Multi-process complex loading process material deformation and performance unified prediction method and application thereof

Abstract

The invention belongs to the technical field of material processing and manufacturing, and particularly relates to a multi-process complex loading process material deformation and performance unified prediction method and application thereof. Aiming at the technical defects of an existing material constitutive model in the aspects of cross-process applicability, multi-process historical effect characterization and formation-structure-performance whole-process unified evaluation, a unified internal variable system is constructed; by combining a multi-process unified constitutive theory, a multi-process composite loading path calibration method and a multi-data source collaborative parameter identification and simulation integration technology, unified prediction of deformation behaviors, structure evolution and post-forming performance of materials under different forming processes, different loading modes and complex historical paths is realized. Compared with the prior art, the method of the invention makes significant technical progress in the aspects of model versatility, prediction precision and engineering implementation convenience.

Description

Technical Field

[0001] This invention belongs to the field of materials processing and manufacturing technology, specifically relating to a unified prediction method for material deformation and properties under complex loading processes and its application. Background Technology

[0002] With the widespread application of high-performance structural components, complex hollow parts, and large integral parts in industries such as aerospace, energy and power, and transportation, these parts often undergo a comprehensive deformation process involving multiple processes and multiple paths. For example, this includes multiple hot forming processes combined with intermediate heat treatment and complex loading conditions. The microstructure evolution of materials under such conditions (e.g., dislocation density evolution, dynamic recovery, dynamic / static recrystallization, porosity damage, grain growth, etc.) and macroscopic properties (strength, plasticity, etc.) are highly coupled. Traditional constitutive models established under a single process and a single loading path are insufficient to meet the engineering requirements for accurate prediction of "multiple processes, the entire process, and the entire path".

[0003] In existing technologies, commonly used constitutive models in engineering mainly include empirical or semi-empirical flow stress models (such as the Johnson-Cook and Arrhenius models) and microstructure evolution models developed for specific processes (such as those applicable to hot pressing or creep processes). These models are usually fitted based on experimental data under specific temperatures, strain rates, and loading methods, and their applicability is limited. When the process flow changes (such as changing the hot forming route, adding multiple processes, or adding multiple forming passes) or the loading path becomes more complex, the model often needs to be recalibrated or even rebuilt, making it difficult to achieve a "transferable and unified" description across multiple processes.

[0004] On the other hand, with the promotion of numerical simulation and digital factories, numerical methods such as finite element / finite volume are widely used in forming simulation and performance prediction. To achieve virtual simulation of the entire process from material forming to service performance, a unified constitutive modeling method is needed that can adapt to different process windows and loading paths while taking into account macroscopic deformation, microstructure evolution, and forming performance. However, existing constitutive modeling techniques often perform well in a certain aspect (e.g., flow stress prediction during forming) or a certain process (e.g., creep or hot forming prediction), but it is difficult to simultaneously meet the needs of process diversity and unified performance prediction in a single model. Summary of the Invention

[0005] In view of this, and in order to solve the problems existing in the prior art, the present invention provides a material constitutive modeling method for multi-process and composite loading path conditions. The present invention describes the deformation behavior and performance evolution of materials under different processes and loading paths within a unified framework, providing a unified material constitutive model and performance prediction method for multi-stage processes such as forging, rolling, hot pressing, hot torsion, hot expansion, heat treatment, and hot creep.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A unified prediction method for material deformation and properties under complex loading processes involving multiple processes is proposed. This method achieves integrated prediction of material deformation behavior / microstructure and forming properties under different processes and loading modes by designing a unified internal variable system, a unified method for multiple processes, a calibration method for multi-process composite loading paths, and a collaborative parameter identification and simulation integration technology from multiple data sources.

[0007] It is worth noting that existing material constitutive modeling and performance prediction technologies have the following main shortcomings: (1) The model has limited applicability and is difficult to cope with the superposition of multiple processes and multiple paths.

[0008] Most constitutive models are established under single-process conditions (such as single-pass hot compression, single-strain-rate tension, and single-stress-level creep), and can only maintain high accuracy under conditions similar to experimental conditions. When the processing route involves multiple processes in series (such as hot pressing-creep forming), multiple loading passes, or complex loading paths, the parameters of traditional models are no longer applicable.

[0009] (2) It is difficult to predict deformation behavior and service performance in a unified manner under the same framework.

[0010] Existing technologies often separate the "flow behavior during forming" from the "performance evolution during service": a deformation constitutive model is used in the forming stage, while the forming performance is predicted through experimental methods or microstructure during service. This not only requires a large number of experiments but also leads to a waste of human and material resources. It lacks a clear transmission relationship in the causal chain of process-microstructure-performance, making it difficult to truly achieve full-process prediction of "deformation-microstructure-performance".

[0011] (3) Insufficient description of complex loading paths and historical dependency effects.

[0012] Under multi-stage deformation (deformation-cooling-re-deformation), complex process paths, or multiple deformation paths (high temperature and high strain rate followed by low temperature and low strain rate with varying temperature and strain rate), the stress-strain response and microstructure evolution of materials exhibit significant historical dependence. Existing constitutive models typically use simple equivalent variables or assume fixed loading paths for fitting, which are insufficient in describing the sensitivity to path changes and historical effects, leading to a significant increase in prediction errors under real multi-path processes.

[0013] (4) The physical meaning of the parameters is weak and the calibration cost is high.

[0014] Many empirical or semi-empirical constitutive model parameters have no physical meaning; they simply predict macroscopic deformation behavior without characterizing the mechanism or predicting the microstructure. They only predict the shape and cannot characterize the microstructure of the formed part, which makes it impossible to clearly show the forming quality.

[0015] In summary, existing technologies lack a material constitutive modeling method that considers both the microscopic evolution mechanism of materials and the multi-process and composite loading path, and can simultaneously complete the deformation process and performance prediction of multiple processes within a unified framework.

[0016] Based on the above problems, this invention proposes a material constitutive modeling method for "unified prediction of deformation and performance under multi-process composite loading paths". By designing a unified internal variable system, a unified method for multiple processes, a calibration method for multi-process composite loading paths, and a multi-data source collaborative parameter identification and simulation integration technology, it realizes the integrated prediction of the deformation behavior / microstructure and forming performance of materials under different processes and loading modes.

[0017] Furthermore, the unified internal variable system includes phase transitions, dislocation density, dynamic recrystallization behavior, grain size, or damage variables.

[0018] Furthermore, the multi-process unification method includes using the Arrhenius formula to unify the evolution behavior of process parameters at different temperatures; using equations such as hyperbolic sine functions and exponential functions in combination with phase proportions to unify flow behavior; and using stress threshold theory (i.e., back stress theory) to unify hot-pressing deformation and creep deformation behavior.

[0019] Furthermore, in the multi-data source collaborative parameter identification and simulation integration technology, the data types include one or more of stress-strain curves, creep curves, dislocation density, grain size-strain / time data, recrystallization data, damage data, and post-forming properties. A global optimization algorithm is used to calibrate the data and obtain a set of constitutive parameters that are applicable to multiple processes and multiple paths.

[0020] Overall, firstly, this invention establishes a unified internal variable framework system to provide a transmission bridge for complex deformation paths, wherein the internal variable system includes phase transformation, dislocation density, dynamic recrystallization behavior, and grain size damage variables. Specifically, (1) dislocation density evolution: using strain-driven accumulation and recovery terms to describe work hardening and dynamic recovery behavior; (2) recrystallization and grain evolution: establishing the relationship between grain size and strain, temperature, and time through dynamic recrystallization fraction evolution and grain growth equation; (3) damage and porosity evolution: introducing damage variables, controlling the initiation and expansion of damage through strain rate, stress triaxiality, plastic strain, etc., to characterize fracture tendency; and introducing microstructure evolution behavior in the form of integrals, so that the material exhibits different responses under different paths.

[0021] Secondly, this invention unifies theoretical methods, establishing unified methods for temperature, multiphase flow behavior, and multiple processes. Specifically, it uses the Arrhenius equation to unify the evolution behavior of process parameters at different temperatures; it uses hyperbolic sine functions, exponential functions, and other equations combined with phase proportions to unify flow behavior; and it uses stress threshold theory (i.e., back stress theory) to unify hot-pressing deformation and creep deformation behavior.

[0022] Furthermore, the novel multi-process-loading path parameter calibration method of this invention obtains the material deformation behavior and microstructure evolution behavior under different deformation behaviors, such as hollow blades which require multi-pass thermoforming-thermal creep gas expansion forming. These behaviors include uniaxial tension, constant load creep, multi-stage loading, back stress test (i.e., cyclic loading test), and microstructure interruption test. Through intelligent calibration, the deformation behavior and microstructure evolution behavior under different processes are obtained. The deformation behavior under different microstructure states responds to the behavior, enabling the accurate description of macroscopic deformation behavior and microstructure evolution under multiple processes, multi-stage deformation, and other composite paths.

[0023] Furthermore, in the unified parameter identification method for multiple processes and multiple types of data described in this invention, namely the multi-data source collaborative parameter identification method: (1) Data types include, but are not limited to: stress-strain curves, creep curves, dislocation density, grain size-strain / time data, recrystallization data, damage data, post-forming properties, etc. (2) Construct a unified multi-objective / weighted objective function and parameter calibration process, and incorporate the errors of different working conditions and different test types into the same evaluation system to ensure high accuracy under different processes and complex deformation paths; (3) Use global optimization algorithms, such as particle swarm optimization, genetic algorithm, whale algorithm, gray wolf algorithm and their improved algorithms, to perform parameter calibration and obtain a set of constitutive parameters that are applicable to multiple processes and multiple paths.

[0024] Specifically, this invention obtains performance formula parameters by regressing and fitting the measured yield strength of specimens in different deformation states with their corresponding internal variable data, thus achieving a unified mapping from deformation and microstructure to post-forming performance. The performance prediction formula constructed in this way can be directly called within the framework of a unified constitutive model, and can be used to predict the performance distribution of different regions of the part in real time or post-processing during simulation.

[0025] Furthermore, in the numerical implementation and simulation integration technology for multi-process composite paths, the constitutive model of this invention is encapsulated as a user material subroutine (such as UMAT, VUMAT, CREEP, etc.). The inputs include strain increment, temperature, time step, etc., and the outputs include stress increment, tangent stiffness matrix, and updated internal variables. Moreover, for multi-process simulation, process chain simulation is achieved by setting different load / boundary condition stages in the finite element model and continuously calling the same set of user subroutines on the time axis.

[0026] Finally, in the numerical implementation, this invention supports the output of the following at any process stage: (1) mechanical response: stress, strain, stress components, stress triaxiality, etc.; (2) microstructure indicators: dislocation density distribution, recrystallization fraction, grain size distribution, damage variables, etc.; (3) post-forming properties: yield strength σ 0.2.

[0027] Therefore, this invention achieves unified prediction of material deformation behavior and service performance under multi-process composite loading paths by unifying theoretical methods, internal variable systems and constitutive equation frameworks, explicitly characterizing composite loading paths and historical effects, combining intelligent algorithms with multi-data source collaborative identification methods, and a unified implementation form for numerical simulation platforms. This solves the problems of scattered models, difficulty in cross-process transfer, and difficulty in integrated evaluation of the entire process of forming, microstructure and performance in the prior art.

[0028] Furthermore, the specific steps of the unified prediction method for material deformation and properties under complex loading processes involving multiple processes include: S1: Determine the deformation behavior and basic test procedures based on the part forming process; S2: Conduct relevant basic experiments to obtain deformation behavior, tissue evolution behavior and post-deformation performance parameters under different deformation states; S3: Calibrates unified model parameters for multiple processes at a single temperature using intelligent algorithms; S4: Perform multi-temperature unified constitutive fitting on the temperature-related parameters in step S3, and iteratively optimize according to accuracy requirements; S5: Based on the unified constitutive model at multiple temperatures, fit the deformation and microstructure evolution under complex loading paths at multiple stages to obtain a unified constitutive model with high prediction accuracy under multiple process-composite loading paths. S6: Based on the unified constitutive model obtained in step S5, obtain the internal variable parameters under different deformation states, and fit the performance prediction formula after forming through the internal variable parameters; S7: Encapsulate the unified constitutive model obtained in step S5 and the post-forming performance prediction formula obtained in step S6 into the simulation software user subroutine to achieve unified prediction and real-time display of deformation, microstructure and performance under multiple processes and complex paths.

[0029] It is worth noting that step S1 analyzes the typical deformation modes and loading methods experienced by the target part during the actual forming process, including but not limited to uniaxial tension, constant load creep, cyclic loading, multi-stage deformation, and composite paths of stress control or strain control, based on the actual forming process path of the target part. It also identifies the temperature range, strain rate range, and strain range under different process conditions. The required types of basic material tests, test condition combinations, and loading path designs are determined, forming a basic test scheme to support the construction model under multi-process composite loading paths.

[0030] Step S2 involves conducting the corresponding basic material tests according to the test plan determined in step S1. These basic tests may include, but are not limited to: uniaxial tensile tests, compression tests, creep tests, back stress tests (cyclic loading tests), and multi-stage tests.

[0031] Mechanical tests are used to obtain macroscopic deformation data such as stress-strain curves, strain-time curves, creep curves, and cyclic stress response under different working conditions; for cyclic loading tests, information such as back stress evolution can be obtained.

[0032] Meanwhile, through metallographic observation, EBSD, XRD, TEM and other microstructure characterization methods, microstructure analysis was performed on samples under different deformation degrees, different temperatures and different loading histories to obtain microstructure parameters such as grain size and its distribution, recrystallization volume fraction, dislocation density, phase composition and its volume fraction, and evolution of pores or damage.

[0033] Mechanical property tests (such as room temperature or high temperature tensile tests, hardness tests, etc.) were conducted on specimens under different deformation states to obtain the yield strength corresponding to different internal variable states. Using the aforementioned macroscopic deformation data, back stress data, microstructure data, and property data, a multi-source experimental database describing the coupling relationship between material deformation, microstructure, and properties was constructed.

[0034] In step S3, a target temperature T is first selected. iThe single-pass macroscopic deformation data, back stress data, and microstructure data corresponding to different process conditions (different strain rates, different strain levels, different loading methods, etc.) at this temperature were used as calibration objects. Among the determined constitutive model parameters, a multi-objective evaluation function was constructed, including stress-strain matching error, back stress evolution matching error, and microstructure parameter (such as grain size, dislocation density, recrystallization volume fraction, etc.) matching error. Intelligent optimization methods such as particle swarm optimization, genetic algorithm, whale algorithm, gray wolf algorithm, and their improved algorithms were used to perform calibration at this temperature T. i The constitutive parameters of the material are uniformly optimized and calibrated at each discrete temperature T. Through the above calibration process, the material constitutive parameters are optimized at each discrete temperature T. i We obtained a set of "single-temperature-multi-process unified" model parameters that can simultaneously and well fit macroscopic deformation behavior, back stress evolution, and microstructure evolution, providing a foundation for subsequent multi-temperature unified fitting.

[0035] In step S4, after completing each temperature T i After single-temperature calibration, the parameter values ​​of each temperature point obtained in step S3 are used as input to perform multi-temperature unified constitutive fitting on the temperature-related parameters in order to obtain a parameter expression form that is applicable within a certain temperature range.

[0036] In step S5, based on the unified constitutive parameters obtained across multiple processes and temperature ranges, multi-stage complex loading path conditions are introduced, including multi-stage deformation, variable temperature, and variable strain rate conditions. Using the experimental data corresponding to these complex path conditions as target data, a multi-objective evaluation function is constructed, including stress-strain history error, strain-time history error, and microstructure evolution error. Intelligent algorithms are then used to further optimize and calibrate the parameters in the model. Through this step, the unified constitutive model not only achieves high prediction accuracy under multiple temperatures and simple loading paths, but also accurately reproduces the material's stress response, back stress evolution, damage accumulation, and microstructure evolution under multi-stage composite loading paths. This forms a material constitutive model with a unified form and high prediction accuracy under "multi-process – multi-temperature – complex path" conditions.

[0037] Step S6, based on the unified constitutive model obtained in step S5, calculates the material state under different deformation conditions and loading histories, and extracts the internal variable parameters corresponding to each state, including but not limited to damage variables. D dislocation density ρ Grain size d Based on this, a quantitative relationship between post-forming performance and internal variables is established, preferably using the following performance prediction expression:

[0038] in, σy P represents the yield strength under the corresponding condition. DIS P is a dislocation strengthening parameter that characterizes the contribution of dislocation strengthening. d Grain strengthening parameters that characterize the contribution of grain refinement to strengthening. σ y0 This refers to the initial yield strength or reference strength of the material.

[0039] The measured yield strength of specimens under different deformation states was obtained. σ y Its corresponding D, ρ, d Regression fitting was performed on the data with equal internal variables, and P was obtained through inversion. DIS P d , σ y0 The performance formula parameters achieve a unified mapping from deformation and microstructure to post-forming performance. The performance prediction formula constructed in this way can be directly called within the framework of the unified constitutive model, and can be used to predict the performance distribution of different regions of the part in real time or post-processing during simulation.

[0040] Finally, in step S7, the unified constitutive model determined in steps S5 and S6 is integrated into the finite element method or other numerical simulation software via a user subroutine. The user subroutine interface may include, but is not limited to, Abaqus' UMAT, VUMAT, and CREEP subroutines, DEFORM's USR_MTR and USR_RPD material subroutine interfaces, or other commercial simulation platforms with user material interfaces.

[0041] In the specific simulation process, corresponding boundary conditions, load methods (stress control, strain control), temperature field and pressure field distribution are set according to different part forming processes. The simulation software calls the same set of user subroutines at each integration point to update the stress-strain response, internal variables and microstructure at that integration point in real time, and further calculates the corresponding performance indicators through performance prediction formulas.

[0042] By combining the above results with post-processing or visualization modules, the deformation field, microstructure evolution distribution, and property distribution of materials can be displayed in real time under multiple processes and complex loading paths, realizing the integrated and unified prediction of deformation, microstructure, and properties, and providing unified and reliable theoretical and engineering support for process design, parameter optimization, and material development.

[0043] Furthermore, the method for multi-temperature unified constitutive fitting in step S4 includes: direct fitting using the Arrhenius formula or reparameterization using the Arrhenius type anchored to a reference temperature.

[0044] It is worth noting that the multi-temperature Arrhenius direct fitting method includes: (2) in, This is a temperature-dependent constant. For the corresponding coefficients, The corresponding activation energy (J mol) -1 ), I Here are the number of material parameters, and R is the gas constant (8.3145 J mol). -1 K -1 ), T j Where is the temperature (in Kelvin units), and J is the quantity of temperature.

[0045] By analyzing different T i Click on parameters C i Regression fitting is used to obtain a unified constitutive parameter expression that continuously varies across temperature ranges, achieving a unified characterization of parameter changes with temperature. Subsequently, the obtained unified constitutive parameters for multiple temperatures are substituted into the constitutive model to re-predict the experimental data under multiple operating conditions at each temperature, and the results are compared with the experimental results. When the prediction accuracy reaches a pre-set threshold (e.g., all error indicators are within the allowable range), the current unified constitutive parameters for multiple temperatures are considered to meet the requirements, and the process proceeds to step S5; if the fitting accuracy is insufficient, the process returns to step S3 based on the error distribution results, readjusts the single-temperature calibration process, updates the parameters at each temperature point, and then executes step S4 again.

[0046] The Arrhenius-type reparameterization method anchored to a reference temperature includes: First, in step S3, select multiple temperatures T. i (e.g., 750 ℃, 800 ℃, 850 ℃, etc.), single-temperature multi-process unified calibration is performed using experimental data of multiple process conditions at each temperature, resulting in a set of temperature T. i A corresponding set of constitutive parameters.

[0047] Based on this, temperature T* is used as the reference temperature. In subsequent multi-temperature fitting processes, temperature-independent parameters remain at their calibrated values ​​at the reference temperature T*, while temperature-dependent parameters are extended and described using Arrhenius-type relations. For any temperature-dependent parameter X, its calibrated value at the reference temperature T* is denoted as X^T*, and its evolution with temperature T can be written as: X(T) = X0·exp(±Q_X / (R·T)) (3) Where X0 is the amplitude parameter to be calibrated, and Q_X is the activation energy (J / mol) corresponding to this parameter. -1R is the gas constant, T is the temperature, and the "±" sign is selected based on the physical or empirical trend of the parameter changing with temperature.

[0048] Substituting T = T* and X = X^T* into equation (3), we get: Q_X = R·T*·[ ln(X^T*) ln(X0) ] (4) Substituting equation (4) back into equation (3), X(T) can be transformed into an expression that depends only on a single parameter X0: X(T) = X0·exp{ R·T*·[ ln(X^T*) ln(X0) ] / (R·T)} (5) Wherein, X^T* is given by the calibration result at the reference temperature T* in step S3 and is regarded as a constant value; in the multi-temperature fitting stage, only X0 is used as the optimization variable, and the activation energy Q_X is automatically determined by equation (4). In this way, when T = T*, X(T) strictly returns to X^T*, and the calibrated parameters at the reference temperature can remain unchanged when performing multi-temperature fitting, which is beneficial to improving the physical consistency and numerical stability of parameter identification.

[0049] In specific implementation, multiple temperatures T in step S3 will be used. i Macroscopic deformation data (including reference temperature T* and other temperatures) and corresponding microstructure characteristics (such as grain size, recrystallization volume fraction, dislocation density, etc.) are used as target data for unified fitting. On the one hand, a unified full-temperature shared form is adopted for model parameters that are considered to be independent of temperature, and overall optimization is performed under multiple temperature and multiple process conditions. On the other hand, the above temperature-related parameters are expressed by Arrhenius type reparameterization using equations (3) to (5), with X0 of each parameter as the design variable. By constructing a multi-objective evaluation function that simultaneously includes stress-strain error, strain-time error and important microstructure parameter errors, intelligent algorithms such as particle swarm optimization algorithm are used to uniformly optimize all undetermined parameters, and the parameter fitting of the unified constitutive model at multiple temperatures is completed.

[0050] A unified constitutive parameter set applicable to multiple temperature ranges can be obtained by either directly fitting the Arrhenius formula or by using an Arrhenius-type reparameterization method anchored to a reference temperature. After completing the multi-temperature fitting, the obtained unified constitutive parameters are substituted into the constitutive model to verify the deformation behavior and microstructure evolution under various temperatures and operating conditions. When the prediction accuracy meets the preset requirements, the process proceeds to step S5.

[0051] Furthermore, the performance prediction formula for step S6 is as follows:

[0052] in, σ y This represents the yield strength under the corresponding condition. D As a damage variable, ρ P is the dislocation density. DIS P is a dislocation strengthening parameter that characterizes the contribution of dislocation strengthening. d Grain strengthening parameters that characterize the contribution of grain refinement to strengthening. d Grain size, σ y0 The initial yield strength or reference strength of the material Furthermore, the user subroutines in step S7 include Abaqus' UMAT, VUMAT, and CREEP subroutines, DEFORM's USR_MTR and USR_RPD material subroutine interfaces, or other commercial simulation platforms with user material interfaces.

[0053] Through the above steps, this invention constructs a unified prediction method for deformation and performance under multi-process composite loading paths. It can not only uniformly handle the deformation-microstructure-performance prediction problem under different process conditions and complex paths, but also facilitates integration and promotion in existing commercial finite element software platforms through user subroutines, thereby significantly improving the compatibility and engineering practicality of material modeling and process simulation.

[0054] The second objective of this invention is to provide an application of the unified prediction method for material deformation and properties under complex loading processes described above.

[0055] Application of a unified prediction method for material deformation and properties under complex loading processes in multiple processes in the fields of part forming process design, material property prediction and digital manufacturing simulation technology.

[0056] In the field of overall process design and optimization for multi-process forming, the unified prediction method for material deformation and properties under complex loading processes disclosed in this invention can perform continuous coupled simulation of multiple passes and processes such as forging, rolling, hot forming, hot torsion, hot expansion, heat treatment, and hot creep under a unified constitutive framework, realizing full-process simulation prediction from blank to finished product. By changing process parameters (such as temperature, strain rate, strain path, holding time, and loading states such as constant speed and constant load), the impact on material flow behavior and microstructure evolution can be quickly assessed, providing a basis for process design, parameter optimization, and process window expansion, reducing the number of actual experiments and trial molding costs.

[0057] In the field of critical component service performance prediction, the unified prediction method for material deformation and properties under complex loading processes disclosed in this invention introduces state variables and performance prediction equations related to microstructure and damage evolution into the constitutive model, thereby achieving a unified description of performance during the forming and service stages. Using this method, the strength performance indicators of components under service conditions can be predicted based on the actual forming history and residual microstructure, thus achieving coupled prediction of the entire process from forming to microstructure to performance.

[0058] In the field of model integration for commercial and self-developed numerical simulation platforms, the unified prediction method for material deformation and performance in complex loading processes of multiple processes disclosed in this invention can achieve modularity and parameter unification in modeling form. It is easy to embed into existing commercial finite element software and self-developed computing platforms as user subroutines (such as UMAT, VUMAT, etc.) for complex component forming simulation, stress / deformation prediction and service behavior simulation, thereby improving the adaptability of simulation results to actual multi-process routes.

[0059] In the field of rapid evaluation and scale-up of new materials and processes, the unified prediction method for material deformation and performance under complex loading processes of multiple processes disclosed in this invention utilizes limited experimental data to quickly establish an initial constitutive model under the constraints of physical mechanisms. It then predicts the deformation response and performance level of the material under different process paths through multi-condition simulation, providing a reference for the evaluation of new material applications and the design of process scale-up test schemes, and accelerating the process of transformation from laboratory research to engineering applications.

[0060] Therefore, this invention can significantly improve the consistency and reliability of material deformation and performance prediction under multi-process composite loading paths, and provide a systematic and engineering-applicable material basis modeling solution for the process design and quality control of high-performance complex components.

[0061] Compared with the prior art, the present invention has the following beneficial effects: (1) The model structure is unified, realizing "one model to the end" for multiple processes and multiple paths. By constructing a unified state variable system and a unified constitutive equation structure, the same model can be used continuously under multiple processes and working conditions. This solves the problem that existing constitutive models "each manage a section of the process and it is difficult to connect the entire process chain". There is no need to build independent models for different processes, which significantly reduces the number of models and maintenance costs of complex process chains and improves the standardization of engineering applications.

[0062] (2) It has strong integrated prediction capabilities for the entire process of forming, microstructure, and performance, solving the problem that deformation behavior and service performance are "separate and difficult to unify" in traditional material constitutive modeling methods. It couples flow stress, microstructure evolution, and damage / performance evolution within the same framework, and can directly predict the strength performance indicators during the service stage using internal variables after the forming simulation is completed. Compared with the traditional segmented mode of "forming first and then performing performance analysis separately", this invention can provide a direct mapping from process parameters to service performance, providing a more reliable basis for process optimization and service life design.

[0063] (3) The ability to describe complex loading paths and historical effects is significantly improved. Since the material state is transferred through the internal variable system in the model, the material state and deformation behavior are directly coupled. It has higher identification ability and prediction accuracy for complex working conditions, and can avoid the problem of the prediction accuracy of traditional models dropping sharply after the path changes. It is more suitable for the "multi-segment loading" process route commonly seen in actual engineering.

[0064] Therefore, this invention solves the key technical problem of unified prediction of material deformation and performance under multi-process composite loading paths by using a unified internal variable system, a unified method for multiple processes, a calibration method for multi-process composite loading paths, and a collaborative parameter identification method for multiple data sources. It has significant advantages and practical application value in terms of model unification and engineering application. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0066] Figure 1 This is a schematic flowchart of the material modeling method disclosed in this invention.

[0067] Figure 2 The TC4 hollow blade multi-process combination process disclosed in Embodiment 1 of the present invention includes: 1-complete hollow blade, 2-blade, 3-cavity, 4-air passage, 5-tenon, and 6-blade tip.

[0068] Figure 3 This is a schematic diagram of the back stress solution method in Embodiment 1 of the present invention.

[0069] Figure 4 This is a schematic diagram of the macroscopic results of experiments corresponding to different processes in Example 1 of the present invention.

[0070] Figure 5This is the unified constitutive equation system for multiple processes in Embodiment 1 of the present invention.

[0071] Figure 6 This is a schematic diagram of the process for solving multiple temperature parameters of the unified constitutive model at 750℃-850℃ in Embodiment 1 of the present invention.

[0072] Figure 7 This is an evaluation diagram of the unified constitutive prediction results of TC4 titanium alloy at 750℃-850℃ for multiple processes in Example 1 of the present invention.

[0073] Figure 8 This is a cloud map showing the effect of unified prediction of "deformation-microstructure-property" for the TC4 titanium alloy hollow blade under multiple processes and complex path conditions in Embodiment 1 of the present invention. Detailed Implementation

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] The term "embodiment" used herein, as an example, is not necessarily to be construed as superior to or better than other embodiments. Performance testing in the embodiments of this application, unless otherwise specified, employs conventional testing methods in the art. It should be understood that the terminology used in this application is merely for describing particular implementations and is not intended to limit the scope of this disclosure.

[0076] Unless otherwise stated, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; other experimental methods and technical means not specifically mentioned herein refer to experimental methods and technical means commonly used by one of ordinary skill in the art.

[0077] To better illustrate the content of this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented even without certain specific details. In the embodiments, some methods, means, instruments, and devices well-known to those skilled in the art are not described in detail in order to highlight the main points of this application.

[0078] Without conflict, the technical features disclosed in the embodiments of this application can be combined arbitrarily, and the resulting technical solution belongs to the content disclosed in the embodiments of this application.

[0079] This invention belongs to the field of materials processing and manufacturing technology, specifically relating to a unified prediction method for material deformation and properties under complex loading processes involving multiple processes, and its application. Addressing the technical shortcomings of existing material constitutive models in cross-process applicability, characterization of multi-process historical effects, and unified evaluation of the entire forming-microstructure-property process, this invention constructs a unified internal variable system. Combined with a unified constitutive theory for multiple processes, a multi-process composite loading path calibration method, and multi-data source collaborative parameter identification and simulation integration technology, this invention achieves unified prediction of material deformation behavior, microstructure evolution, and post-forming properties under different forming processes, different loading modes, and complex historical paths. Compared with existing technologies, this invention represents a significant technological advancement in model universality, prediction accuracy, and ease of engineering implementation.

[0080] To better understand the present invention, the following embodiments are provided for further detailed description of the present invention, but they should not be construed as limiting the present invention. Any non-essential improvements and adjustments made by those skilled in the art based on the above-described invention are also considered to fall within the protection scope of the present invention.

[0081] Example 1: Unified Constituent Modeling and Performance Prediction Method for TC4 Hollow Blades under Multi-Process and Multi-Stage Forming Conditions Traditional models are mostly based on simple single-axis and single-path tests, which are poorly sensitive to complex paths such as variable temperature and rate, and loading-unloading-reloading, making it difficult to accurately reflect path-dependent behavior. In addition, existing models have application bottlenecks such as weak physical meaning of parameters, difficulty in cross-process calibration, and high test costs. This embodiment takes the multi-process and multi-stage forming of TC4 hollow blades as the object, and in the temperature range of 750 to 850 ℃, combines uniaxial tensile test, constant load creep test, multi-stage loading test, back stress test, and microstructure and mechanical property test to give the practical application process based on the method of this invention.

[0082] 1. Operating conditions and sample description This embodiment uses TC4 titanium alloy, a typical material for aero-engines, and the target component is a hollow blade. The process path includes a combination of multiple processes such as bending hot forming, hot twisting, hot shaping, and hot air expansion. Figure 2 As shown, the corresponding material deformation states cover typical working conditions such as high-temperature tension, constant load creep, and multi-stage loading.

[0083] The material specimens used are high-temperature mechanical specimens that match the blade forming temperature range. The gauge length of the tensile and multi-stage loading specimens can be selected as 25 mm, and the cross-sectional dimensions are determined according to the testing machine and fixture conditions. The creep specimens are standard round bars or plates.

[0084] 2. Material Constitutive Modeling Step S1: Determine the deformation behavior and basic test plan based on the hollow blade forming process. Based on the actual manufacturing process of TC4 hollow blades, the typical deformation modes during the multi-process forming process are analyzed, including: (1) Uniaxial high-temperature stretching at 750-850 °C (simulating thermoforming process); (2) Constant load creep at 750–850 °C (simulating thermal expansion and ultra-low strain rate process), with stress levels ranging from 135 MPa to 15 MPa; (3) Multi-stage loading path: with 750 ℃ ​​and 850 ℃ as characteristic temperatures, combined with a strain rate of 0.1 s. - ¹and 0.0001 s - ¹ Combined loading, using a multi-stage test format of “loading-cooling-reloading”, with a total true strain of approximately 0.2 in each loading stage, is used to simulate the historical dependence effect of multiple heating and intermediate cooling in the process forming; (4) Back stress test (cyclic loading test): at typical temperatures (e.g., 800 ℃ and 850 ℃) and strain rates (0.01 s⁻¹). - ¹, 0.001 and 0.0001 s - ¹) Under cyclic loading, the back stress evolution behavior is obtained; the method is as follows: Figure 3 As shown; (5) Microstructure and mechanical property test: Under the above typical working conditions and deformation degree (such as strain 0.2, 0.4, 0.6, etc.), metallographic, EBSD, TEM and other microstructure data are collected, including room temperature tensile test, and mechanical property parameters after forming are obtained. σ 0.2 is used to characterize the yield strength.

[0085] Step S2: Conduct basic experiments and construct a multi-source database Based on the plan established in step S1, the following tests were conducted on the TC4 material: (1) Uniaxial tensile test At temperatures of 750 ℃, 800 ℃, and 850 ℃, strain rates of 0.1 s⁻¹ were applied. - ¹ and 0.0001 s - ¹ A uniaxial high-temperature tensile test was performed. For specimens with a gauge length of 25 mm, the strain rate was 0.1 s⁻¹. - At ¹, the loading rate is approximately 2.5 mm / s; the strain rate is 0.0001 s⁻¹. - At ¹, the loading rate is approximately 0.0025 mm / s. Real stress-strain curves at different temperatures and strain rates are recorded to characterize the flow stress level and strain hardening / softening features, such as... Figure 4 (a).

[0086] (2) Creep test under constant load Constant stress levels ranging from 135 MPa to 15 MPa (e.g., 135, 105, 75, 45, 15 MPa) were set at 750 ℃, 800 ℃, and 850 ℃ for constant load creep tests. Strain-time curves and creep rate-time curves were recorded to characterize the creep behavior and creep stage characteristics under long-term high-temperature loading. Figure 4 (b).

[0087] (3) Multi-stage loading test With 750 ℃ ​​and 850 ℃ as typical temperatures, the combined strain rate is 0.1 s⁻¹. - ¹and 0.0001 s - ¹ Design a multi-stage test path of "loading – cooling – reloading". For example: Step 1: At 750 ℃, at a rate of 0.1 s - ¹Stretch to a true strain of approximately 0.2; cool to room temperature; Step 2: Reheat to 850 ℃, at a speed of 0.0001 s. - ¹Continue stretching until the cumulative true strain is approximately 0.4 (an additional 0.2 is added in this stage).

[0088] Other combined paths can be designed as needed, for example, the first stage at 850 ℃ and 0.0001 s. - ¹, The second stage is at 750 ℃ ​​for 0.1 s - ¹, etc., to achieve permutations and combinations of temperature and strain rate. Record stress-strain-time data for each stage to reflect multi-stage forming and history-dependent effects, such as... Figure 4 (c).

[0089] (4) Back stress test (cyclic loading test) At 800 ℃ and 850 ℃, with a strain rate of 0.01 s⁻¹ - ¹、0.001 s - ¹、0.0001 s - Cyclic tensile loading tests were conducted at 1 and 45 MPa, respectively. Cyclic stress response curves were recorded at strains of 0.2 and 0.4, or at times of 50, 100, and 500 s, to identify the back stress evolution behavior at various temperatures, strain rates, and creep stresses.

[0090] (5) Microstructure and mechanical property testing At different deformation states (such as true strains of 0.1, 0.2, 0.4, 0.6, etc.) after the completion of the above-mentioned uniaxial tensile, creep, multi-stage loading and back stress tests, the specimens were observed by metallography, EBSD and TEM to measure the grain size and distribution, recrystallization volume fraction, dislocation density and phase composition; at the same time, room temperature or high temperature tensile properties were tested to obtain the yield strength σ 0.2 in the corresponding state.

[0091] Through the above steps, a system covering 750–850 °C and strain rates of 0.1–0.0001 s⁻¹ was established. - ¹ A multi-source database of “macroscopic deformation – back stress – microstructure evolution – performance” under multiple conditions, including creep stress of 135–15 MPa and multi-stage loading paths.

[0092] Step S3: Unified calibration of multiple processes at a single temperature (taking 750 ℃ ​​as an example) In step S3, 750 ℃ ​​is first selected as the target temperature T1, and the experimental data obtained under different process conditions at this temperature are subjected to unified calibration for single-temperature multi-process testing. Specifically, this includes: (1) At 750 °C and a strain rate of 0.1 s - ¹and 0.0001 s - ¹The stress-strain curves obtained from uniaxial tensile tests are calibration data for macroscopic flow behavior; (2) The creep curves of constant load at various stress levels (135-75 MPa) at 750 ℃ ​​are used as the calibration data for creep behavior; (3) Calibration data based on back stress at 750 ℃; (4) The grain size and dislocation density after different degrees of deformation at 750 °C are used as calibration data for microstructure evolution.

[0093] The specific formula is as follows: Figure 5 As shown, a multi-objective evaluation function was constructed, including stress-strain error, strain-time (creep) error, back stress evolution error, and microstructure parameter error. Intelligent algorithms such as particle swarm optimization were used to uniformly optimize the model parameters at 750℃, resulting in a set of "750℃-multi-process unified" model parameters that can simultaneously fit the tensile, creep, back stress, and microstructure evolution behaviors.

[0094] Similarly, the above process was repeated for the data at 800 ℃ and 850 ℃ to obtain the model parameter sets for "800 ℃ – multi-process unification" and "850 ℃ – multi-process unification" respectively, providing basic data for subsequent multi-temperature unified fitting.

[0095] Step S4: Fitting unified constitutive parameters at multiple temperatures In step S4, using the single-temperature calibration parameters obtained in step S3 at 750 ℃, 800 ℃, and 850 ℃ as input, multi-temperature unified constitutive fitting is performed on the temperature-related parameters. This can be done using either of the two parallel methods mentioned above, as follows: Figure 6 As shown: Path 1: Direct solution of the Arrhenius formula at multiple temperatures (Method 1) S4-1 Read Single Temperature Calibration Results Read the various single-temperature calibration parameters Ci obtained in step S3 at 750 ℃, 800 ℃, and 850 ℃, including key parameters related to creep rate, flow stress, back stress, and tissue evolution.

[0096] S4-2 Establishing Temperature Dependence For each type of temperature-related parameter C i Establish Arrhenius-type temperature dependence: (2) in, This is a temperature-dependent constant. For the corresponding coefficients, The corresponding activation energy (J mol) -1 ), I Here are the number of material parameters, and R is the gas constant (8.3145 J mol). -1 K -1 ), T j Where is the temperature (in Kelvin units), and J is the quantity of temperature.

[0097] S4-3 Constructing a multi-temperature objective function Single-temperature calibration values ​​at 750 ℃, 800 ℃, and 850 ℃ C i (T i Substituting into the above formula, based on stress-strain / strain-time data for all temperature points and all loading paths (tensile, creep, multi-stage loading, back stress test), a comprehensive objective function encompassing multiple temperatures and working conditions is constructed. C 0i and Qci To optimize variables.

[0098] S4-4 Multi-temperature regression solution Using regression or numerical optimization algorithms, for C 0i and Qci We perform optimization to minimize the comprehensive objective function, thereby obtaining the result that minimizes the error. C 0i Qci .

[0099] S4-5 Generating parameter expressions for a uniform temperature range Using the obtained C 0i and Qci ,Will C i(T) is uniformly expressed within the temperature range of 750–850 ℃, resulting in a unified constitutive parameter function applicable to this temperature range. C i (T).

[0100] S4-6 Multi-temperature prediction and error evaluation After unification C i (T) Substitute into the constitutive model to predict the tensile, creep, multi-stage loading and back stress tests at 750 ℃, 800 ℃ and 850 ℃, calculate the prediction error of each working condition, and perform a weighted comprehensive evaluation.

[0101] S4-7 Accuracy Determination and Iteration If the overall prediction error is less than or equal to the preset accuracy threshold, the multi-temperature solution is determined to be converged, and a set of unified constitutive parameters applicable to 750~850 ℃ is output. If the overall error exceeds the preset threshold, the multi-temperature solution is deemed to have failed, the process returns to step S3, the parameter range is adjusted, the single-temperature calibration is performed again, and steps S4-1 to S4-7 in path one are executed again.

[0102] Path 2: Arrhenius reparameterization multi-temperature solution process anchored to reference temperature (Method 2) S4-1′ Read the single-temperature calibration result and select the reference temperature Read the temperature-related parameters obtained in step S3 at 750 ℃, 800 ℃, and 850 ℃, select 750 ℃ ​​as the reference temperature T*, and record the temperature-related parameters calibrated at 750 ℃ ​​as X^T*.

[0103] S4-2′ Establish reparameterized form For each type of temperature-dependent parameter X, the Arrhenius reparameterization form is used: X(T) = X0·exp(±Q_X / (R·T))(3) and through Q_X = R·T* · [ln(X^T*) ln(X0)](4) Representing Q_X as a function based on X0 and X^T* enables reparameterization with X0 as the only undetermined parameter.

[0104] S4-3′ Constructing a multi-temperature objective function While keeping X^T* constant at 750 ℃, with X0 as the undetermined variable, tensile test, creep test, and multi-stage loading and back stress test data at 800 ℃ and 850 ℃ are used to construct a comprehensive objective function for multiple temperatures and multiple working conditions, and fit the response at 800 ℃ and 850 ℃.

[0105] S4-4′ Multi-temperature calibration and Q_X inversion The intelligent optimization algorithm is used to optimize X0 and minimize the comprehensive objective function. After obtaining X0, Q_X is obtained by back-calculating using the reparameterized relation, and the complete Arrhenius expression of X(T) is updated.

[0106] S4-5′ Generate parameter expressions for a uniform temperature range Based on the obtained X0 and Q_X, a uniformly applicable X(T) expression is constructed within the temperature range of 750 to 850 ℃, realizing the unified calibration of temperature-related parameters at multiple temperatures.

[0107] S4-6′ Multi-temperature prediction and error evaluation Substituting the unified X(T) into the constitutive model, we make unified predictions for tensile, creep, multi-stage loading, and back stress tests at 750 ℃, 800 ℃, and 850 ℃, calculate the prediction errors for each path and temperature, and make a comprehensive evaluation.

[0108] S4-7′ Precision Determination and Iteration If the overall prediction error is less than or equal to the preset accuracy threshold, the reparameterized multi-temperature solution is considered successful, and a set of unified constitutive parameters applicable to 750–850 ℃ is output. If the overall error is greater than the preset threshold, the solution result is determined to be unsatisfactory. The process returns to step S3, the parameter range is readjusted for single temperature calibration, and S4-1′~S4-7′ in path two are executed again.

[0109] A comprehensive evaluation is performed on the tensile, creep, multi-stage loading, and back stress test data at different temperatures. When the prediction error meets the predetermined accuracy requirements, a set of unified constitutive parameters applicable to the 750–850 °C range is obtained. If the error does not meet the requirements, the process returns to step S3, refits, and repeats step S4.

[0110] Step S5: Unified constitutive model calibration under multi-stage complex loading paths In step S5, based on the obtained unified constitutive parameters for multiple temperatures from 750 to 850 °C, typical multi-stage loading path test data are introduced, namely the aforementioned "750 °C, 0.1 s..." - ¹ → Cooling → 850 ℃, 0.0001 s - ¹, the test conditions with a true strain of 0.2” per segment and other temperature-strain rate combinations are used as target data.

[0111] The stress-strain-time curves and microstructure evolution data (such as grain size, recrystallization fraction, dislocation density, etc. after each stage) obtained from multi-stage loading tests are incorporated into a multi-objective evaluation function to further intelligently optimize and calibrate the parameters in the model.

[0112] This step enables the unified constitutive model to accurately reproduce the stress response, strain accumulation, and microstructure evolution of TC4 material under multi-stage loading-cooling-reloading conditions, forming a unified constitutive model with high prediction accuracy under multiple processes and composite loading paths.

[0113] Step S6: Fit the parameters of the performance prediction formula after shaping based on the internal variable state. In step S6, the unified constitutive model calibrated in step S5 is applied to the above-mentioned tension, creep, and multi-stage loading processes to extract the internal variables under each typical deformation state, including damage variables. D dislocation density ρ Grain size d .

[0114] Based on the measured yield strength σ0.2 and performance data under the corresponding conditions, the following formula is used to establish the performance prediction relationship:

[0115] The dislocation enhancement parameter P was obtained through regression fitting. DIS Grain strengthening parameter P d and initial strength σ y0 This allows us to obtain post-forming performance prediction formulas that can be directly called within a unified constitutive framework, achieving a unified mapping from "deformation – microstructure – internal variables" to "performance".

[0116] Overall forecast situation as follows Figure 7 As shown, the prediction accuracy (c) and performance prediction (d) under thermoforming process-uniaxial stretching (a), thermo-expansion process-thermal creep (b), and multi-stage deformation are respectively displayed. The results show that the prediction effect is good.

[0117] Step S7: Encapsulate and apply the process to the multi-process forming of TC4 hollow blades in simulation software. In step S7, the above unified constitutive model and performance prediction formula are embedded into the finite element software in the form of a user material VUMAT subroutine.

[0118] Taking the multi-process forming of TC4 hollow blades as an example, a three-dimensional forming simulation model was established, including multiple pre-forming, intermediate shaping, and final forming stages. For each process, corresponding boundary conditions such as temperature (750–850 ℃), deformation rate, and contact and friction conditions were set. During the simulation, the same set of material user subroutines was called at each integration point to update the stress-strain state, internal variables, and microstructure parameters in real time. The corresponding performance indicators, such as yield strength, were calculated using performance prediction formulas.

[0119] Post-processing can yield the dislocation density distribution (a), damage distribution (b), grain size distribution (c), back stress distribution (d), and post-deformation performance distribution (e) of hollow blades at each process and in the final formed state. Figure 8 As shown, the unified prediction of "deformation-microstructure-property" of TC4 hollow blades under multiple processes and complex path conditions is realized, providing a quantitative basis for process window optimization (such as temperature, strain rate, and process sequence).

[0120] Through the above embodiments, the method of the present invention has been fully applied in the specific working condition of multi-process forming of TC4 hollow blades, demonstrating that the method can be applied in the temperature range of 750–850 °C and the time range of 0.1–0.0001 s. - ¹Unified modeling and prediction of material constitutive behavior, microstructure evolution and post-forming properties under various working conditions such as tension, constant load creep of 135–15 MPa and multi-stage loading.

[0121] Therefore, addressing the technical problems of weak physical meaning of model parameters, difficulty in cross-process application, and poor sensitivity to complex paths such as variable temperature and rate, loading-unloading-reloading, etc., which are mostly based on simple path experiments in existing material constitutive modeling methods, this paper significantly improves the compatibility and engineering practicality of material modeling and process simulation by using a unified constitutive modeling framework, explicit characterization of path and historical effects, and mechanism-data collaborative modeling strategy.

[0122] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A unified prediction method for material deformation and properties under complex loading processes involving multiple processes, characterized in that, By designing a unified internal variable system, a unified method for multiple processes, a calibration method for multi-process composite loading paths, and a multi-data source collaborative parameter identification and simulation integration technology, we can achieve integrated prediction of the deformation behavior / microstructure and forming performance of materials under different processes and loading modes.

2. The method for unified prediction of material deformation and properties under complex loading processes according to claim 1, characterized in that, The unified internal variable system includes phase transitions, dislocation density, dynamic recrystallization behavior, grain size, or damage variables.

3. The method for unified prediction of material deformation and properties under complex loading processes according to claim 1, characterized in that, The multi-process unification method includes using the Arrhenius formula to unify the evolution behavior of process parameters at different temperatures; using equations such as hyperbolic sine functions and exponential functions in combination with phase proportions to unify flow behavior; and using stress threshold theory (back stress theory) to unify hot-pressing deformation and creep deformation behavior.

4. The method for unified prediction of material deformation and properties under complex loading processes according to claim 1, characterized in that, In the multi-data source collaborative parameter identification and simulation integration technology, the data types include one or more of the following: stress-strain curves, creep curves, dislocation density, grain size-strain / time data, recrystallization data, damage data, and post-forming properties. A global optimization algorithm is used to calibrate the data and obtain a set of constitutive parameters that are applicable to multiple processes and multiple paths.

5. The method for unified prediction of material deformation and properties under complex loading processes according to any one of claims 1 to 4, characterized in that, The specific steps include: S1: Determine the deformation behavior and basic test procedures based on the part forming process; S2: Conduct relevant basic experiments to obtain deformation behavior, tissue evolution behavior and post-deformation performance parameters under different deformation states; S3: Calibrates unified model parameters for multiple processes at a single temperature using intelligent algorithms; S4: Perform multi-temperature unified constitutive fitting on the temperature-related parameters in step S3, and iteratively optimize according to accuracy requirements; S5: Based on the unified constitutive model at multiple temperatures, fit the deformation and microstructure evolution under complex loading paths at multiple stages to obtain a unified constitutive model with high prediction accuracy under multiple process-composite loading paths. S6: Based on the unified constitutive model obtained in step S5, obtain the internal variable parameters under different deformation states, and fit the performance prediction formula after forming through the internal variable parameters; S7: Encapsulate the unified constitutive model obtained in step S5 and the post-forming performance prediction formula obtained in step S6 into the simulation software user subroutine to achieve unified prediction and real-time display of deformation, microstructure and performance under multiple processes and complex paths.

6. The method for unified prediction of material deformation and properties under complex loading processes according to claim 5, characterized in that, The multi-temperature unified constitutive fitting method in step S4 includes: direct fitting using the Arrhenius formula or reparameterization using the Arrhenius type anchored to a reference temperature.

7. The method for unified prediction of material deformation and properties under complex loading processes according to claim 5, characterized in that, The performance prediction formula for step S6 is: in, σ y This represents the yield strength under the corresponding condition. D As a damage variable, ρ P is the dislocation density. DIS P is a dislocation strengthening parameter that characterizes the contribution of dislocation strengthening. d Grain strengthening parameters that characterize the contribution of grain refinement to strengthening. d Grain size, σ y0 This refers to the initial yield strength or reference strength of the material.

8. The method for unified prediction of material deformation and properties under complex loading processes according to claim 5, characterized in that, The user subroutines in step S7 include Abaqus' UMAT, VUMAT, and CREEP subroutines, DEFORM's USR_MTR and USR_RPD material subroutine interfaces, or other commercial simulation platforms with user material interfaces.

9. The application of the unified prediction method for material deformation and properties under complex loading processes in multiple processes as described in any one of claims 1 to 8 in the fields of part forming process design, material property prediction and digital manufacturing simulation technology.

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