the crystallization kinetic model parameters are integrated to construct a material gene spectrum containing the key physical properties of the plastic material.

[0025] Step S2: constructing a group of physical property correlation control equations according to the material gene spectrum to obtain a group of physical property correlation control equations; obtaining a mold geometry model; setting complete model boundary conditions according to the mold geometry model and the group of physical property correlation control equations to obtain a complete model boundary condition set; constructing a multiphase behavior model according to the complete model boundary condition set to obtain a multiphase behavior model;

[0026] In an embodiment of the present invention, based on the material gene spectrum constructed in step S1, appropriate flow, heat transfer and phase change control equations are selected to construct an original control equation group describing the behavior of the plastic melt. The viscosity model, thermodynamic parameters, crystallization kinetics model, PVT relationship and other information obtained in the material gene spectrum are embedded in the original control equation group to form a physical property correlation control equation group that can accurately describe the behavior of the target plastic material. Obtain a three-dimensional CAD model of the plastic mold to be analyzed. According to the mold geometry and injection molding process parameters, the model's inlet velocity, mold wall temperature and other boundary conditions as well as the initial temperature field and flow field state are set to obtain a complete model boundary condition set. Select a suitable numerical discretization format, such as the finite volume method and the second-order upwind format, discretize the physical property correlation control equation group, design a multi-physics field coupling solution algorithm, such as a separation solution strategy and parallel computing technology, and code the algorithm to finally obtain a multi-phase behavior model capable of multi-physics field coupling simulation.

[0027] Step S3: parameterizing the mold geometry model to obtain mold geometry model parameters; performing multi-physical field coupling transient solution according to the multi-phase behavior model and the mold geometry model parameters to obtain original field variable time series data; performing micro-environment mapping according to the original field variable time series data to obtain a mold cavity microclimate map;

[0028] In an embodiment of the present invention, for the three-dimensional CAD model of the mold to be analyzed, the parametric design function of the CAD software is used to parametrically define key geometric features such as the cooling water channel size and the pouring system size, and the mold geometric model parameters are obtained. The parametric mold geometric model is imported into the meshing software, and a variety of mesh types such as hexahedrons and tetrahedrons are used for fine discretization, and a boundary layer mesh is set near the wall to generate a high-density discrete mesh. The high-density discrete mesh is imported into the multi-physics field simulation software, and the simulation parameters such as the inlet velocity, mold temperature, time step and convergence criterion are accurately configured according to the requirements of the injection molding process parameters and the multi-phase behavior model. The simulation software is started, and the multi-physics field coupling transient solution is performed on the injection molding process, and the original field variable data such as the speed, pressure, temperature and crystallinity of each grid unit inside the mold cavity at different times are recorded. The original field variable time series data is post-processed, and microscopic variables such as the shear rate, cooling rate gradient and crystallinity distribution inside the mold cavity are calculated, and the microscopic variable distribution snapshot at a specific time is extracted, and the transient microscopic environment data stream is integrated in chronological order. Finally, the transient micro-environment data stream is mapped onto the mold geometry model for visualization, generating a cavity microclimate map that intuitively displays the microscopic physical environment inside the mold cavity.

[0029] Step S4: Obtaining target part performance requirement data, and constructing an optimization objective function to obtain a multi-objective optimization function; using a multi-phase behavior model to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain design performance evaluation data; performing performance topology optimization based on the design performance evaluation data and mold geometry model parameters to obtain a performance topology solution;

[0030] In an embodiment of the present invention, performance requirement data such as strength, warpage and surface roughness of the target plastic part are obtained. According to microscopic variables such as residual stress, temperature distribution and cooling rate in the cavity microclimate map, a mapping relationship between the performance index of the part is established to achieve quantification of the performance index. The mold design parameters that need to be optimized are clarified, such as the size and layout of the cooling water channel and the pouring system, and their value ranges are set to construct a parametric design space. According to the performance requirements of the part, a multi-objective optimization function with the goal of minimizing warpage and maximizing strength is constructed. Different design schemes in the parametric design space are simulated using a multiphase behavior model, and performance evaluation is performed based on the cavity microclimate map to obtain performance evaluation data of the design scheme. Using optimization algorithms such as genetic algorithms, according to the objective function and performance evaluation data, the optimal mold design parameter combination is searched in the parametric design space to obtain a candidate optimization parameter set. For structures such as cooling water channels, topology optimization methods are used to perform structural evolution and reconstruction to obtain a topology evolution model. Finally, the candidate optimization parameter set and the topology evolution model are performance verified and screened to determine the final performance topology scheme.

[0031] Step S5: parametrically reconstruct the optimized model according to the performance topology scheme to obtain an optimized parametric CAD model; generate an intelligent processing strategy for the optimized parametric CAD model according to the mold cavity microclimate map and the material gene spectrum to obtain intelligent processing guidance information; organize and output blueprint information according to the intelligent processing guidance information to obtain an intelligent mold blueprint;

[0032] In an embodiment of the present invention, according to the performance topology scheme obtained in step S4, the geometric model of the mold is rebuilt in the CAD software, and the optimized complex structures such as cooling channels and pouring systems are accurately created to obtain an optimized parameterized CAD model. The key simulation data such as temperature, pressure and crystallinity distribution in the mold cavity microclimate map generated in step S3 are integrated into the optimized parameterized CAD model, and visualized in the form of color mapping or attribute annotation to obtain a physical information enhanced CAD model. The material attribute information in the material gene spectrum generated in step S1 is embedded in the physical information enhanced CAD model, and accurate material parameters are specified for different components of the mold to obtain a material attribute enriched CAD model. Based on the information in the material attribute enriched CAD model and the mold cavity microclimate map, suitable cutting parameters, tool types and processing paths are recommended for different areas of the mold to generate intelligent processing strategies. The material attribute enriched CAD model and the intelligent processing guidance information are integrated, and format conversion and data encapsulation are performed according to manufacturing standards to obtain a manufacturable digital model package. Finally, all the information in the manufacturable digital model package is organized and sorted to generate an intelligent mold blueprint containing traditional engineering drawings, three-dimensional models, simulation results display and intelligent processing guidance information.

[0033] Preferably, step S1 comprises the following steps:

[0034] Step S11: finely preparing the target plastic raw material to obtain a homogeneous dry material;

[0035] Step S12: performing multi-field rheological property scanning on the homogenized dry material to obtain an original rheological curve set;

[0036] Step S13: extracting viscosity model parameters from the original rheological curve set to obtain initial parameters of the viscosity model; accurately measuring the thermodynamic properties of the plastic material on the homogeneous dry material to obtain a thermodynamic parameter set;

[0037] Step S14: performing crystallization kinetic behavior analysis based on the thermodynamic parameter set and the homogenized dried material to obtain crystallization kinetic model parameters;

[0038] Step S15: Use a PVT tester to accurately characterize the PVT relationship of the homogenized dry material to obtain a PVT relationship data table;

[0039] Step S16: Perform comprehensive calibration of model parameters on the viscosity model initial parameters, thermodynamic parameter set, crystallization kinetics model parameters and PVT relationship data table to obtain a calibrated model parameter set; perform material gene spectrum integration on the calibrated model parameter set to obtain a material gene spectrum.

[0040] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes:

[0041] Step S11: finely preparing the target plastic raw material to obtain a homogeneous dry material;

[0042] In the embodiment of the present invention, first, the target plastic raw material is taken, and its specific brand and batch must be completely consistent with the raw materials used in the subsequent injection molding production to ensure the accuracy of the physical property characterization. Use a precision balance to accurately weigh no less than 100 grams of plastic raw material particles, and then perform particle size analysis. Through a laser particle size analyzer, the raw material particles are dispersed in a flowing medium, and the particle size distribution of the particles is measured using the laser diffraction principle to ensure that the particle size is uniform and avoid the inconsistency of melting behavior caused by particle size differences. Then, the weighed raw material is placed in a vacuum drying oven, and the drying temperature is set to the drying temperature recommended by the target plastic material manufacturer, such as 80 degrees Celsius, and dry nitrogen is introduced to maintain a low humidity environment. Continue drying for no less than 4 hours, during which the humidity in the drying oven is monitored in real time using a dew point meter to ensure that the humidity is lower than the moisture absorption limit of the material, such as less than 0.02%. After drying, the raw material is sealed and stored in a drying container to prevent moisture absorption again, providing a consistent initial state for subsequent tests.

[0043] Step S12: performing multi-field rheological property scanning on the homogenized dry material to obtain an original rheological curve set;

[0044] In an embodiment of the present invention, the homogenized dried material prepared in step S11 is sent to a rheometer equipped with different test fixtures in batches for multi-field rheological property scanning. First, a parallel plate fixture is used to set the temperature gradient, for example, from the upper limit to the lower limit of the commonly used processing temperature for injection molding, and an isothermal test is performed at intervals of 10 degrees Celsius. At each temperature point, a steady-state shear test is performed, and the shear rate is controlled to gradually increase from the negative first power of 0.1 seconds to the negative first power of 1000 seconds, and the viscosity of the melt is recorded as the shear rate changes. Secondly, the fixture is replaced with a cone-plate fixture, and a dynamic oscillation test is performed, and different oscillation frequencies are set, for example, 0.01 Hz to 100 Hz, and the storage modulus, loss modulus and loss tangent are measured to characterize the viscoelasticity of the melt. Then, using a rheometer equipped with a pressure control module, the above-mentioned steady-state shear test is repeated at different pressures to examine the effect of pressure on the viscosity of the melt. During all testing processes, the rheometer needs to precisely control temperature and pressure, and record raw data such as torque and angular velocity in real time, ultimately generating a set of original rheological curves containing information such as viscosity and modulus at different temperatures, pressures, and shear rates.

[0045] Step S13: extracting viscosity model parameters from the original rheological curve set to obtain initial parameters of the viscosity model; accurately measuring the thermodynamic properties of the plastic material on the homogeneous dry material to obtain a thermodynamic parameter set;

[0046] In the embodiment of the present invention, for the original rheological curve set obtained in step S12, a nonlinear least square method is used, and a data fitting software such as RheoCompass or Origin is used to select a suitable viscosity model, such as the Cross-WLF model, whose mathematical expression can better describe the shear thinning behavior and temperature dependence of the plastic melt. The original rheological data is imported into the software, the initial values ​​and boundary conditions of the model parameters are set, and the deviation of the viscosity curve predicted by the model from the experimental data points is minimized by an iterative optimization algorithm such as the Levenberg-Marquardt algorithm, and the initial parameters of the viscosity model such as the zero shear viscosity, apparent activation energy, and temperature-related coefficients of the Cross-WLF model are finally extracted. At the same time, the homogeneous dried material prepared in step S11 is taken, and a differential scanning calorimeter (DSC) is used to perform heating, cooling, and reheating scanning under nitrogen protection according to the standard test procedure. The heating rate is set to 10 degrees Celsius per minute, and the glass transition temperature, cold crystallization temperature (if any), melting temperature, and corresponding enthalpy change are recorded. By analyzing the DSC curve, the specific heat capacity of the material is calculated, for example, using the three-point method or the secant method, to obtain a set of thermodynamic parameters of the target plastic material, including glass transition temperature, melting temperature, specific heat capacity and other values.

[0047] Step S14: performing crystallization kinetic behavior analysis based on the thermodynamic parameter set and the homogenized dried material to obtain crystallization kinetic model parameters;

[0048] In an embodiment of the present invention, the heating and cooling curves obtained by the differential scanning calorimeter (DSC) in step S13 are used to focus on analyzing the crystallization exothermic peak during the cooling process. Data analysis software such as Origin is used to integrate the exothermic peak and calculate the crystallinity at different cooling rates. At the same time, multiple different cooling rates, such as 5 degrees Celsius per minute, 10 degrees Celsius per minute, and 20 degrees Celsius per minute, are selected to repeat the DSC cooling experiment to obtain the crystallization exothermic curve at different cooling rates. Based on these non-isothermal crystallization data, crystallization kinetic models such as the Avrami equation or the Nakamura model are used for fitting. For example, for the Avrami equation, the curve of the crystallization fraction changing with time is double-logarithmically plotted, the slope and intercept of the straight line are analyzed, and the Avrami index n and the crystallization rate constant k are determined. Alternatively, for the Nakamura model, the numerical integration method is used, combined with the DSC data, to reverse the parameters in the model, such as the effective activation energy and the pre-exponential factor. In the process of model fitting, it is necessary to consider the influence of nucleation mechanism and crystal growth mode, make multiple fitting attempts, and select the model parameters with the highest fitting degree as the final crystallization kinetics model parameters.

[0049] Step S15: Use a PVT tester to accurately characterize the PVT relationship of the homogenized dry material to obtain a PVT relationship data table;

[0050] In an embodiment of the present invention, the homogeneous dry material prepared in step S11 is taken and placed in the sample chamber of a high-pressure PVT tester. The tester measures the specific volume or density of the plastic material by precisely controlling the temperature and pressure of the sample chamber. First, a series of isotherms are set, for example, from the upper limit to the lower limit of the processing temperature, with an interval of 5 degrees Celsius. On each isotherm, the pressure is gradually increased, for example, from atmospheric pressure to the highest pressure that may be reached during the injection molding process, with an interval of 5 MPa. At each temperature and pressure combination, after the system reaches thermodynamic equilibrium, the PVT tester accurately measures the sample volume and calculates the corresponding specific volume or density value. During the test, the heating, cooling and pressurization rates need to be strictly controlled to ensure the accuracy of the measured data. Finally, the measured temperature, pressure and specific volume (or density) data are organized into a table to form a PVT relationship data table, which will be used to construct or verify the state equation of the plastic material.

[0051] Step S16: performing comprehensive calibration of model parameters on the viscosity model initial parameters, the thermodynamic parameter set, the crystallization kinetics model parameters and the PVT relationship data table to obtain a calibrated model parameter set; performing material gene spectrum integration on the calibrated model parameter set to obtain a material gene spectrum;

[0052] In an embodiment of the present invention, the initial parameters of the viscosity model obtained in step S13, the thermodynamic parameter set, the crystallization kinetics model parameters obtained in step S14, and the PVT relationship data table obtained in step S15 are imported into a material modeling and simulation software platform, such as Moldflow Material Characterization or Polyflow Material Data Fitting. On this platform, a multi-objective optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, is used to comprehensively calibrate various model parameters based on experimental data. Specifically, the viscosity model parameters are substituted into the fluid dynamics simulation model, the rheological test process in step S12 is simulated, the simulation results are compared with the experimentally measured rheological curve, and the viscosity model parameters are adjusted to achieve the highest degree of fit between the two. Similarly, the thermodynamic parameters and the crystallization kinetics model parameters are substituted into the heat transfer and crystallization kinetics simulation model, the DSC test process in step S14 is simulated, the simulation results are compared with the experimentally measured DSC curve, and the model parameters are further optimized. At the same time, the PVT relationship data table is used to fit the state equation, such as the Tait equation, to determine the parameters of the state equation. After multiple rounds of iterative optimization, the calibrated model parameter set is obtained until the deviation between all model prediction results and experimental data is within an acceptable range. Finally, the calibrated viscosity model parameters, thermodynamic parameters, crystallization kinetics model parameters, and PVT relationship data are integrated into a structured database according to the predetermined data structure and format. The database is the material gene spectrum of the target plastic material, which contains key information describing the physical behavior of the material under different working conditions.

[0053] Preferably, step S14 comprises the following steps:

[0054] Step S141: performing in-situ polarizing microscope crystallization observation on the homogenized dried material according to the thermodynamic parameter set to obtain a crystal morphology evolution image;

[0055] Step S142: performing in-situ small-angle X-ray scattering structural analysis on the homogenized dried material according to the thermodynamic parameter set to obtain nanoscale structural evolution data;

[0056] Step S143: performing a rapid scanning calorimetry non-isothermal crystallization analysis on the homogenized dried material according to the thermodynamic parameter set to obtain a non-isothermal crystallization exothermic curve;

[0057] Step S144: performing a correlation analysis between thermodynamic parameters and microstructure on the thermodynamic parameter set, crystal morphology evolution image, nanoscale structure evolution data, and non-isothermal crystallization exothermic curve to obtain multi-scale crystallization behavior characteristics;

[0058] Step S145: Selecting a crystallization kinetics model and preliminarily estimating parameters according to the multi-scale crystallization behavior characteristics to obtain preliminary model parameters and model type;

[0059] Step S146: using the non-isothermal crystallization exothermic curve to perform model parameter fitting and optimization on the preliminary model parameters and model type to obtain optimized model parameters;

[0060] Step S147: performing crystallization kinetics model verification on the optimized model parameters to obtain crystallization kinetics model parameters.

[0061] In an embodiment of the present invention, a small amount of the homogeneous dried material prepared in step S11 is placed on a slide of a polarizing microscope equipped with a heating and cooling stage. According to the thermodynamic parameter set obtained in step S13, the temperature program of the microscope heating and cooling stage is set. First, the sample is heated to 10 degrees Celsius above its melting temperature and kept for 5 minutes to eliminate the thermal history. Subsequently, the sample is cooled to room temperature at a set cooling rate, such as 10 degrees Celsius per minute. During the cooling process, the polarizing light plate and the analyzer of the polarizing microscope are used to observe the nucleation and growth process of the crystal inside the sample in real time. By connecting to a CCD camera of the microscope, a microscopic image of the sample is taken or a video is recorded at a set time interval, such as every 5 seconds. The image or video records the appearance of the crystal nucleus, the growth of the spherulite, and the evolution of the crystal morphology, such as the size, shape and distribution of the spherulite. The experiment at different cooling rates is repeated to examine the effect of the cooling rate on the crystal morphology. The obtained image will be used to analyze the nucleation mechanism and growth rate of the crystal.

[0062] The homogenized dried material prepared in step S11 is loaded onto the sample stage of the in-situ small angle X-ray scattering (SAXS) instrument. The sample stage is equipped with a precise temperature control system, and its temperature program setting is the same as the temperature program of the polarizing microscope in step S141, referring to the thermodynamic parameter set obtained in step S13. The SAXS instrument generates X-rays of a specific wavelength to irradiate the sample. The scattered X-rays are received by the detector to form a scattering spectrum. During the cooling process, the SAXS instrument collects the scattering spectrum in real time. By analyzing the position, intensity and shape of the scattering peaks in the scattering spectrum, the structural information of the crystal at the nanoscale can be obtained, such as the average size of the crystal, the interlayer spacing of the wafer, and the orientation of the crystal. The law of the change of the scattering spectrum with temperature reflects the evolution of the nanoscale structure during the crystallization process. The experiments at different cooling rates are repeated, and the data obtained will be used to study the influence of the cooling rate on the microstructure of the crystal.

[0063] Take the homogeneous dried material prepared in step S11 and use a rapid scanning calorimeter to perform non-isothermal crystallization analysis. Set a series of different cooling rates, for example, from 1 degree Celsius per second to 100 degrees Celsius per second. For each cooling rate, the sample is quickly heated to 10 degrees Celsius above its melting temperature and kept for enough time to eliminate the thermal history. Subsequently, the sample is quickly cooled at the set cooling rate, and the heat flow change of the sample during the cooling process is recorded. The rapid scanning calorimeter can accurately measure the exothermic peak in the rapid cooling process, which corresponds to the crystallization process of the plastic material. By analyzing the starting temperature, peak temperature, peak shape and integrated area of ​​the exothermic peak at different cooling rates, information such as the crystallization starting temperature, maximum crystallization rate and crystallinity at different cooling rates can be obtained. The obtained non-isothermal crystallization exothermic curve will be used for parameter fitting of the crystallization kinetic model.

[0064] Integrate the thermodynamic parameter set obtained in step S13, the crystal morphology evolution image obtained in step S141, the nanoscale structure evolution data obtained in step S142, and the non-isothermal crystallization exothermic curve obtained in step S143. Use data analysis software, such as Origin or Matlab, to correlate and analyze different experimental data. For example, compare the crystallization start temperature and crystallization peak shape in the DSC curve with the nucleation time and spherulite growth rate observed under a polarizing microscope to analyze the relationship between the crystallization start temperature and the nucleus density. Correlate the changes in crystal size and interlayer spacing obtained by SAXS with the changes in crystallinity in the DSC curve to study the effect of nanoscale structure evolution on macroscopic thermal behavior. Analyze the difference in non-isothermal crystallization behavior obtained by rapid scanning calorimetry and the crystal morphology under different cooling rates observed under a polarizing microscope to reveal the mechanism of the influence of cooling rate on the crystallization process. Through multi-angle data comparison and analysis, summarize the multi-scale crystallization behavior characteristics of the target plastic material, such as the nucleation mechanism, crystal growth mode, and the influence of cooling rate on the crystallization process.

[0065] According to the multi-scale crystallization behavior characteristics obtained in step S144, a suitable crystallization kinetics model is selected. If obvious nucleation and growth processes are observed, and the crystallization process is mainly controlled by temperature, the Avrami equation is selected. If it is necessary to describe the non-isothermal crystallization process and consider the influence of the cooling rate, the Nakamura model or its improved model is selected. According to the selected model, the model parameters are preliminarily estimated using the non-isothermal crystallization exothermic curve obtained in step S143. For example, for the Avrami equation, the Avrami index n and the crystallization rate constant k are preliminarily estimated by linearly fitting the crystallization fraction over time curve at different cooling rates. For the Nakamura model, the effective activation energy and pre-exponential factor of the model are preliminarily estimated based on experimental data. The initial parameter estimation can be completed by curve fitting using software such as Origin or Matlab.

[0066] Using the accurate non-isothermal crystallization exothermic curve obtained in step S143, the nonlinear least square method is used to perform parameter fitting and optimization on the crystallization kinetic model preliminarily selected in step S145. Using data fitting software, such as Origin or professional statistical analysis software SAS, the non-isothermal crystallization data obtained in the experiment are compared with the crystallization behavior predicted by the model, and the deviation between the crystallization exothermic curve predicted by the model and the experimental curve is minimized by adjusting the model parameters. During the optimization process, different optimization algorithms, such as Levenberg-Marquardt algorithm or genetic algorithm, can be tried, and a reasonable parameter search range and iteration termination condition are set. Through multiple iterations, a set of optimal model parameters are finally obtained, which can best describe the non-isothermal crystallization behavior measured in the experiment.

[0067] The crystallization kinetics model parameters optimized in step S146 are verified using the in-situ polarizing microscope crystallization observation results obtained in step S141 and the in-situ small-angle X-ray scattering structural analysis results obtained in step S142. The optimized model parameters are substituted into the crystallization kinetics equation to predict the crystallization morphology evolution and nanoscale structural changes under different cooling conditions. The information such as spherulite growth rate, crystal size change, crystal orientation predicted by the model is compared with the experimentally observed results. If the model prediction results are in good agreement with the experimental observation results, it indicates that the crystallization kinetics model and its parameters can accurately describe the crystallization behavior of the target plastic material. If there is a large deviation between the predicted results and the experimental results, it is necessary to re-evaluate the model selection or parameter fitting process until a satisfactory verification result is obtained, and finally determine the crystallization kinetics model parameters.

[0068] Preferably, step S2 comprises the following steps:

[0069] Step S21: construct a basic equation group according to the material gene spectrum to obtain an original control equation group;

[0070] Step S22: embedding the material constitutive relation into the original control equations according to the material gene spectrum to obtain the physical property correlation control equations;

[0071] Step S23: obtaining a mold geometry model; setting boundary conditions and initial conditions according to the mold geometry model and the physical property-related control equations to obtain a complete model boundary condition set;

[0072] Step S24: selecting a numerical discretization format according to the complete model boundary condition set and the physical property associated control equation group to obtain a discretized equation group;

[0073] Step S25: Designing and optimizing a solution algorithm according to the discretized equation group to obtain a multi-phase behavior model algorithm;

[0074] Step S26: Integrate and encapsulate the model code of the multi-phase behavior model algorithm to obtain the multi-phase behavior model.

[0075] In an embodiment of the present invention, according to the basic principles of fluid dynamics, heat transfer and phase change dynamics, a group of original control equations describing the behavior of the plastic melt during the injection molding process is constructed. First, for the flow behavior of the melt, the Navier-Stokes equations of incompressible viscous fluid are adopted, including the continuity equation and the momentum conservation equation, and the viscous stress term is considered in the momentum conservation equation. Secondly, for the heat transfer of the melt, the energy conservation equation is adopted, and the equation includes the heat conduction term, the convection term and the latent heat release term caused by the plastic phase change. For semi-crystalline plastics, an additional phase field evolution equation describing the crystallization process is constructed, such as the Avrami equation or the Nakamura equation based on the evolution of crystallinity, in which the crystallization rate is related to the temperature and shear rate. These groups of partial differential equations constitute the basic mathematical framework for describing the flow, heat transfer and crystallization behavior of plastic melts, laying the foundation for the subsequent embedded material constitutive relationship.

[0076] The various physical property models in the material gene spectrum obtained in step S1 are embedded into the original control equation group constructed in step S21. The viscous stress term in the momentum conservation equation is described by the Cross-WLF viscosity model calibrated in step S16, which can reflect the changes in melt viscosity with temperature, shear rate and pressure. The thermal conductivity and specific heat capacity in the energy conservation equation are thermodynamic parameters accurately measured in step S13. The crystallization rate constant in the phase field evolution equation uses the crystallization kinetic model parameters calibrated in step S16. In addition, the Tait state equation obtained in step S16 is used to establish the relationship between melt density and temperature and pressure, which is used to calculate the density terms in the momentum and energy conservation equations. By substituting these specific material constitutive relations and parameters into the original control equation group, the equation group can more accurately describe the behavior of the target plastic material in the injection molding process, forming a physical property correlation control equation group.

[0077] Obtain the 3D CAD model of the plastic mold to be analyzed, and use CAD software to extract geometric information such as mold cavity, pouring system, cooling water channel, etc. Set the model boundary conditions according to the injection molding process parameters. At the melt inlet, set the velocity inlet boundary conditions according to the injection speed or volume flow rate, and set the melt inlet temperature. At the mold wall, set the constant temperature boundary conditions or convection heat transfer boundary conditions according to the settings of the mold temperature control system, and the convection heat transfer coefficient is determined according to the properties and flow rate of the cooling medium. For symmetrical structures, set symmetrical boundary conditions to reduce the amount of calculation. The initial conditions are set to the temperature distribution and flow field state of the mold and melt at the beginning of the simulation. The initial temperature of the mold is usually set to the preheating temperature of the mold. The initial temperature of the melt can be set to the melt inlet temperature. The initial flow field state can be set to a static state. These boundary conditions and initial conditions will serve as necessary inputs for solving the physical property association control equations.

[0078] For the set of physical property correlation control equations obtained in step S22, the finite volume method is used for numerical discretization. For the flow control equation, the second-order upwind format is used to discretize the convection term to ensure the stability of the numerical solution and reduce numerical dissipation; the central difference format is used to discretize the diffusion term to ensure the calculation accuracy. For the energy conservation equation, the convection term is also discretized using the second-order upwind format, and the heat conduction term is discretized using the central difference format. For the phase field evolution equation, according to the specific form of the equation, select a suitable discretization format, such as an explicit or implicit format. The time term is discretized using a second-order backward difference format to ensure the accuracy of time advancement. The differential terms in the control equations are converted into algebraic difference equations to form a discretized set of equations, which can be numerically solved by a computer.

[0079] For the discretized equation group obtained in step S24, a multi-physics field coupling solution algorithm is designed. Due to the mutual influence between the flow field, temperature field and crystallization field, a separate solution strategy is adopted. In one time step, the momentum equation is first solved to obtain the velocity field and pressure field; then, the velocity field is substituted into the energy conservation equation to solve the temperature field; then, the temperature field is substituted into the phase field evolution equation to solve the crystallinity distribution. The solutions of different physical fields are coupled in an iterative manner until the set convergence criteria are met, such as the residual is less than the preset value. In order to improve the computational efficiency, parallel computing technology is used to decompose the computational domain into multiple subdomains and solve them in parallel on different computing cores. In addition, adaptive time step control is used to automatically adjust the time step according to the convergence during the calculation process to improve the computational efficiency and stability. Finally, a multiphase behavior model algorithm that can effectively solve the multi-physics field coupling problem is formed.

[0080] The multiphase behavior model algorithm designed in step S25 is implemented in code using high-level programming languages ​​such as Fortran or C++. The code is modularized, and the flow solver, heat transfer solver and crystallization solver are respectively encapsulated into independent modules for easy maintenance and expansion. Object-oriented programming ideas are adopted to define data structures describing material properties, boundary conditions and calculation grids. Parallel computing functions are implemented using parallel computing libraries such as MPI. The code is fully tested and verified to ensure the accuracy and stability of the calculation results. The written code is encapsulated into a dynamic link library or executable file, and a clear application program interface (API) is provided to facilitate integration and calling with other simulation software or user programs, and finally a multiphase behavior model that can be used for actual simulation is obtained.

[0081] Preferably, step S22 includes the following steps:

[0082] Step S221: extracting gene data from the material gene spectrum to obtain rheological gene data, thermodynamic gene data, crystallization gene data, microscopic characterization data and PVT data;

[0083] Step S222: constructing a viscoelastic constitutive model according to the rheological gene data and the original control equation group to obtain a viscoelastic model equation;

[0084] Step S223: Developing a multiphase constitutive model according to the thermodynamic gene data, the crystallization gene data and the original control equations to obtain a multiphase constitutive model;

[0085] Step S224: integrating microstructure information into the multiphase constitutive model according to the microscopic characterization data to obtain a microscopic modified constitutive model;

[0086] Step S225: refining the PVT state equation according to the PVT data and the original control equation group to obtain the precise PVT state equation;

[0087] Step S226: modularizing the physical property model code for the viscoelastic model equation, the microscopic modified constitutive model, and the precise PVT state equation to obtain an embeddable physical property model code;

[0088] Step S227: Perform physical property correlation on the embeddable physical property model code and the original control equation group to obtain a physical property correlated control equation group.

[0089] In an embodiment of the present invention, various material data required for constructing a constitutive model are extracted from the material gene spectrum database generated in step S1 according to predefined data fields and formats. Rheological gene data include viscosity data, dynamic modulus data, etc. at different temperatures, pressures and shear rates, and these data are derived from the multi-field rheological property scanning results of step S12. Thermodynamic gene data include specific heat capacity, thermal conductivity, glass transition temperature, melting temperature, etc., and these data are derived from the precise measurement results of thermodynamic properties in step S13. Crystallization gene data include crystallinity, crystallization rate constant, etc. at different cooling rates, and these data are derived from the crystallization kinetic behavior analysis results of step S14. Microscopic characterization data include in-situ polarizing microscope images, SAXS scattering spectra, etc., and these data are derived from the in-situ observation and analysis results of step S14. PVT data include specific volume or density data at different temperatures and pressures, and these data are derived from the precise characterization results of the PVT relationship in step S15. The various types of gene data extracted will serve as the basis for the subsequent construction of various physical property models.

[0090] According to the rheological gene data extracted in step S221, especially the dynamic modulus data, a constitutive model capable of describing the viscoelastic behavior of the plastic melt is constructed. The generalized Maxwell model is used to decompose the viscoelastic behavior of the melt into a superposition of multiple Maxwell units. Using data fitting software, such as RheoCompass or Igor Pro, the dynamic modulus data is fitted with the theoretical prediction of the generalized Maxwell model to determine the relaxation time and modulus parameters of each Maxwell unit. Alternatively, the Prony series model is used to represent the stress relaxation modulus as a superposition of multiple exponential decay terms, and the coefficients and relaxation time of the Prony series are determined by fitting stress relaxation experimental data or using the conversion relationship of dynamic modulus data. The constructed viscoelastic model equation can reflect the stress response of the plastic melt at different strain rates and will be embedded in the momentum conservation equation of the original control equation group of step S21.

[0091] For semi-crystalline plastics, a multiphase constitutive model capable of describing the coupling effects of flow, temperature and crystallization is developed based on the thermodynamic gene data and crystallization gene data extracted in step S221. Based on the viscoelastic model constructed in step S222, phase field variables describing the crystallization process, such as crystallinity, are introduced. The crystallization dynamics adopts the Nakamura model obtained in step S14, and its crystallization rate is related to temperature and shear rate. By constructing coupling equations, the flow field, temperature field and crystallization field are linked. For example, the shear heat generation term is added to the energy conservation equation, and the crystallization latent heat release term is also added to the energy conservation equation, and the shear rate and temperature affect the crystallization rate. The constructed multiphase constitutive model can describe the complex behavior of semi-crystalline plastics during the injection molding process and will be embedded in the original control equation group of step S21.

[0092] The multiphase constitutive model developed in step S223 is corrected using the microscopic characterization data extracted in step S221, such as in-situ polarizing microscope images and SAXS scattering patterns. Polarizing microscope images are analyzed to obtain crystal size and morphology information at different shear rates and temperatures, and this information is used to correct the nucleation rate and crystal growth rate in the crystallization kinetics model. SAXS scattering patterns are analyzed to obtain crystal orientation information at different shear rates and temperatures, and viscosity model correction terms related to molecular orientation are constructed and added to the viscoelastic model constructed in step S222. By incorporating microstructural information into the macroscopic constitutive model, the model can more accurately reflect the real physical behavior of the plastic material, and a microscopic corrected constitutive model is obtained.

[0093] According to the PVT data extracted in step S221, the PVT state equation that accurately describes the relationship between pressure, volume and temperature of the plastic material is refined. The Tait state equation is used, which can better describe the PVT behavior of the polymer melt. Using data fitting software, such as Origin or Matlab, the PVT data is fitted with the theoretical prediction of the Tait equation to determine the various parameters in the Tait equation, such as the compression coefficient and thermal expansion coefficient of the material. Alternatively, a more complex SAFT state equation is used, which can take into account the molecular structure and segment interactions of the polymer, thereby more accurately describing the PVT behavior. The constructed precise PVT state equation will be used to calculate the density term in the original control equation group in step S21.

[0094] The viscoelastic model equation constructed in step S222, the microscopic modified constitutive model constructed in step S224, and the PVT state equation refined in step S225 are written into independent, reusable code modules using high-level programming languages ​​such as Fortran or C++. Each physical property model module contains functions or subroutines for calculating physical property parameters such as material viscosity, stress, and density. Code writing follows the principle of modular design to improve the readability and maintainability of the code. Each module provides a clear input and output interface for easy calling in different simulation programs. For example, the input of the viscoelastic model module is temperature, shear rate, etc., and the output is viscosity value and stress tensor.

[0095] The embeddable physical property model code module generated in step S226 is integrated into the numerical solver of the original control equation group constructed in step S21. When solving the momentum conservation equation, the viscoelastic model code module is called to calculate the viscous stress term. When solving the energy conservation equation, the precise PVT state equation code module is called to calculate the density, and the thermodynamic parameters are called to calculate the thermal conductivity and specific heat capacity. When solving the phase field evolution equation, the crystallization kinetics model is called to calculate the crystallization rate. In this way, the physical property model describing the microscopic and macroscopic behavior of the material is closely combined with the control equation group to form a physical property correlation control equation group that can truly reflect the multi-physical field coupling effect of the plastic material during the injection molding process.

[0096] Preferably, step S3 comprises the following steps:

[0097] Step S31: parameterizing the mold geometry model to obtain mold geometry model parameters;

[0098] Step S32: performing fine discretization of the mold geometric model parameters to obtain a high-density discrete grid;

[0099] Step S33: accurately configuring simulation parameters according to the multi-phase behavior model and the high-density discrete grid to obtain refined simulation parameter configuration;

[0100] Step S34: performing multi-physics field coupling transient solution according to the refined simulation parameter configuration, multi-phase behavior model and high-density discrete grid to obtain original field variable time series data;

[0101] Step S35: performing fine extraction of cavity microscopic variables on the original field variable time series data to obtain a snapshot of microscopic variable distribution;

[0102] Step S36: integrating the micro-environment time series evolution of the micro-variable distribution snapshot to obtain a transient micro-environment data stream;

[0103] Step S37: Visualize and map the micro-environment data according to the transient micro-environment data stream and the high-density discrete grid to obtain a mold cavity microclimate map.

[0104] In an embodiment of the present invention, for the three-dimensional CAD model of the plastic mold to be analyzed, the parametric design function of the CAD software is used to parametrically define the key geometric feature dimensions of the mold. For example, the diameter, position, and spacing of the cooling water channel, the main channel and branch channel dimensions of the pouring system, the length, width, and depth of the cavity are all defined as adjustable design parameters. Each parameter is assigned a meaningful name and initial value, and its value range and variation step are set. The dependency relationship between the parameters is also established. For example, the spacing between multiple cooling water channels can be associated with the number of cooling water channels. The parameterized mold model can quickly generate different geometric variants by modifying the parameter values, providing a basis for subsequent parameter optimization. The parameterized information, including parameter names, initial values, value ranges, and relationships, is saved as parametric design space data.

[0105] Import the parameterized mold geometry model obtained in step S31 into meshing software, such as Ansys Meshing or HyperMesh. According to the simulation accuracy requirements, the mold model is finely meshed. For the cavity area, hexahedral meshes are used for division to improve the calculation accuracy; for complex geometric areas such as cooling channels and pouring systems, tetrahedral meshes are used for division to better adapt to the geometric shape. A boundary layer mesh is set near the wall to accurately capture the flow and heat transfer phenomena near the wall. The determination of the mesh size needs to be verified by mesh independence, that is, the mesh size is gradually reduced until the calculation results no longer change significantly with the reduction of the mesh size. Finally, a high-density discrete mesh containing millions or even tens of millions of mesh cells is generated, which contains the node coordinates and unit connection information of all areas of the mold geometry model.

[0106] Import the high-density discrete grid generated in step S32 into multi-physics simulation software, such as Ansys Fluent or COMSOL Multiphysics. According to the specific injection molding process parameters, the simulation model is configured with detailed parameters. The melt inlet boundary condition is set to the velocity inlet, the inlet velocity is calculated according to the injection rate, and the inlet temperature is set to the melt temperature. The mold wall boundary condition is set to a constant temperature boundary, and the temperature value is determined according to the set value of the mold temperature control system. The time step is set to a sufficiently small value, such as 0.001 seconds, to capture the transient change process. The convergence criterion is set to a residual less than the negative fourth power of 10 to ensure the accuracy of the calculation results. Select a suitable numerical solver and discrete format, such as the SIMPLE algorithm for pressure-velocity coupling and the second-order upwind format for convection terms. The multiphase behavior model obtained in step S26 is embedded in the simulation software and called as a material model. All simulation parameter configuration information is saved as a simulation example file.

[0107] Based on the simulation parameters configured in step S33 and the high-density discrete grid generated in step S32, the multi-physics field simulation software is started to perform transient numerical solution of the injection molding process. The solver will simultaneously solve the control equations of the flow field, temperature field and crystallization field, and calculate the values ​​of physical quantities such as velocity, pressure, temperature, and crystallinity of each grid unit inside the mold cavity at different time steps. The simulation process consumes a lot of computing resources and time, and usually requires parallel calculations on high-performance computing clusters. The simulation results are saved in the form of a time series, recording the changes of the physical field variables inside the mold cavity over time. The original field variable time series data contains the velocity vector, pressure value, temperature value and crystallinity value of each grid node at each time step.

[0108] The original field variable time series data obtained in step S34 is post-processed to extract the microscopic physical quantity information inside the mold cavity. The post-processing module of the simulation software or a custom post-processing program is used to calculate the microscopic variables such as the shear rate, cooling rate gradient, and crystallization rate of each grid unit. The shear rate is calculated based on the velocity gradient tensor. The cooling rate gradient is calculated based on the temperature difference and distance between adjacent grid units. The crystallization rate is directly extracted from the calculation results of the crystallization kinetic model. At a specific time point, such as the end of filling, the end of pressure holding, or the end of cooling, the distribution data of these microscopic variables in the entire mold cavity are extracted to form a snapshot of the distribution of microscopic variables. For example, a shear rate distribution cloud map, a cooling rate gradient distribution cloud map, and a crystallinity distribution cloud map inside the mold cavity at the end of filling can be obtained.

[0109] The snapshots of the distribution of microscopic variables at different time points extracted in step S35 are integrated in chronological order to construct a complete data stream of the evolution of the microscopic environment inside the mold cavity over time. The microscopic variable distribution data at different time points are organized and stored using post-processing software or custom scripts. For example, the shear rate distribution data at different times can be stored as a series of matrices or arrays and arranged in chronological order. The evolution of the microscopic variables in the mold cavity can be displayed in the form of animations or time series graphs. For example, an animation showing the change in shear rate distribution during the filling process can be generated, or a curve showing the change in temperature over time at a specific position in the mold cavity can be generated. The transient microscopic environment data stream records the changing trajectory of the microscopic physical quantities inside the mold cavity during the entire molding process.

[0110] Using the post-processing module of the simulation software, such as Ansys CFD-Post or Tecplot, the transient micro-environment data stream obtained in step S36 is mapped to the high-density discrete grid generated in step S32 for visualization. Microscopic variable distribution cloud maps at different times can be generated, such as temperature distribution cloud maps, pressure distribution cloud maps, crystallinity distribution cloud maps, shear rate distribution cloud maps, and cooling rate distribution cloud maps. The size of the microscopic variables is represented by color depth or contour lines. An animation can be generated to show the evolution of microscopic variables over time. Microscopic variable data of a specific section or a specific position can also be extracted for curve drawing. The mold cavity microclimate map finally generated intuitively shows the microscopic physical environment inside the mold cavity, providing a basis for subsequent mold design optimization.

[0111] Preferably, step S4 comprises the following steps:

[0112] Step S41: acquiring target part performance requirement data; performing performance index quantification mapping on the target part performance requirement data according to the mold cavity microclimate map to obtain a computable performance index set;

[0113] Step S42: parametrically define the design variables of the mold geometric model parameters to obtain a parametric design space;

[0114] Step S43: constructing an optimization objective function according to the target part performance requirement data to obtain a multi-objective optimization function;

[0115] Step S44: using the multi-phase behavior model to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain design performance evaluation data;

[0116] Step S45: performing an optimization algorithm iteration search based on the multi-objective optimization function, the parameterized design space, and the design solution performance evaluation data to obtain a candidate optimization parameter set;

[0117] Step S46: performing topological structure evolution and reconstruction on the mold geometry model parameters according to the candidate optimization parameter set to obtain a topological evolution model;

[0118] Step S47: verify and screen the performance solution of the topology evolution model to obtain a performance topology solution.

[0119] In an embodiment of the present invention, the design specifications and performance requirements of the target plastic parts, such as strength, warpage, surface roughness, etc., are obtained. In view of the strength requirements, the residual stress distribution data of the part at the final molding moment is extracted from the mold cavity microclimate diagram, the maximum Von-Mises stress value of the part is calculated, and compared with the yield strength of the material as a strength performance indicator. In view of the warpage requirements, the geometric deviation data of the part from the original CAD model after cooling is completed is extracted, and the maximum deformation of the key parts of the part is calculated as a warpage performance indicator. In view of the surface roughness requirements, the temperature fluctuation and cooling rate gradient data near the mold cavity wall are extracted, and an empirical formula or proxy model between the temperature fluctuation and cooling rate gradient and the surface roughness is established, and the surface roughness value of the part is calculated as a surface quality performance indicator. These calculable performance indicators will be used for subsequent optimization evaluation.

[0120] For the parameterized mold geometry model in step S31, the design variables that need to be optimized are clearly defined. In the cooling system design, the diameter of the cooling water channel varies in the range of 3 mm to 8 mm with a step length of 1 mm, the spacing of the cooling water channel varies in the range of 10 mm to 30 mm with a step length of 2 mm, and the arrangement of the cooling water channel is defined as series, parallel, or a combination of series and parallel. In the pouring system design, the main channel diameter varies in the range of 4 mm to 10 mm with a step length of 1 mm, the branch channel diameter varies in the range of 2 mm to 6 mm with a step length of 0.5 mm, and the gate type is defined as a point gate, a fan gate, or a membrane gate. The combination of these design variables constitutes a parametric design space that can be explored by the optimization algorithm.

[0121] According to the performance indicators quantified in step S41, a multi-objective optimization function is constructed. Minimizing the warpage of parts and maximizing the strength of parts are the main optimization goals, while considering shortening the molding cycle. The objective function is defined as the weighted sum of various performance indicators, and the weight coefficient is determined according to the importance of each performance indicator. For example, the weight coefficient of warpage is set to 0.5, the weight coefficient of strength is set to 0.4, and the weight coefficient of molding cycle is set to 0.1. For constraints, such as the maximum stress value of the part must be less than the yield strength of the material, and the warpage must be less than the set tolerance range, these constraints are added to the optimization function and treated as penalty terms or constraints. The constructed multi-objective optimization function will guide the search direction of the optimization algorithm.

[0122] For different mold design schemes in the parametric design space defined in step S42, the multiphase behavior model constructed in step S2 is used to simulate the injection molding process, and the performance is evaluated using the cavity microclimate map generated in step S3. For each design scheme, after the simulation is completed, the residual stress distribution, warpage deformation, and cavity wall temperature distribution of the parts are extracted. According to the quantitative mapping relationship of the performance indicators established in step S41, the performance indicator values ​​such as strength, warpage deformation, and surface roughness corresponding to the design scheme are calculated. The simulation calculations are performed in parallel on a high-performance computing cluster to shorten the evaluation time. The performance evaluation data of the design scheme is recorded for subsequent optimization algorithm iterations.

[0123] A genetic algorithm is used to iteratively optimize the multi-objective optimization function constructed in step S43. The parameterized design space defined in step S42 is used as the search space of the genetic algorithm, and the design variables are used as genes. The initial population size is 100, the crossover probability is set to 0.8, and the mutation probability is set to 0.05. According to the performance evaluation data obtained in step S44, the fitness value of each individual is calculated. A new population is generated through genetic operations such as selection, crossover and mutation. Repeat the iteration until the maximum number of iterations, such as 100 generations, is reached, or a solution that meets the performance requirements is found. The optimization algorithm outputs a Pareto optimal solution set, which contains several groups of mold design parameter combinations with high performance potential as candidate optimization parameter sets.

[0124] For the candidate optimization parameter set obtained in step S45, for the parts involving topology optimization, such as the layout of cooling water channels, the topology optimization module of the CAD software is used to perform structural evolution and reconstruction. According to the cooling water channel centerline or temperature distribution target provided by the optimization algorithm, materials are added or removed inside the mold model to automatically generate a new cooling water channel topology structure. For structural changes that cannot be directly achieved through topology optimization, such as the runner shape of the pouring system, the CAD model is manually adjusted according to the optimization parameters. After topological structure evolution and reconstruction, a series of mold models with different cooling and pouring system layouts are obtained as topological evolution models.

[0125] The topology evolution model obtained in step S46 is simulated with high precision using the multiphase behavior model constructed in step S2, and the performance is verified according to the performance indicators defined in step S41. The simulation results of different topology structure models are compared to screen out the optimal design scheme that meets the performance requirements and has good robustness. Robustness evaluation is achieved through sensitivity analysis of process parameter fluctuations. The final mold design parameters, including geometric dimensions, cooling system layout, pouring system design, etc., are determined as the performance topology scheme, which will serve as the basis for the subsequent generation of the intelligent mold blueprint.

[0126] Preferably, step S44 includes the following steps:

[0127] Step S441: constructing a micro-variable-performance indicator association model according to the mold cavity microclimate map and the computable performance indicator set to obtain a micro-variable-performance indicator mapping model;

[0128] Step S442: generating a design solution microclimate map for the parameterized design space using the multi-phase behavior model to obtain a design solution microclimate map set;

[0129] Step S443: Calculate the performance index driven by the micro-environment for the design scheme microclimate atlas using the micro-variable-performance index mapping model to obtain the design scheme performance evaluation data.

[0130] In an embodiment of the present invention, a plurality of cavity microclimate diagrams generated in step S3 and a computable performance index set calculated in step S41 are collected. Statistical analysis software, such as SPSS or R, is used to analyze the correlation between microscopic variables (e.g., residual stress, crystallinity, temperature gradient) and part performance indicators (e.g., strength, warpage) in the cavity microclimate diagram. A multivariate linear regression method is used to establish a linear regression model with residual stress, crystallinity and temperature gradient as independent variables and part strength as dependent variable to determine the influence coefficient of each microscopic variable on strength. A neural network method is used to construct a feedforward neural network including an input layer, a hidden layer and an output layer, with the cooling rate distribution as input and the part warpage as output. The existing data is used for training to obtain a nonlinear mapping relationship between the cooling rate distribution and the warpage. The constructed microscopic variable-performance index mapping model can quickly predict part performance based on the cavity microclimate diagram.

[0131] For the parameterized design space defined in step S42, select representative design schemes, for example, select 100 design points using the Latin hypercube sampling method. For each design scheme, the multiphase behavior model constructed in step S2 is used to simulate the injection molding process. The simulation parameter configuration refers to the operation of step S33. After the simulation is completed, the temperature distribution, pressure distribution, shear rate distribution, crystallinity distribution and other data inside the mold cavity are extracted to generate a mold cavity microclimate map corresponding to the design scheme. The simulation calculations are performed in parallel on a high-performance computing cluster to shorten the calculation time. The mold cavity microclimate map data of all design schemes are stored to form a design scheme microclimate atlas, which will be used for subsequent performance indicator calculations.

[0132] For the mold cavity microclimate map of each design scheme generated in step S442, the micro-variable-performance index mapping model constructed in step S441 is used to calculate the part performance index corresponding to the design scheme. For example, for the mold cavity microclimate map of a certain design scheme, the residual stress distribution, crystallinity distribution and temperature gradient distribution data at the final molding moment of the part are extracted, and input into the multivariate linear regression model constructed in step S441 to calculate the strength prediction value of the design scheme. The cooling rate distribution data of the design scheme is extracted and input into the neural network model constructed in step S441 to calculate the warpage deformation prediction value of the design scheme. The performance index prediction values ​​of all design schemes are recorded to form design scheme performance evaluation data, which will be used for subsequent optimization algorithm iterations.

[0133] Preferably, step S5 comprises the following steps:

[0134] Step S51: performing parameterized reconstruction of the optimized model according to the performance topology solution to obtain an optimized parameterized CAD model;

[0135] Step S52: Integrate the mold cavity microclimate map into the optimized parameterized CAD model, perform information annotation, and obtain a physical information enhanced CAD model;

[0136] Step S53: embedding material gene information into the physical information enhanced CAD model according to the material gene spectrum to obtain a material property enriched CAD model;

[0137] Step S54: generating intelligent processing strategies based on the material property enriched CAD model and the mold cavity microclimate map to obtain intelligent processing guidance information;

[0138] Step S55: digital model integration and standardization of the material property enriched CAD model and intelligent processing guidance information to obtain a manufacturable digital model package;

[0139] Step S56: organize and output the blueprint information of the manufacturable digital model package to obtain the intelligent mold blueprint.

[0140] In an embodiment of the present invention, according to the performance topology solution outputted in step S47, the geometric model of the mold is reconstructed in a three-dimensional CAD software, such as Siemens NX or CATIA. For the topologically optimized parts such as cooling channels and pouring systems, their three-dimensional entities are accurately created based on the centerline data or geometric shapes provided by the optimization results. For the parameter-optimized parts, such as the diameter and spacing of the cooling channels, the optimized parameter values ​​are directly assigned to the corresponding parametric features. The reconstructed CAD model retains the parametric features to facilitate subsequent modification and adjustment. The optimized parametric CAD model accurately reflects the design intent of the performance topology solution.

[0141] The key simulation data in the cavity microclimate map generated in step S3, such as temperature distribution, pressure distribution, crystallinity distribution and cooling rate distribution, are integrated into the optimized parameterized CAD model reconstructed in step S51. Using the attribute management function of the CAD software, attribute information is added to the cooling channel surface of the model, and the average temperature value and maximum temperature gradient of the surface are marked. In key areas of the cavity, such as near the gate and the last filling area, the final crystallinity value and residual stress value of the area are marked. Using the color mapping function, the temperature distribution, pressure distribution and crystallinity distribution are intuitively displayed on the mold model surface in the form of a color cloud map. The physical information enhanced CAD model intuitively shows the performance of the mold during the injection molding process.

[0142] The key material parameter information in the material gene spectrum generated in step S1 is embedded into the physical information enhanced CAD model of step S52. The material library management function of the CAD software is used to specify precise material properties for different components of the mold. For example, the material of the cavity insert is set to high-strength mold steel, and its thermal conductivity, specific heat capacity, elastic modulus, Poisson's ratio and other material parameters are imported. The material of the cooling water channel is set to stainless steel, and its corresponding material parameters are imported. These material parameters are directly derived from the material gene spectrum database. The material property enriched CAD model contains the precise material information of all components of the mold.

[0143] Based on the material property enriched CAD model of step S53 and the cavity microclimate map of step S3, intelligent processing guidance information is generated. Different cutting parameters are recommended for different areas of the mold cavity according to their predicted temperature distribution and material properties. For example, in high-temperature areas, lower cutting speeds and smaller feed rates are recommended to reduce thermal deformation. In low-stress areas, higher cutting speeds can be used to improve processing efficiency. For complex structures such as cooling channels, suitable tool types and processing paths are recommended based on their geometric shapes and material properties. Intelligent processing guidance information is added to the CAD model in the form of text or annotations.

[0144] Integrate the material property enriched CAD model of step S53 and the intelligent processing guidance information of step S54, and perform format conversion and data packaging according to manufacturing standards. Export the CAD model to a common 3D model format such as STEP or Parasolid to ensure that different CAM software can read it. Package the intelligent processing guidance information with the model file in the form of a PDF document or text file. Add metadata to the model file, including indexes of material information, simulation information, and processing guidance information, to facilitate manufacturing personnel to find and use. The manufacturable digital model package contains all the necessary information for mold manufacturing.

[0145] All information in the manufacturable digital model package generated in step S55 is organized and sorted to generate the final intelligent mold blueprint. The drawing module of the CAD software is used to generate a traditional engineering drawing containing a three-dimensional view of the mold, a parts list, assembly relationships, key dimensions and tolerances. The integrated simulation results, such as temperature distribution cloud maps, pressure distribution cloud maps and crystallinity distribution cloud maps, are added to the blueprint as a basis for design. The intelligent processing guidance information is added to the corresponding part drawings in the form of annotations or tables. The intelligent mold blueprint is output in a digital form, such as a PDF document or an interactive three-dimensional model, which is convenient for manufacturing personnel to consult and use at the production site.

[0146] Preferably, the present invention further provides a parametric simulation model design system for a plastic mold, which is used to execute the parametric simulation model design method for a plastic mold as described above, and the parametric simulation model design system for a plastic mold comprises:

[0147] The plastic physical property analysis module is used to perform fine preparation of the target plastic raw materials to obtain homogeneous dry materials; perform multi-field rheological property scanning on the homogeneous dry materials, and extract viscosity model parameters to obtain initial viscosity model parameters; perform precise measurement of the thermodynamic properties of the plastic material on the homogeneous dry materials, and perform crystallization kinetic behavior analysis to obtain crystallization kinetic model parameters; perform material gene spectrum integration based on the initial viscosity model parameters and crystallization kinetic model parameters to obtain the material gene spectrum;

[0148] The physical field coupling model construction module is used to construct a group of physical property correlation control equations according to the material gene spectrum to obtain a group of physical property correlation control equations; obtain a mold geometry model; set the boundary conditions of the complete model according to the mold geometry model and the group of physical property correlation control equations to obtain a complete model boundary condition set; construct a multiphase behavior model according to the complete model boundary condition set to obtain a multiphase behavior model;

[0149] The micro-environment mapping simulation module is used to parameterize the mold geometry model to obtain the mold geometry model parameters; perform multi-physical field coupling transient solution based on the multi-phase behavior model and the mold geometry model parameters to obtain the original field variable time series data; perform micro-environment mapping based on the original field variable time series data to obtain the mold cavity microclimate map;

[0150] The performance topology optimization module is used to obtain the performance requirement data of the target parts, and to construct the optimization objective function to obtain the multi-objective optimization function; the multi-phase behavior model is used to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain the performance evaluation data of the design scheme; the performance topology optimization is performed based on the performance evaluation data of the design scheme and the mold geometry model parameters to obtain the performance topology scheme;

[0151] The intelligent mold blueprint generation module is used to perform parametric reconstruction of the optimized model according to the performance topology solution to obtain an optimized parametric CAD model; generate intelligent processing strategies for the optimized parametric CAD model according to the mold cavity microclimate map and the material gene spectrum to obtain intelligent processing guidance information; organize and output blueprint information according to the intelligent processing guidance information to obtain an intelligent mold blueprint.

Claims

1. A parametric simulation model design method for plastic molds, characterized in that: The following steps are included Step S1: finely prepare the target plastic raw material to obtain a homogeneous dry material; perform multi-field rheological property scanning on the homogeneous dry material, and extract viscosity model parameters to obtain initial parameters of the viscosity model; The thermodynamic properties of the plastic material are accurately measured on the homogeneous dry material, and the crystallization kinetics behavior is analyzed to obtain the crystallization kinetics model parameters; The material gene spectrum is integrated according to the initial parameters of the viscosity model and the parameters of the crystallization kinetics model to obtain the material gene spectrum; Step S2: constructing a set of physical property correlation control equations according to the material gene spectrum to obtain a set of physical property correlation control equations; Acquire the mold geometry model; set the complete model boundary conditions according to the mold geometry model and the physical property related control equation group to obtain the complete model boundary condition set; construct the multiphase behavior model according to the complete model boundary condition set to obtain the multiphase behavior model; Step S3: parameterizing the mold geometry model to obtain mold geometry model parameters; performing multi-physics field coupling transient solution according to the multi-phase behavior model and the mold geometry model parameters to obtain original field variable time series data; Based on the original field variable time series data, micro-environment mapping is performed to obtain the mold cavity microclimate map; Step S4: Obtaining target part performance requirement data, and constructing an optimization objective function to obtain a multi-objective optimization function; The multi-phase behavior model is used to evaluate the performance of the multi-objective optimization function based on microclimate, and the performance evaluation data of the design scheme is obtained; Perform performance topology optimization based on the design performance evaluation data and mold geometry model parameters to obtain a performance topology solution; Step S5: parametrically reconstruct the optimized model according to the performance topology solution to obtain an optimized parametric CAD model; generate an intelligent processing strategy for the optimized parametric CAD model according to the mold cavity microclimate map and the material gene spectrum to obtain intelligent processing guidance information; organize and output blueprint information according to the intelligent processing guidance information to obtain an intelligent mold blueprint.

2. The parametric simulation model design method for plastic mold according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: finely preparing the target plastic raw material to obtain a homogeneous dry material; Step S12: performing multi-field rheological property scanning on the homogenized dry material to obtain an original rheological curve set; Step S13: extracting viscosity model parameters from the original rheological curve set to obtain initial parameters of the viscosity model; accurately measuring the thermodynamic properties of the plastic material on the homogeneous dry material to obtain a thermodynamic parameter set; Step S14: performing crystallization kinetic behavior analysis based on the thermodynamic parameter set and the homogenized dried material to obtain crystallization kinetic model parameters; Step S15: Use a PVT tester to accurately characterize the PVT relationship of the homogenized dry material to obtain a PVT relationship data table; Step S16: Perform comprehensive calibration of model parameters on the viscosity model initial parameters, thermodynamic parameter set, crystallization kinetics model parameters and PVT relationship data table to obtain a calibrated model parameter set; perform material gene spectrum integration on the calibrated model parameter set to obtain a material gene spectrum.

3. The parametric simulation model design method for plastic mold according to claim 3, characterized in that: Step S14 includes the following steps: Step S141: performing in-situ polarizing microscope crystallization observation on the homogenized dried material according to the thermodynamic parameter set to obtain a crystal morphology evolution image; Step S142: performing in-situ small-angle X-ray scattering structural analysis on the homogenized dried material according to the thermodynamic parameter set to obtain nanoscale structural evolution data; Step S143: performing a rapid scanning calorimetry non-isothermal crystallization analysis on the homogenized dried material according to the thermodynamic parameter set to obtain a non-isothermal crystallization exothermic curve; Step S144: performing a correlation analysis between thermodynamic parameters and microstructure on the thermodynamic parameter set, crystal morphology evolution image, nanoscale structure evolution data, and non-isothermal crystallization exothermic curve to obtain multi-scale crystallization behavior characteristics; Step S145: Selecting a crystallization kinetics model and preliminarily estimating parameters according to the multi-scale crystallization behavior characteristics to obtain preliminary model parameters and model type; Step S146: using the non-isothermal crystallization exothermic curve to perform model parameter fitting and optimization on the preliminary model parameters and model type to obtain optimized model parameters; Step S147: performing crystallization kinetics model verification on the optimized model parameters to obtain crystallization kinetics model parameters.

4. The parametric simulation model design method for plastic mold according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: construct a basic equation group according to the material gene spectrum to obtain an original control equation group; Step S22: embedding the material constitutive relation into the original control equations according to the material gene spectrum to obtain the physical property correlation control equations; Step S23: obtaining a mold geometry model; setting boundary conditions and initial conditions according to the mold geometry model and the physical property-related control equations to obtain a complete model boundary condition set; Step S24: selecting a numerical discretization format according to the complete model boundary condition set and the physical property associated control equation group to obtain a discretized equation group; Step S25: Designing and optimizing a solution algorithm according to the discretized equation group to obtain a multi-phase behavior model algorithm; Step S26: Integrate and encapsulate the model code of the multi-phase behavior model algorithm to obtain the multi-phase behavior model.

5. The parametric simulation model design method for plastic mold according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: extracting gene data from the material gene spectrum to obtain rheological gene data, thermodynamic gene data, crystallization gene data, microscopic characterization data and PVT data; Step S222: constructing a viscoelastic constitutive model according to the rheological gene data and the original control equation group to obtain a viscoelastic model equation; Step S223: Developing a multiphase constitutive model according to the thermodynamic gene data, the crystallization gene data and the original control equations to obtain a multiphase constitutive model; Step S224: integrating microstructure information into the multiphase constitutive model according to the microscopic characterization data to obtain a microscopic modified constitutive model; Step S225: refining the PVT state equation according to the PVT data and the original control equation group to obtain the precise PVT state equation; Step S226: modularizing the physical property model code for the viscoelastic model equation, the microscopic modified constitutive model, and the precise PVT state equation to obtain an embeddable physical property model code; Step S227: Perform physical property correlation on the embeddable physical property model code and the original control equation group to obtain a physical property correlated control equation group.

6. The parametric simulation model design method for plastic mold according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: parameterizing the mold geometry model to obtain mold geometry model parameters; Step S32: performing fine discretization of the mold geometric model parameters to obtain a high-density discrete grid; Step S33: accurately configuring simulation parameters according to the multi-phase behavior model and the high-density discrete grid to obtain refined simulation parameter configuration; Step S34: performing multi-physics field coupling transient solution according to the refined simulation parameter configuration, multi-phase behavior model and high-density discrete grid to obtain original field variable time series data; Step S35: performing fine extraction of cavity microscopic variables on the original field variable time series data to obtain a snapshot of microscopic variable distribution; Step S36: integrating the micro-environment time series evolution of the micro-variable distribution snapshot to obtain a transient micro-environment data stream; Step S37: Visualize and map the micro-environment data according to the transient micro-environment data stream and the high-density discrete grid to obtain a mold cavity microclimate map.

7. The parametric simulation model design method for plastic mold according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: acquiring target part performance requirement data; performing performance index quantification mapping on the target part performance requirement data according to the mold cavity microclimate map to obtain a computable performance index set; Step S42: parametrically define the design variables of the mold geometric model parameters to obtain a parametric design space; Step S43: constructing an optimization objective function according to the target part performance requirement data to obtain a multi-objective optimization function; Step S44: using the multi-phase behavior model to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain design performance evaluation data; Step S45: performing an optimization algorithm iteration search based on the multi-objective optimization function, the parameterized design space, and the design solution performance evaluation data to obtain a candidate optimization parameter set; Step S46: performing topological structure evolution and reconstruction on the mold geometry model parameters according to the candidate optimization parameter set to obtain a topological evolution model; Step S47: verify and screen the performance solution of the topology evolution model to obtain a performance topology solution.

8. The parametric simulation model design method for plastic mold according to claim 7, characterized in that: Step S44 includes the following steps: Step S441: constructing a micro-variable-performance indicator association model according to the mold cavity microclimate map and the computable performance indicator set to obtain a micro-variable-performance indicator mapping model; Step S442: generating a design solution microclimate map for the parameterized design space using the multi-phase behavior model to obtain a design solution microclimate map set; Step S443: Calculate the performance index driven by the micro-environment for the design scheme microclimate atlas using the micro-variable-performance index mapping model to obtain the design scheme performance evaluation data.

9. The parametric simulation model design method for plastic mold according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing parameterized reconstruction of the optimized model according to the performance topology solution to obtain an optimized parameterized CAD model; Step S52: Integrate the mold cavity microclimate map into the optimized parameterized CAD model, perform information annotation, and obtain a physical information enhanced CAD model; Step S53: embedding material gene information into the physical information enhanced CAD model according to the material gene spectrum to obtain a material property enriched CAD model; Step S54: generating intelligent processing strategies based on the material property enriched CAD model and the mold cavity microclimate map to obtain intelligent processing guidance information; Step S55: digital model integration and standardization of the material property enriched CAD model and intelligent processing guidance information to obtain a manufacturable digital model package; Step S56: organize and output the blueprint information of the manufacturable digital model package to obtain the intelligent mold blueprint.

10. A parametric simulation model design system for plastic molds, characterized in that: Used to execute the parametric simulation model design method for plastic molds as claimed in claim 1, the parametric simulation model design system for plastic molds comprises: The plastic physical property analysis module is used to perform fine preparation of the target plastic raw materials to obtain homogeneous dry materials; perform multi-field rheological property scanning on the homogeneous dry materials, and extract viscosity model parameters to obtain initial viscosity model parameters; perform precise measurement of the thermodynamic properties of the plastic material on the homogeneous dry materials, and perform crystallization kinetic behavior analysis to obtain crystallization kinetic model parameters; perform material gene spectrum integration based on the initial viscosity model parameters and crystallization kinetic model parameters to obtain the material gene spectrum; The physical field coupling model construction module is used to construct a group of physical property correlation control equations according to the material gene spectrum to obtain a group of physical property correlation control equations; obtain a mold geometry model; set the boundary conditions of the complete model according to the mold geometry model and the group of physical property correlation control equations to obtain a complete model boundary condition set; construct a multiphase behavior model according to the complete model boundary condition set to obtain a multiphase behavior model; The micro-environment mapping simulation module is used to parameterize the mold geometry model to obtain the mold geometry model parameters; perform multi-physical field coupling transient solution based on the multi-phase behavior model and the mold geometry model parameters to obtain the original field variable time series data; perform micro-environment mapping based on the original field variable time series data to obtain the mold cavity microclimate map; The performance topology optimization module is used to obtain the performance requirement data of the target parts, and to construct the optimization objective function to obtain the multi-objective optimization function; the multi-phase behavior model is used to perform microclimate-based performance evaluation on the multi-objective optimization function to obtain the performance evaluation data of the design scheme; the performance topology optimization is performed based on the performance evaluation data of the design scheme and the mold geometry model parameters to obtain the performance topology scheme; The intelligent mold blueprint generation module is used to perform parametric reconstruction of the optimized model according to the performance topology solution to obtain an optimized parametric CAD model; generate intelligent processing strategies for the optimized parametric CAD model according to the mold cavity microclimate map and the material gene spectrum to obtain intelligent processing guidance information; organize and output blueprint information according to the intelligent processing guidance information to obtain an intelligent mold blueprint.

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