A method for optimizing the properties of difficult-to-deform materials and a method for constructing multi-level physical models.

Abstract

This invention discloses a method for optimizing the performance of difficult-to-deform materials and a method for constructing a multi-level physical model, belonging to the technical field of performance optimization of metallic materials. The multi-level physical model construction method includes: constructing a macroscopic-scale model, a microscopic-scale model, and a mesoscopic-scale model; then coupling and integrating the macroscopic-scale model, the mesoscopic-scale model, and the microscopic-scale model to form the multi-level physical model. The constructed multi-level physical model is a computational framework that quantitatively couples macroscopic thermodynamic processes, mesoscopic microstructure evolution, and microscopic defect mechanisms. Based on the above multi-level physical model, the method for optimizing the performance of difficult-to-deform materials features a designed thermo-mechanical coupling and reorganization path with clear stage objectives and quantitative parameter control logic, shifting process development from "experience-based trial and error" to "theoretical prediction and precise execution," ensuring the stability and repeatability of process effects.

Description

Technical Field

[0001] This invention belongs to the technical field of performance optimization of metallic materials, and specifically relates to a method for performance optimization of difficult-to-deform materials and a method for constructing multi-level physical models. Background Technology

[0002] Difficult-to-deform materials, such as titanium alloys and nickel-based superalloys, play an irreplaceable role in key fields such as aerospace and energy equipment due to their excellent properties such as high strength and high temperature resistance. However, these materials generally suffer from problems such as poor plasticity, high deformation resistance, easy cracking, and difficulty in precisely controlling the microstructure during hot working.

[0003] Currently, process optimization for such materials mainly relies on experimental exploration and experience accumulation of macroscopic process parameters (such as temperature and deformation). This "trial and error" approach is not only time-consuming and costly, but also makes it difficult to achieve targeted control of microstructure (such as grain size, morphology, and orientation), thus limiting the full realization of the material's performance potential.

[0004] At the theoretical research level, by coupling macroscopic thermo-mechanical fields, mesoscopic grain evolution, and microscopic defect (such as dislocation) evolution, it is possible to quantitatively simulate and predict changes in the microstructure and properties of materials under given process conditions. However, in existing technologies, such models are mostly used for post-process interpretation or limited parameter optimization of existing processes, lacking a systematic application to mature process schemes for designing specific and executable grain remodeling paths. Although some existing patents or literature may mention the concept of "model-aided design," the process paths given are often vague (e.g., only vaguely mentioning "multi-pass deformation"), lacking specific and clear descriptions of the path design principles, the quantitative / qualitative relationships between key parameters at each stage, and their correlation with the evolution of the target microstructure, resulting in weak feasibility, repeatability, and stability of the scheme.

[0005] Therefore, there is an urgent need in this field for an innovative method that can closely integrate advanced theoretical models with specific industrial practices, and can proactively and precisely control the reorganization of the microstructure of difficult-to-deform materials, thereby achieving a synergistic improvement in performance and processability. Summary of the Invention

[0006] The main objective of this invention is to provide a method for optimizing the properties of difficult-to-deform materials and a method for constructing a multi-level physical model. This addresses the problems in existing technologies where the development of processes for difficult-to-deform materials relies on trial and error, microstructure is difficult to control precisely, and theoretical models are not effectively used for active process design. Specifically, this invention constructs a multi-level physical model to reverse-engineer a thermo-mechanical coupling and reorganization path that includes specific stage objectives, key parameter control logic, and the intrinsic relationships between parameters. This path is then precisely executed, thereby achieving directional and controllable optimization of the microstructure and properties of difficult-to-deform materials.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for constructing a multi-level physical model for optimizing the properties of difficult-to-deform materials, comprising the following steps: Constructing a macroscopic model: Establishing a constitutive model of the material to connect macroscopic deformation conditions with rheological stress; Constructing a microscale model: Establishing a coupled microscale defect evolution model, including: a dislocation density evolution model, used to describe the proliferation and recovery of dislocation density during material deformation and thermal activation; and a deformation energy storage model, used to calculate the deformation energy stored in the material based on dislocation density. Constructing a mesoscale model: Establishing coupled mesoscale microstructure evolution models, including: a recrystallization kinetic model to describe the grain formation and growth process driven by deformation energy storage; a phase transformation kinetic model to describe the phase transformation behavior of materials during cooling or deformation; and a grain growth model to simulate the grain coarsening process during heat treatment. The macroscopic scale model, mesoscopic scale model and microscopic scale model are coupled and integrated to form the multi-level physical model.

[0008] A second aspect of the present invention provides a method for optimizing the properties of difficult-to-deform materials based on a multi-level physical model, comprising the following steps: Path design steps: Based on the aforementioned multi-level physical model and the target microstructure of the evolution of difficult-to-deform materials, a thermo-mechanical coupling and reorganization path is designed in reverse. The thermo-mechanical coupling and reorganization path includes at least two stages with different structural transformation targets, and the process parameter combination for achieving the corresponding target in each stage is determined using the multi-level physical model. Path execution steps: According to the designed thermo-coupling recombination path and the determined combination of process parameters, the difficult-to-deform material is processed to induce directional grain structure recombination, thereby optimizing the material properties.

[0009] Compared with the prior art, the present invention has the following beneficial effects: (1) The multi-level physical model constructed in this invention is not a general simulation software, but is specifically designed for “reverse design of thermo-mechanical coupling and recombination path”. By taking microscopic deformation energy storage as the core driving variable and establishing its coupling relationship with organizational state variables such as mesoscopic recrystallization / phase change rate, the model has the ability to solve “process parameters” from “target organization” in reverse, forming a specific and operable virtual design and optimization tool.

[0010] (2) The model of the present invention adopts a modular design, and can select and couple the corresponding model components (such as phase transformation model, precipitation model, spheroidization model) according to different material systems (titanium alloy, nickel-based alloy, etc.) and performance targets, so as to be applicable to the process development of various difficult-to-deform materials.

[0011] (3) The method for optimizing the performance of difficult-to-deform materials based on the above model in this invention shifts the process development from "experience-based trial and error" to "theoretical prediction and precise execution". The designed thermo-coupling recombination path has clear stage goals and quantitative parameter control logic (such as energy storage threshold and recrystallization fraction threshold), which ensures the stability and repeatability of the process effect and can precisely control the microstructure (such as grain size and phase composition) to meet diverse performance requirements.

[0012] (4) Through the path designed by the model, it is possible to actively create a structure in the material that is conducive to subsequent processing, so that the rheological stress of the material is significantly reduced, the plasticity is improved, the number of processing passes is reduced, and the mechanical properties of the difficult-to-deform material and the efficiency of subsequent processing are significantly improved at the same time.

[0013] Other features and effects of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0014] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating the multi-level physical model construction method for optimizing the properties of difficult-to-deform materials according to the present invention is shown. Figure 2 A flowchart illustrating the performance optimization method for difficult-to-deform materials based on a multi-level physical model according to the present invention is shown. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. 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.

[0017] To achieve the above objectives, a first aspect of the embodiments of the present invention provides a method for constructing a multi-level physical model for optimizing the properties of difficult-to-deform materials, such as... Figure 1 As shown, it includes the following steps: Constructing a macroscopic model: Establishing a constitutive model of the material to connect macroscopic deformation conditions with rheological stress; Constructing a microscale model: Establishing a coupled microscale defect evolution model, including: a dislocation density evolution model, used to describe the proliferation and recovery of dislocation density during material deformation; and a deformation energy storage model, used to calculate the deformation energy stored in the material based on dislocation density. Constructing a mesoscale model: Establishing coupled mesoscale microstructure evolution models, including: a recrystallization kinetic model to describe the grain formation and growth process driven by deformation energy storage; a phase transformation kinetic model to describe the phase transformation behavior of materials during cooling or deformation; and a grain growth model to simulate the grain coarsening process during heat treatment. The macroscopic scale model, mesoscopic scale model and microscopic scale model are coupled and integrated to form the multi-level physical model.

[0018] The multi-level physical model constructed in this invention is a computational framework that quantitatively couples macroscopic thermodynamic processes, mesoscopic organizational evolution, and microscopic defect mechanisms. Its model construction follows the principle of "top-down driven, bottom-up feedback."

[0019] Macroscale model: As the input layer, it provides the temperature field (T) and strain field (T) of the entire field. ), strain rate field ( The spatiotemporal distribution of the stress field (σ) and the constitutive relation of the material is determined by solving the thermo-mechanical coupled finite element equations.

[0020] Mesoscale model: The core evolutionary layer, taking the collection of grains within a representative volumetric unit as the object, describes the evolution of their size (D), shape (aspect ratio), orientation (texture), and phase fractions (e.g., α, β, γ′). The driving force of its evolution comes from the deformation history and temperature history transmitted at the macroscale, while the dynamic parameters of its evolutionary mechanisms (e.g., recrystallization, grain growth, phase transformation) depend on the state variables (e.g., dislocation density) provided at the microscale.

[0021] Microscale: The mechanism layer, its core is describing the evolution of dislocation density (ρ). It is the macroscopic deformation energy storage (E... stored The physical carrier of the crystallization determines the nucleation rate and growth rate of recrystallization at the mesoscale.

[0022] The three-scale models are strongly coupled through key bridging variables (strain, strain rate, dislocation density, and deformation energy storage): macroscopic strain, strain rate, and temperature. , The microscopic dislocation density (ρ) is used as input to drive the evolution of microscopic dislocation density; the microscopic dislocation density (ρ) affects the nucleation rate and growth rate of mesoscopic recrystallization; the evolution of mesoscopic structure, in turn, affects the rheological stress (σ) and hardening / softening behavior, thereby updating the macroscopic constitutive relation and forming a closed loop.

[0023] In some preferred embodiments of the present invention, the macroscopic constitutive model (i.e., the rheological stress model), used to calculate the macroscopic rheological stress of a material under specific thermo-mechanical conditions, serves as a bridge connecting process parameters and the internal deformation response of the material. An Arrhenius-type hyperbolic sine constitutive equation considering dynamic recovery (DRV) and dynamic recrystallization (DRX) is adopted, and its expression is: ; in, Macroscopic strain rate (s) -1 ); Flow stress (MPa); Activation energy of thermal deformation (J·mol) -1 R: Universal gas constant (8.314 J·mol⁻¹) -1 ·K -1 T: Absolute temperature (K); : Constitutive constants related to the material (dimensionless); Z: Temperature-compensated strain rate factor (Zener-Hollomon parameter).

[0024] In some preferred embodiments of the present invention, the recrystallization kinetics model at the mesoscale is based on thermo-mechanical conditions and includes at least one of a dynamic recrystallization model, a static recrystallization model, and a subdynamic recrystallization model. It describes the grain formation and growth process driven by deformation energy storage, including the recrystallization volume fraction X. rex and the grain size D after recrystallization rex The expression.

[0025] The recrystallization volume fraction X is described using the modified Johnson-Mehl-Avrami-Kolmogorov (JMAK) equation. rex Evolution over time: ; For static recrystallization (SRX), This is the incubation period; for dynamic recrystallization (DRX), this equation is often used to describe the dynamics of the steady-state deformation stage; for subdynamic recrystallization (MDRX), It is approximately 0. The rate constant B is the core, coupling microscopic and mesoscopic information: ; in: : Recrystallization volume fraction (dimensionless); t: Isothermal time or equivalent time (s) at a given strain rate; : Recrystallization incubation period (s); k: Avrami index, related to nucleation and growth mechanisms (dimensionless). : Recrystallization rate constant (dimensions depend on k); Nucleation rate (m) -3 ·s -1 It is highly dependent on the local dislocation density. Temperature T; M: Grain boundary mobility (m 4 ·J -1 ·s -1 ), depends on temperature and grain boundary properties; d: spatial dimension (usually 3); Dislocation density (m) -2 m: an exponent characterizing the dependence of nucleation rate on dislocation density (dimensionless). Apparent activation energy for recrystallization (J·mol⁻¹) -1 R: Universal gas constant; T: Absolute temperature (K).

[0026] Grain size D after recrystallization rex (This is related to the average grain size after recrystallization and the competition between nucleation and growth), and the commonly used formula is: ; or ; in: : Average grain size after recrystallization (m); C, K: Material constants; q: Exponent (usually about 0.5-1); Nucleation rate (m) -3 ·s -1 M: Grain boundary mobility (m) 4 ·J -1 ·s -1 ).

[0027] In some preferred embodiments of the present invention, the mesoscale model includes a grain growth model that describes the grain size coarsening process at high temperatures after recrystallization, and its expression is: ; Where: D: grain size (m) at time t; : Initial grain size (m) at the start of grain growth; n: Grain growth exponent (dimensionless, usually 2-4); Grain growth rate constant (m) n ·s -1 ); t: isothermal holding time (s); Grain boundary migration activation energy (J·mol) -1 R: Universal gas constant; T: Absolute temperature (K) of isothermal insulation.

[0028] In some preferred embodiments of the invention, the phase transformation kinetic model includes the volume fraction and kinetics for predicting solid-state phase transformations (such as austenite decomposition and β-phase → α-phase transformation) during cooling. JMAK-type equations based on TTT (time-temperature-transformation) or CCT (continuous cooling transformation) curves are commonly used to describe the transformation variables under isothermal or continuous cooling conditions. ; For continuous cooling, a superposition integration (Scheil superposition method) is required. Model parameters ( , , The results were obtained by fitting experimental data of the TTT / CCT curve of the material.

[0029] in: Volume fraction of phase transition products (dimensionless). Volume fraction of phase transition products (dimensionless). : Phase transition incubation period (s); t: Time (s).

[0030] Furthermore, the phase transition kinetic model also includes the Johnson-Mehl equations, used to calculate the relationship between the phase transition volume fraction and time. Its specific expression is: ; Where f(t): the volume fraction of phase transition at time t (dimensionless); Nucleation rate (m) -3 ·s -1 v: Linear growth rate (m) s 1 ); t: time (s).

[0031] In some preferred embodiments of the present invention, the mesoscale model further includes a diffusion-controlled precipitation kinetics model to describe the precipitation behavior of second-phase particles (such as carbides, γ′ phase) in solid solutions, which is also commonly described by the Johnson-Mehl-Avrami (JMAK) equations: ; Or in a more general form: ; Wherein, the rate constant Conforms to the Arrhenius relation: ; The size of the precipitated phase is described by the Ostwald ripening model (such as LSW theory).

[0032] in: : Volume fraction of precipitated phase (dimensionless); t: Isothermal aging time (s); Kinetic parameters related to the nucleation of the precipitation phase, growth mechanism, and diffusion process; Apparent activation energy of precipitation process (J·mol) - ¹).

[0033] In some preferred embodiments of the present invention, during the deformation process, most of the mechanical work is dissipated as heat, and a small portion (approximately 1-10%) is stored as deformation energy in crystal defects, mainly dislocations. The deformation energy storage model can be approximately represented as follows: ; in, Deformation energy storage (J·m -3 ); b: Shear modulus (Pa), a function of temperature; b: Burgers vector modulus (m); Dislocation density (m) -2 ).

[0034] In some preferred embodiments of the present invention, the dislocation density evolution model describes the change in dislocation density during deformation and thermal activation, and is the core connecting macroscopic deformation with mesoscopic recrystallization / phase transition driving. It employs a classic Kocks-Mecking type equation, expressed as: ; in: Dislocation density (m) -2 ); b: True strain (dimensionless); b: Burgers vector modulus (m); The dislocation multiplication coefficient (dimensionless) is weakly correlated with strain rate and temperature. The dislocation recovery coefficient (dimensionless) is a strong function of temperature and activation energy, and is usually expressed as: ; in, Dislocation recovery activation energy (J·mol) -1 R: Universal gas constant; T: Absolute temperature (K).

[0035] The first term on the right side of this Kocks-Mecking type equation The second term on the right represents dislocation multiplication caused by strain hardening (related to strain rate and temperature). This represents dislocation annihilation caused by dynamic recovery (strongly dependent on temperature T and activation energy Q). rex This model quantizes deformable energy storage E. stored With E recovery The core of the competition is to ensure the accumulation of E in the previous stage. stored In subsequent stages, it is effectively used to drive the desired recrystallization or phase transition, rather than being consumed by ineffective recovery.

[0036] A second aspect of the embodiments of the present invention also provides a method for optimizing the properties of difficult-to-deform materials based on a multi-level physical model, such as... Figure 2 As shown, it includes the following steps: Path design steps: Based on the aforementioned multi-level physical model and the target microstructure of the evolution of difficult-to-deform materials, a thermo-mechanical coupling and reorganization path is designed in reverse. The thermo-mechanical coupling and reorganization path includes at least two stages with different structural transformation targets, and the process parameter combination for achieving the corresponding target in each stage is determined using the multi-level physical model. Path execution steps: According to the designed thermo-coupling recombination path and the determined combination of process parameters, the difficult-to-deform material is processed to induce directional grain structure recombination, thereby optimizing the material properties.

[0037] This invention utilizes a multi-level physical model to design a specific reorganization path with clearly defined stage objectives and parameter control logic, and executes it precisely, significantly surpassing the effects of conventional heat treatment or empirical multi-pass deformation. It achieves controllable preparation of the microstructure and simultaneous improvement of the properties of difficult-to-deform materials, and brings significant efficiency gains to subsequent processing.

[0038] In some preferred embodiments of the present invention, the reverse design process of the thermo-coupling recombination path includes: (1) Determine the target microstructure for the evolution of difficult-to-deform materials, including: analyzing the initial microstructure of the difficult-to-deform materials to be treated (such as phase composition, grain size, texture, etc.) and determining the properties to be optimized; based on the correlation between the properties to be optimized and the microstructure, determine the microstructure characteristics to be obtained (such as equiaxed fine grains, weak texture, or gradient structure of fine grains in the core and coarse grains on the surface). (2) Based on the target structure, a thermodynamic processing sequence comprising at least two stages is designed in reverse using the multi-level physical model, the thermodynamic processing sequence comprising: (2.1) First stage: Introduce deformation energy storage in difficult-to-deform materials through high-temperature deformation, break the initial structure or induce phase transformation; (2.2) Second stage: The deformation energy storage is precisely controlled by heat preservation or deformation to drive recrystallization and / or phase transformation.

[0039] The first stage of this invention lays the physical foundation and driving force for the entire material recombination process by introducing sufficiently high deformation energy storage to drive subsequent recrystallization, grain growth, or phase transformation processes. This deformation energy storage is directly related to the dislocation density inside the material. Simultaneously with introducing deformation energy storage, high-temperature deformation also achieves the fragmentation of the initial structure or induces specific phase transformations, providing a precursor for obtaining the target structure in subsequent stages.

[0040] The core objective of the second stage is to "regulate energy storage in preparation for obtaining the final target structure." The specific physical processes depend on the material system and the overall path design, and can be either driven recrystallization (static or subdynamic), driven phase transition, or both.

[0041] Its driving force is primarily recrystallization: This is the most common case. For example, in high-temperature alloys or steels, after a high dislocation density is introduced in the first stage, static recrystallization is driven by the stored deformation energy through holding at a slightly lower temperature (T2), forming new strain-free grains.

[0042] Phase transformation is the primary driver: In some materials, phase transformation is the key mechanism for grain reorganization. For example, in TC4 titanium alloy, after deformation in the β phase region in the first stage, rapid cooling to the (α+β) two-phase region and holding at that temperature (second stage) are the main processes, with the β→α phase transformation being the primary process. The newly formed α phase interface can become a nucleation site for subsequent recrystallization, but the core of this stage is phase transformation, not typical recrystallization. Furthermore, in the deformation process of some materials, recrystallization and phase transformation occur synergistically.

[0043] In some preferred embodiments of the present invention, the process parameters (T1, ε1, ...) for the first stage are determined using the multi-level physical model. ); where T1 is the deformation temperature of the first stage, and ε1 is the true strain of the first stage, The strain rate for the first stage includes: The target deformation energy storage value and the dynamic recrystallization volume fraction threshold are used as model inputs; Using the aforementioned multi-level physical model and iterative optimization algorithm, a set of process parameters (T1, ε1, ...) is found. This ensures that when the true strain reaches ε1, the deformation energy storage calculated by the model is not lower than the target deformation energy storage value, and the dynamic recrystallization fraction does not exceed the dynamic recrystallization volume fraction threshold.

[0044] The model utilizes the equations of the dislocation density evolution model at a preset temperature T1 and strain rate. Under the given conditions, the curve of dislocation density ρ as a function of strain ε is calculated by integration. This curve forms the basis for the entire first-stage parameter optimization and is specifically used to determine: Whether the target energy storage is met: The ρ-ε curve obtained by integration can be used to instantly calculate the deformation energy storage corresponding to any strain point using the deformation energy storage O-type. The model needs to be verified to see if the calculated deformation energy storage can reach or exceed the target deformation energy storage value preset to drive subsequent recrystallization / phase transformation when the strain reaches the preset termination value ε1.

[0045] Whether the dynamic recrystallization threshold is exceeded: Under the same ρ-ε curve and deformation conditions, the coupled dynamic recrystallization kinetic model can calculate the dynamic recrystallization volume fraction in parallel. The curve showing the evolution of strain ε. The model needs validation; the calculated values ​​should be obtained when the strain reaches ε1. Whether it has not exceeded the "dynamic recrystallization volume fraction threshold" set to avoid premature consumption of too much stored energy.

[0046] Therefore, this ρ-ε curve is a key tool for simultaneously and quantitatively predicting and constraining the two objectives of "energy storage introduction" and "organizational evolution." The optimization algorithm achieves this by repeatedly adjusting (T1, ε1, ... We use this to "shape" the curve so that its endpoint (ε=ε1) satisfies both of the above conditions.

[0047] In some preferred embodiments of the present invention, when a heat preservation path is adopted, the process parameters (T2, t2) of the second group are determined using the multi-level physical model, wherein T2 is the processing temperature of the second stage and t2 is the heat preservation time, including: The microstructure of the material at the end of the first stage and temperature T2 are used as the initial conditions for the model; the microstructure includes dislocation density and substructure characteristics; The decay curve ρ(t) of dislocation density ρ with heat preservation time t was simulated by a multi-level physical model. Instantaneous deformation energy storage is calculated based on the decay curve ρ(t) and the deformation energy storage formula. ; Determine the insulation time t2, so that the instantaneous energy storage at time t2 is... Not less than the target remaining energy storage value required to drive subsequent recrystallization and / or phase change Furthermore, the volume fraction of static recrystallization at time t2 does not exceed the threshold for static recrystallization volume fraction.

[0048] When the second stage employs a heat preservation approach, the static recrystallization (SRX) model is used. This model uses the microstructure state at the end of the first stage (including dislocation density ρ and substructure, etc.) as the initial state and predicts the recrystallization volume fraction X at the heat preservation temperature T2. srx The evolution over time t. This is precisely to accurately determine the holding time t2 so that the recrystallization process reaches the desired extent (e.g., partial recrystallization to retain energy storage, or complete recrystallization to obtain fine crystals).

[0049] The determination of time t2 is an optimization result under multiple constraints. In addition to satisfying the core condition of "instantaneous energy storage ≥ target remaining energy storage value", a second key constraint must also be satisfied: static recrystallization (SRX) volume fraction X. srx The threshold value shall not be exceeded. The process and determining factors that ensure excessive SRX does not occur are as follows: Control mechanism: Whether excessive SRX occurs is mainly determined by the holding temperature T2 and the holding time t2. SRX is a thermally activated process; temperature T2 determines its driving force and rate, while time t2 determines the progress.

[0050] Model Prediction and Constraints: Using a coupled static recrystallization (SRX) kinetic model (such as the JMAK equation), with the initial dislocation density ρ and temperature T2 as inputs, the SRX volume fraction X can be predicted. srx The growth curve over time t. When determining t2, it is necessary to simultaneously examine the model's predicted X at time t2. srx Whether it does not exceed the maximum allowable value (e.g., 10%), this maximum value is the "static recrystallization volume fraction threshold".

[0051] Typically, as the insulation time t increases, the energy storage E... stored The response rate decreases monotonically, while the SRX score X srx The temperature increases monotonically. The optimal t2 is either the upper limit of the possible time window that satisfies the above two requirements, or a compromise point. If both conditions cannot be satisfied simultaneously, the adjustment temperature T2 must be returned.

[0052] In some preferred embodiments of the present invention, when a deformation path is adopted, the process parameters (T2, ε2) of the second set are determined using the multi-level physical model, wherein T2 is the processing temperature of the second stage, and ε2 is the true strain of the second stage, including: The microstructure of the material at the end of the first stage and temperature T2 are used as the initial conditions for the model; the microstructure includes dislocation density and substructure characteristics; The total deformation energy storage, determined by the newly introduced dislocation increment and the recovery effect, was calculated using the multi-level physical model after the application of true strain ε2. Determine the true strain ε2 such that the total deformation energy storage meets the target remaining energy storage value required to drive subsequent recrystallization and / or phase transformation. .

[0053] When the second stage employs a deformation path, the deformation amount is lower than that of the first stage, utilizing a subdynamic recrystallization (MDRX) model. A static recrystallization (SRX) model is used instead. This model predicts rapid recrystallization when deformation continues immediately at a new temperature T2 after deformation is interrupted. It is used to determine the small strain ε2 to control the structural evolution caused by MDRX. In the final low-strain deformation and subsequent heat treatment, the main processes are also SRX or MDRX. The model uses these kinetics to predict and optimize the final microstructure (e.g., recrystallization completion, grain size). The selection of T2 is based on the material's physical and metallurgical properties and process objectives; the basic logic is as follows: The selection of T2 must first serve the core objective of the second phase: "regulating energy storage and preparing for nucleation." The lower limit of temperature T2 must be higher than or equal to the static recrystallization (SRX) temperature of the material to ensure that the stored deformation has sufficient driving force to drive recovery or recrystallization within a reasonable time. Additionally, if a phase transformation is involved, T2 must be within the temperature range where the target phase transformation occurs (e.g., for titanium alloys, T2 must be set in the α+β two-phase region to promote the β→α phase transformation). The upper temperature limit for T2: T2 is typically lower than the first-stage deformation temperature T1. This is because: a) Reduce the diffusion ability of atoms, thereby slowing down the rate of dislocation recovery and recrystallization, achieving "precise control" of the energy storage consumption process and avoiding excessive consumption; b) To prevent unfavorable phase transformations or excessive grain growth in certain materials.

[0054] Model-assisted optimization: After clarifying the above objectives and constraints, the specific value of T2 can be determined through sensitivity analysis or parameter sweep optimization using the model. For example, the model can simulate the changing trend of the holding time t2 required to achieve the same "target remaining energy storage" under different candidate T2 values, as well as the corresponding static recrystallization process. Ultimately, an optimal T2 value can be selected within a reasonable range by combining production efficiency (t2 should not be too long) and organizational objectives (such as avoiding excessive SRX).

[0055] In some preferred embodiments of the present invention, the cooling rate of the cooling regime is also controlled to regulate the phase change process or the second phase precipitation behavior.

[0056] The determination of the cooling regime mainly revolves around the core parameter of cooling rate, and sometimes also includes the temperature nodes and residence time of staged cooling. Parameter determination process: Target input: Specify the desired phase composition (e.g., the ratio of martensite to bainite) and the second phase size / distribution; Model simulation and optimization: Based on the phase transformation kinetics model of the material (such as the mathematical model of TTT / CCT curve), simulate the type, quantity and start / end temperature of transformation products of supercooled austenite (or high-temperature phase) under different cooling rates; By combining a diffusion-controlled precipitation kinetics model, the precipitation sequence, growth, and coarsening behavior of the second phase (such as carbide, γ′ phase) under specific cooling paths are predicted. Using iterative optimization algorithms (such as traversal or genetic algorithms), under the premise of satisfying process constraints (such as the upper limit of equipment cooling capacity and the maximum cooling rate to avoid cracking), we can find the cooling path that makes the final microstructure obtained by simulation closest to the target microstructure.

[0057] In some preferred embodiments of the present invention, the design of the thermo-mechanical processing sequence further includes: a third stage: stabilizing the microstructure through deformation and cooling regimes to obtain the target grain size and phase composition. The purpose of this stage is to complete recrystallization and adjust the texture.

[0058] In some preferred embodiments of the present invention, in the third stage, the process parameters (T3, ε3, ...) of the third group are determined using the multi-level physical model. ), where T3 is the deformation temperature of the third stage, and ε3 is the true strain of the third stage. The strain rate for the third stage includes: Using the microstructure at the end of the second stage (mesoscopic parameters: recrystallization volume fraction, average grain size and its distribution, phase composition and texture (crystal orientation distribution); microscopic parameters: dislocation density (ρ) and substructure characteristics (such as subgrain size, subgrain boundary orientation difference) as the initial simulation conditions for the multi-level physical model, a set of process parameters (T3, ε3, ...) are found through the multi-level physical model and iterative optimization algorithm. A combination of parameters that enables the final recrystallized grain size to be closest to the target grain size and has a sufficient recrystallization volume fraction (usually required to be ≥95%).

[0059] In some preferred embodiments of the present invention, the design of the thermo-mechanical treatment sequence further includes: a fourth stage, a post-treatment stage: stress annealing or aging heat treatment is performed on the difficult-to-deform material after grain structure reorganization, wherein the process parameters (T4, t4) of the stress annealing or aging heat treatment are optimized and determined through the multi-level physical model, and the process includes: Target inputs: For stress annealing, the main goal is to eliminate residual stress below the safety threshold while controlling grain growth to prevent significant growth; for aging treatment, the main goal is to achieve optimal size, number density, and distribution of strengthening phases (such as γ′ phase and carbides) to achieve peak strength or ideal strength-toughness matching. Model simulation and optimization: Stress annealing: Combining a grain growth model and a residual stress relaxation model based on creep or recovery mechanisms (through coupling in the microscale model), the decrease in residual stress and the growth of grain size under different (T4, t4) combinations are simulated. The optimization objective is to find (T4, t4) such that the residual stress is below the target value and the grain growth does not exceed the allowable range.

[0060] Aging treatment: Using precipitation kinetic models (such as the aforementioned JMAK equation) and precipitation phase coarsening models (such as LSW theory), the precipitation sequence, growth, and coarsening kinetics of the strengthening phase are simulated under different (T4, t4) conditions. The optimization objective is to find the (T4, t4) parameter window (usually corresponding to the vicinity of the "peak aging" state) that achieves the best strengthening effect in terms of precipitation phase size distribution.

[0061] In some preferred embodiments of the present invention, in the thermo-coupling remodeling path, the deformation temperature of adjacent stages exhibits a non-monotonic change, and / or the strain path direction changes. Here, the strain path direction specifically refers to the change in the direction of the principal strain in physical space during the continuous deformation process of the material.

[0062] In some preferred embodiments of the present invention, the difficult-to-deform material is, for example, a titanium alloy, a nickel-based superalloy, or an ultra-high-strength steel. The goal of its grain structure reorganization includes obtaining equiaxed fine grains (average size less than 5 μm), weakly textured structures, or gradient structure structures.

[0063] The present invention will be further described in detail below with reference to specific embodiments, which should not be construed as limiting the scope of protection claimed by the present invention.

[0064] Example 1: Grain refinement and performance improvement of TC4 titanium alloy forging billet: Objective: To transform the coarse β-transformation microstructure (lamellar α) of TC4 titanium alloy forgings into an equiaxed (α+β) microstructure with an average α grain size of <3μm, thereby improving strength and plasticity.

[0065] Model construction: A multi-level physical model of TC4 titanium alloy was established, coupling macroscopic thermo-mechanical field, mesoscopic grain evolution process and microscopic defect evolution process (integrating β phase region deformation, phase transformation and (α+β) region dynamic spheroidization mechanism).

[0066] Path design sequence: (1) First stage (fragmented layers, high energy storage, T1=1010°C, ε1=0.5, =0.1 s - ¹, Water quenched to 800°C): Heated to the upper limit of the β phase region, 1010°C, and held for 10 min to homogenize. Strained at a rate of 0.1 s⁻¹. - ¹Perform compression deformation, true strain =0.5 (Model calculations show that at this strain, the dislocation density accumulation is sufficient to fully break the lamellar structure and drive subsequent recrystallization). Immediately after deformation, the material was water-quenched to 800°C (below the β transformation point). The specific parameters were determined as follows: (1.1) Temperature T1 determination: Based on the phase diagram data model of TC4 alloy, the temperature of the β phase region is determined to be 995-1010℃ (with slight adjustment according to composition).

[0067] Optimization objective: Select the upper limit temperature of the β phase region (1010°C) to ensure that the alloy is completely in the β phase region, avoid the presence of the α phase affecting the deformation uniformity, and at the same time avoid excessive temperature leading to abnormal grain growth.

[0068] Model calculation: The precise β-transition temperature is determined by using phase diagram thermodynamic calculation software (such as Thermo-Calc).

[0069] (1.2) Strain ε1 and strain rate Sure: Optimization objective: Introduce sufficient deformable energy storage (Estored_target ≥ 15 MJ / m³) 3 The dynamic recrystallization fraction is ≤10%.

[0070] (1.3) Calculation process of multi-level physical model: a. Input constraints: Deformation temperature range: 1000-1020°C; Strain rate range: 0.01-1 s - ¹; Strain range: 0.3-0.8; Target energy storage: Estored_target = 15 MJ / m 3 ; Dynamic recrystallization threshold: Xdrx_threshold = 10%.

[0071] b. Coupled model simulation: Dislocation density evolution model: For the TC4 alloy in the β phase region: k1 = 1.2 × 10⁻⁶ 9 , k2=25exp(-Qrec / RT), Qrec=180 kJ / mol.

[0072] Dynamic recrystallization model: ;in, =0.25、 =0.45, k=1.8.

[0073] Deformable energy storage model: Where μ = 42 GPa, b = 2.95 × 10⁻⁶ -10 m.

[0074] c. Iterative optimization: A genetic algorithm is used to search within the parameter space to find the energy storage requirement that meets the target energy storage requirement (≥15 MJ / m³). 3 Furthermore, considering the dynamic recrystallization volume fraction (≤10%), ε1 was ultimately determined to be 0.5. =0.1 s - ¹.

[0075] (1.4) Cooling regime determined (water quenching to 800℃): According to the TTT curve of the phase transformation kinetic model, in the cooling process from 1010°C to 800°C, in order to avoid the β phase decomposing into coarse α plates, the cooling rate needs to be ≥100°C / s. Simulation shows that when the cooling rate is ≥100°C / s, the β phase mainly transforms into martensite α′, providing a non-equilibrium microstructure basis for subsequent spheroidization. (2) Second stage (inducing phase transformation and spheroidization preparation, T2=800°C, t2=5min): 800°C for 5min (the model simulation shows that during this time, the β phase partially decomposes, generating fine α plates, while the energy stored by high-temperature deformation is partially retained). The specific parameter determination process is as follows: (2.1) Temperature T2 determined (800°C): Model basis: phase diagram data and phase transition dynamics model.

[0076] Optimization objective: To be located in the α+β two-phase region, promote the β→α phase transition, and at the same time, keep the temperature low enough to suppress rapid recovery.

[0077] Calculation process: a. According to the phase diagram, the equilibrium volume fraction of the α phase at 800°C is approximately 45%.

[0078] b. Diffusion equations using phase transition kinetics model: ,in, Describe the growth rate of second-phase particles (m) s -1 D: Diffusion coefficient (m) 2 s -1 R: gas constant (8.314 J) mol -1 K -1 T: Absolute temperature (K), ΔG: Molar Gibbs free energy difference (J) mo -1 ), V m Molar volume (m 3 mol -1 r: grain radius (m), calculate the α phase growth rate, simulation shows that fine α lamellae (thickness about 0.2 μm) can be obtained at 800°C.

[0079] (2.2) Determination of heat preservation time t2 (5 min): Optimization objectives: approximately 30% β-phase decomposition, retaining deformation energy storage Estored≥8 MJ / m³, and static recrystallization fraction≤5%.

[0080] Model calculation process: a. Initial conditions: State at the end of the first stage: Dislocation density: ρ0 = 1.5 × 10⁻⁶ 15 m -2 The microstructure is β phase + α′ martensite, and the temperature is 800°C.

[0081] b. Parallel simulation: Using a phase transition kinetic model: the Johnson-Mehl equation is employed to calculate the volume fraction X of the β→α phase transition. β→α (t), showing the phase change volume fraction X after 5 min. β→α ≈35%.

[0082] Using the static recovery model (which describes the decay of rheological stress over time during isothermal processes after hot working), the expression is: Where, k2 = 1.5 × 10 -3 exp(-Q rec / RT), ρ(t): dislocation density at time t, ρ0=1.5×10 15 m -2 The calculation shows that after 5 minutes, ρ = 8.2 × 10⁻⁶. 14 m -2 Estored = 7.2 MJ / m 3 .

[0083] Using the static recrystallization model: Where B = 1.3 × 10-6 s -k Given t0=120s and k=2.5, the static recrystallization fraction Xsrx≈3% was calculated at 5min.

[0084] In summary, t2=5min satisfies E stored ≥7 MJ / m 3 And X srx The requirement is ≤5%.

[0085] (3) Third stage (equiaxed and stable, T3=930°C, ε3=0.2, =0.01 s - ¹): Reheat to 930°C in the (α+β) two-phase region, hold for 5 min, and then apply a slow strain rate of 0.01 s⁻¹. - ¹Compression deformation with true strain ε3=0.2 is performed (to promote dynamic spheroidization of the α phase and redistribution of the β phase). After deformation, air cooling is performed.

[0086] (3.1) Temperature T3 determined (930°C): Model basis: Dynamic spheroidization optimization temperature of α+β two-phase region.

[0087] Optimization objective: The temperature should be high enough to promote diffusion-controlled spheroidization, but below the β-transition point to avoid lamellar tissue regeneration.

[0088] Calculation process: According to thermodynamic calculations, at 930°C, the volume fraction of the α phase is about 65% and that of the β phase is about 35%, and this ratio is most favorable for spheroidization of the α phase.

[0089] (3.2) Strain ε3 and strain rate Determine (0.2, 0.01 s) - ¹): Optimization goal: To achieve complete α-phase spheroidization (equiaxed degree ≥90%), and α-grain size <3μm after spheroidization.

[0090] Model calculation process: a. Initial conditions: Tissue α-lamella thickness at the end of the second stage: approximately 0.2 μm, dislocation density: ρ = 8.2 × 10⁻⁶ 14 m -2 The α / β phase ratio is approximately 40 / 60.

[0091] b. Dynamic sphericity model: Based on a modified Raj model, sphericity rate: , where X s For spheroidization volume fraction, C=50, D α Let α be the thickness of the sheet, and Q be the thickness of the sheet. gb σ is the grain boundary diffusion activation energy, μ is the residual internal stress, b is the matrix shear modulus, and D is the Burgers vector of dislocations.α : Diffusion coefficient of α-iron matrix.

[0092] c. Coupled simulation: Simulations were performed at 930°C for different strain rates (0.001–0.1 s⁻¹). - ¹) and the spheroidization process under strain (0.1-0.4), the interaction between α-phase spheroidization and β-phase recrystallization was simulated using cellular automata method, and the optimization was obtained: when ε3=0.2, =0.01s - At ¹, the degree of sphericity was 92%, the average equiaxed α size was 2.3 μm (predicted), and the recrystallization volume fraction was 98%.

[0093] d. Verification: The strain uniformity is ensured to be ≥85% by finite element simulation of the deformation field distribution.

[0094] (4) Fourth stage (post-treatment, T4=750℃, t4=1h): 750°C / 1h stress relief annealing, air cooling.

[0095] (4.1) Temperature T4 determined (750°C): Model basis: Below the recrystallization temperature but high enough to promote dislocation rearrangement.

[0096] Optimization goals: Eliminate ≥70% of residual stress, grain growth ≤10%. Calculation process: a. Based on the residual stress relaxation model: , , Where σ(t): instantaneous residual internal stress at time t, σ0: initial residual stress, and t is the holding time. The stress characteristic relaxation time, : Relaxation precondition (material intrinsic frequency factor) The stress relaxation activation energy is 220 kJ / mol, R is the universal gas constant, and T is the thermodynamic absolute temperature.

[0097] Simulations show that at 750°C, τ≈0.5h, and after 1h, the residual stress drops to 25% of the initial value.

[0098] b. Based on the grain growth model: Calculations show that the grain size increases by approximately 8% after 1 hour at 750°C.

[0099] (4.2) Time t4 is determined (1h): Optimization objective: To minimize processing time while ensuring stress relief effectiveness.

[0100] Calculation process: The stress relaxation curves at different times were simulated, and the stress relief rate reached 75% after 1 hour.

[0101] Path execution steps: The thermo-coupling recombination path designed above and the determined combination of process parameters are strictly executed on the Gleeble thermal simulator to induce directional grain structure recombination in the material, so as to optimize the material performance.

[0102] Results: The TC4 titanium alloy produced after the above treatment exhibited a uniform and fine equiaxed (α+β) microstructure, with an average equiaxed α size of 2.5 μm. Room temperature tensile testing showed a tensile strength of 1005 MPa and an elongation after fracture of 17.5%, demonstrating excellent strength-ductility balance. This microstructure exhibited approximately 28% lower deformation resistance compared to the original microstructure during subsequent simulated rolling.

[0103] For details on the process parameters, optimization objectives, and main models used in each of the above stages, please refer to Table 1.

[0104] Table 1

[0105] Example 2: Improvement of the machinability of Inconel 718 nickel-based alloy ingots: Material: As-cast Inconel 718 nickel-based alloy, containing γ / γ′ eutectic and δ phase.

[0106] Objective: To completely break down the microstructure of the as-cast Inconel 718 nickel-based alloy, suppress grain growth, and provide uniform, fine-grained billets for subsequent hot rolling.

[0107] Model construction: A multi-level physical model of Inconel 718 nickel-based alloy was established, coupling macroscopic thermo-mechanical field, mesoscopic grain evolution process and microscopic defect evolution process.

[0108] Path design sequence: First stage (dissolution and dynamic recrystallization, T1=1120°C, ε1=0.6), =0.05 s - ¹, cooling to 1040°C at 10°C / s): heating to 1120°C (above the δ phase dissolution temperature), holding for 30 min, then cooling at 0.05 s... - ¹Multi-directional forging with true strain εA=0.6 was performed at a strain rate (the model ensures that complete dynamic recrystallization occurs under this parameter combination, initially refining the grains). The cooling rate was controlled, cooling to 1040°C at 10°C / s and then immediately transferred to the next stage (the model was designed with this cooling path to suppress excessive precipitation of the δ phase at grain boundaries while retaining some substructure).

[0109] The second stage (further refinement of subdynamic recrystallization): Deformation with true strain ε=0.25 is immediately performed at 1040°C (using the energy storage and substructure retained in the first stage to induce rapid subdynamic recrystallization), followed by water quenching.

[0110] Path execution: Implemented on a program-controlled forging hydraulic press.

[0111] Results: A fully recrystallized, uniform, fine-grained microstructure (ASTM grade 10 or higher) was obtained, and the original as-cast microstructure was eliminated. The billet pretreated with this method did not crack during standard hot rolling, the rolling force was reduced by approximately 22%, and the uniformity of the finished product microstructure was significantly improved.

[0112] Comparative Example 1: Conventional heat treatment: For the same TC4 forging billet as in Example 1, the industry-standard (α+β) two-phase region solution treatment + aging treatment was adopted: 950°C / 1h solution treatment (air cooling) + 550°C / 4h aging (air cooling).

[0113] Results: The tissue was still mainly composed of lamellar and bundled structures, with incomplete isometricization.

[0114] Performance: Tensile strength 965 MPa, elongation 12%. Subsequent deformation resistance decreased by only about 10%. This indicates that without a specifically designed synergistic pathway for deformation energy storage and thermal activation, effective tissue reorganization and performance leap cannot be achieved.

[0115] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a multi-level physical model for optimizing the properties of difficult-to-deform materials, characterized in that, The construction method includes the following steps: Constructing a macroscopic model: Establishing a constitutive model of the material to connect macroscopic deformation conditions with rheological stress; Constructing a microscale model: Establishing a coupled microscale defect evolution model, including: a dislocation density evolution model, used to describe the proliferation and recovery of dislocation density during material deformation and thermal activation; and a deformation energy storage model, used to calculate the deformation energy stored in the material based on dislocation density. Constructing a mesoscale model: Establishing coupled mesoscale microstructure evolution models, including: a recrystallization kinetic model to describe the grain formation and growth process driven by deformation energy storage; a phase transformation kinetic model to describe the phase transformation behavior of materials during cooling or deformation; and a grain growth model to simulate the grain coarsening process during heat treatment. The macroscopic scale model, mesoscopic scale model and microscopic scale model are coupled and integrated to form the multi-level physical model.

2. The method for constructing a multi-level physical model according to claim 1, characterized in that, The coupling and integration of macro-scale models, meso-scale models, and micro-scale models includes: The macro-scale model, meso-scale model and micro-scale model are transmitted through key bridge variables, which include strain, strain rate, dislocation density and deformation energy storage. The transmission process is as follows: strain, strain rate, and temperature output by the macroscale model drive the evolution of the microscale model; deformation energy storage output by the microscale model drives the evolution of the mesoscale model; and the organizational state variables of the mesoscale model feed back and update the constitutive relation of the macroscale model.

3. The method for constructing a multi-level physical model according to claim 1, characterized in that, The macroscopic constitutive relation model adopts an Arrhenius-type hyperbolic sine constitutive equation that considers dynamic recovery and dynamic recrystallization.

4. The method for constructing a multi-level physical model according to claim 1, characterized in that, The recrystallization kinetic model, based on thermo-mechanical conditions, includes at least one of a dynamic recrystallization model, a static recrystallization model, and a sub-dynamic recrystallization model.

5. A method for optimizing the properties of difficult-to-deform materials based on a multi-level physical model, characterized in that, The method includes the following steps: Path design steps: Based on the multi-level physical model constructed according to any one of claims 1 to 4 and the target microstructure of the evolution of difficult-to-deform materials, a thermo-mechanical coupling and reorganization path is designed in reverse. The thermo-mechanical coupling and reorganization path includes at least two stages with different structural transformation targets, and the process parameter combination for achieving the corresponding target in each stage is determined using the multi-level physical model. Path execution steps: According to the designed thermo-coupling recombination path and the determined combination of process parameters, the difficult-to-deform material is processed to induce directional grain structure recombination, thereby optimizing the material properties.

6. The method for optimizing the properties of difficult-to-deform materials according to claim 5, characterized in that, The reverse design process of the thermo-coupling reorganization path includes: Determine the target microstructure for the evolution of difficult-to-deform materials; Based on the target structure, a thermodynamic processing sequence comprising at least two stages is designed in reverse using the multi-level physical model. The thermodynamic processing sequence includes: The first stage involves introducing deformation energy storage into difficult-to-deform materials through high-temperature deformation, thereby breaking the initial structure or inducing a phase transformation. The second stage involves precisely controlling the deformation energy storage through heat preservation or deformation to drive recrystallization and / or phase transition.

7. The method for optimizing the properties of difficult-to-deform materials according to claim 6, characterized in that, The process parameters (T1, ε1, ...) for the first stage are determined using the multi-level physical model. ); where T1 is the deformation temperature of the first stage, and ε1 is the true strain of the first stage, The strain rate for the first stage includes: The target deformation energy storage value and the dynamic recrystallization volume fraction threshold are used as model inputs; Using the aforementioned multi-level physical model and iterative optimization algorithm, a set of process parameters (T1, ε1, ...) is found. This ensures that when the true strain reaches ε1, the deformation energy storage calculated by the model is not lower than the target deformation energy storage value, and the dynamic recrystallization fraction does not exceed the dynamic recrystallization volume fraction threshold.

8. The method for optimizing the properties of difficult-to-deform materials according to claim 6, characterized in that, In the second stage, when the heat preservation path is adopted, the process parameters (T2, t2) of the second group are determined using the multi-level physical model, where T2 is the processing temperature of the second stage and t2 is the heat preservation time, including: The microstructure of the material at the end of the first stage and the temperature T2 are used as the initial conditions of the model; the microstructure includes dislocation density and substructure characteristics; The decay curve ρ(t) of dislocation density ρ with heat preservation time t was simulated by a multi-level physical model. Instantaneous deformation energy storage is calculated based on the decay curve ρ(t) and the deformation energy storage formula; Determine the holding time t2 such that the instantaneous energy storage at time t2 is not less than the target remaining energy storage value required to drive subsequent recrystallization and / or phase change, and the volume fraction of static recrystallization at time t2 does not exceed the static recrystallization volume fraction threshold.

9. The method for optimizing the properties of difficult-to-deform materials according to claim 6, characterized in that, In the second stage, when a deformation path is adopted, the process parameters (T2, ε2) of the second set are determined using the multi-level physical model, where T2 is the processing temperature of the second stage and ε2 is the true strain of the second stage, including: The microstructure of the material at the end of the first stage and temperature T2 are used as the initial conditions for the model; the microstructure includes dislocation density and substructure characteristics; The total deformation energy storage, determined by the newly introduced dislocation increment and the recovery effect, was calculated using the multi-level physical model after the application of true strain ε2. Determine the true strain ε2 such that the total deformation energy storage meets the target remaining energy storage value required to drive subsequent recrystallization and / or phase transformation.

10. The method for optimizing the properties of difficult-to-deform materials according to claim 6, characterized in that, The thermo-coupling reorganization pathway also includes the control of the cooling rate of the cooling regime to regulate the phase change process or the second phase precipitation behavior.

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