CN119007886A

CN119007886A - Aluminum alloy microstructure simulation method and system based on cellular automaton method

Info

Publication number: CN119007886A
Application number: CN202411032384.5A
Authority: CN
Inventors: 张京京; 李强; 李楷模; 熊志林; 曾鹏
Original assignee: Hunan Industry Polytechnic
Current assignee: Hunan Industry Polytechnic
Priority date: 2024-07-30
Filing date: 2024-07-30
Publication date: 2024-11-22
Anticipated expiration: 2044-07-30
Also published as: CN119007886B

Abstract

The present invention discloses a method and system for simulating aluminum alloy microstructure based on cellular automaton method, wherein the material parameter operation module calculates the aluminum alloy microstructure CA simulation related parameters according to the input aluminum-lithium alloy deformation conditions and heat treatment conditions; the initial structure generation module uses cellular automaton to simulate the aluminum alloy microstructure according to the calculated aluminum alloy microstructure CA simulation related parameters; the aluminum-lithium alloy recrystallization structure simulation module realizes the evolution simulation and prediction of the aluminum alloy recrystallization microstructure to be tested according to the aluminum alloy microstructure CA simulation related parameters, the aluminum alloy microstructure, and the established aluminum-lithium alloy recrystallization CA model, and displays the evolution results in real time. When the present invention uses cellular automaton to simulate the aluminum alloy microstructure, the continuous and discontinuous dynamic recrystallization mechanism of the aluminum-lithium alloy under medium and high temperature deformation conditions is taken into account, and the simulation system can simulate the recrystallized grains formed by the rotation and growth of subgrains.

Description

Aluminum alloy microstructure simulation method and system based on cellular automaton method

Technical Field

The invention relates to the technical field of microstructure simulation, and particularly discloses an aluminum alloy microstructure simulation method and system based on a cellular automaton method.

Background

Various microstructure simulation methods provide a quick, economical and effective way for researching the evolution behavior of the material recrystallization structure. In recent years, the development of a multiscale computing and simulation method based on materialization is rapid, and a plurality of researchers from an atomic scale, a mesoscale to a macroscopic scale have conducted multi-angle deep research on the aspects of material performance prediction, material tissue evolution, microstructure visualization and the like by adopting methods such as a first sex principle, molecular dynamics, discrete dislocation dynamics, continuous dislocation dynamics, crystal plasticity finite element, macroscopic finite element simulation and the like.

Based on the comprehensive research results of the existing microstructure simulation, the Monte Carlo method, the phase field method and the cellular automaton method are mainly adopted at present. Although a plurality of scholars at home and abroad simulate microstructure evolution of the thermal deformation process of various aluminum alloys under different process conditions by adopting a Monte Carlo method, a phase field method and a cellular automaton method, the cellular automaton has large calculation scale and high calculation speed, and can well reproduce microstructure evolution behavior of the thermal processing process. Therefore, the cellular automaton (Cellular Automata, CA for short) based on physical experiments provides an effective means for researching the evolution rule of the material tissue.

At present, many researchers abroad and domestically explore the method for simulating thermal deformation or recrystallization behavior in the heat treatment process by using a cellular automaton.

1) Current state of research abroad

Many researchers abroad have conducted studies on the simulation of the microstructure of materials. For example Yazdipour N et al employ Cellular Automata (CA) to simulate Dynamic Recrystallization (DRX) evolution during thermal deformation; sitko M et al simulate the effect of dynamic recrystallization process time step and cell size on the CA model simulation process by CA. Yanfeng L et al, on the basis of CA simulation theory, established a three-dimensional cellular automaton model for grain growth from the aspects of grain orientation, grain size distribution, grain growth dynamics, grain topology, and the like. Bararpour et Al predict the dynamic recrystallization structure of Al-Mg alloy by using cellular automaton model, and obtain temperature and strain rate results by combining cellular automaton model and three-dimensional finite element model, and predict the microstructure of the coating. In addition to using CA to simulate a dynamic recrystallization process, CA may also be used to simulate a static recrystallization process. The MukhopadhyP and the like establish a static recrystallization CA model, and the model adopts an extensible subgrid technology, can effectively track local changes in the recrystallization process, and comprises grain boundary nucleation, transition zone nucleation and particle drive nucleation.

De Jaeger J et al adopts a three-dimensional cellular automaton method to couple a crystal finite element model with a recrystallization model and apply the crystal finite element model to a three-dimensional compression deformation polycrystalline aggregate, which comprehensively shows the coupling effect between microscopic grain evolution and macroscopic deformation.

Foreign scholars combine cellular automata with various algorithms to simulate static recrystallization organization. For example, MADANI MITRA simulated Static Recrystallization (SRX) during non-isothermal annealing using a new and improved Probability Cellular Automaton (PCA) model. Sitko Mateusz proposes a new grain nucleation adaptive algorithm that is insensitive to CA space size and is suitable for use in a simple SRX grain nucleation process. HASHEMI SEPIDEH under the framework of machine learning, a simulated dataset was generated using a cellular automaton method demonstrating the temporal evolution of microstructure during static recrystallization of Face Centered Cubic (FCC) polycrystalline materials. Amir Asgharzadeh a dynamic model of Static Recrystallization (SRX) of fluid formed steel pipes based on the Cellular Automaton (CA) method was established.

2) Current state of domestic research

Many scholars in China have made many researches on the simulation of the microstructure of the aluminum alloy by the cellular automaton. For example, wang Yongjian establishes a dynamic recrystallization model of a single-phase material and a material containing second-phase particles, and simulates the effect of two-phase particles on dynamic recrystallization behavior by using a cellular automaton method. Shen Gang [30] establishes a coupling model of a deformation Crystal Plastic Finite Element (CPFEM) and a Cellular Automaton (CA) of the multiphase polycrystalline material, and simulates the influence of different annealing temperatures, heating rates, heat preservation time and the like on microstructure evolution. In summary, the use of cellular automata in the field of recrystallization simulation is gradually approaching maturity. Zhang Jie et al established a microstructure evolution CA model during thermal deformation of 7085 aluminum alloys, with initial structure and thermodynamic parameters as input data to the CA model, and dislocation density as internal state variables. The feasibility and predictability of the CA model were verified by experimental data. Liu Lei et al determined that the primary softening mechanism of 2219 aluminum alloy during heat deformation was Continuous Dynamic Recrystallization (CDRX). The microstructure forming method of initial equiaxed crystal and local sub-crystal dispersion is provided, and CDRX in the thermal deformation process of 2219 aluminum alloy is simulated on a Matlab platform.

In addition, domestic scholars have made many studies on static recrystallization organization prediction. For example, tang Xiaole adopts a cellular automaton method to simulate the evolution of static recrystallization structure and texture and analyze the nucleation and grain growth characteristics and the variation trend of energy and grain boundary, but dislocation density and subgrain variation in the deformation process are not led into the cellular automaton, so that the coupling calculation of thermal deformation and heat treatment process is difficult to realize, and the whole deformation recrystallization process is difficult to accurately predict. Guo Yina uses as research object 42CrMo steel as casting state, adopts CA method to simulate static recrystallization grain growth process, describes and analyzes simulated grain morphology from multiple angles of grain size, grain edge number and grain relative area distribution, optimizes grain topology deformation technology, but the recrystallization model is based on grain boundary bow theory, is only suitable for simple recrystallization mechanism of steel material, and is not suitable for complex recrystallization mechanism of aluminum alloy material. Although, many scholars currently conduct researches on the simulation of the evolution of the recrystallization structure of a material from different angles by adopting a cellular automaton method. However, the CA method simulates little and no improvement in microstructure evolution exploration during deformation of aluminum alloys. The first research work on aluminum alloys has been focused on improving formability and optimizing mechanical properties. In addition, dislocation movement promotes dynamic recovery to occur in the thermal deformation process of the aluminum lithium alloy, so that a polygonal structure and a sub-crystal are formed; dynamic recrystallization occurs as deformation continues, forming recrystallized grains; static reversion and static recrystallization also occur during the heat treatment process, forming static recrystallized grains. Thus, the microstructure evolution mechanism of the aluminum-lithium alloy material in the thermoplastic deformation process is very complex.

Although many scholars at home and abroad simulate the microstructure evolution of the thermal deformation process of the aluminum alloy under different process conditions by adopting a cellular automaton method, the traditional simulation is based on the traditional crystal boundary bowing theory, and the coupling effect between the recovery behavior and the intra-crystal recrystallization behavior of the aluminum alloy is ignored. The existing recrystallization simulation system does not comprehensively consider the synergistic effect among dislocation, sub-crystal, dynamic recrystallization and static recrystallization grains, so that the traditional recrystallization simulation method cannot accurately reveal the recrystallization evolution rule in the thermal deformation process of the aluminum-lithium alloy, cannot truly reflect the actual microstructure, and influences the reality and accuracy of the simulation result, thereby limiting the deep development of simulation research work of the aluminum-lithium alloy recrystallization microstructure. In addition, a system grain structure evolution simulation platform is lacking in the process of simulating the aluminum lithium alloy recrystallization structure evolution at present, and the system grain structure evolution simulation platform is difficult to adapt to aluminum lithium alloy material research and development and process experiments.

Disclosure of Invention

The invention provides an aluminum alloy microstructure simulation method and system based on a cellular automaton method, and aims to solve at least one of the technical problems.

One aspect of the invention relates to an aluminum alloy microstructure simulation method based on a cellular automaton method, which comprises the following steps of:

the material parameter operation module calculates the CA simulation related parameters of the microstructure of the aluminum alloy according to the input deformation condition and heat treatment condition of the aluminum lithium alloy;

The initial structure generation module simulates the aluminum alloy microstructure by adopting a cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure;

The aluminum-lithium alloy recrystallization structure simulation module simulates related parameters, an aluminum alloy microstructure and an established aluminum-lithium alloy recrystallization CA model according to the aluminum alloy microstructure CA, realizes the evolution simulation and forecast of the aluminum alloy recrystallization microstructure to be tested, and displays the evolution result in real time.

Further, the deformation condition includes a deformation parameter, the heat treatment condition includes a heat treatment parameter, the aluminum alloy microstructure CA simulation related parameter includes a dislocation density coefficient, a grain boundary mobility M, a grain boundary migration driving force P, a recrystallization critical dislocation density ρ _c, a recrystallization critical energy storage E _c and a maximum storage energy E _max, and the material parameter operation module calculates the aluminum alloy microstructure CA simulation related parameter according to the input deformation condition and heat treatment condition of the aluminum lithium alloy, the step of calculating the aluminum alloy microstructure CA simulation related parameter includes:

According to the deformation parameters and the heat treatment parameters, calculating a dislocation density coefficient, a grain boundary mobility M, a grain boundary migration driving force P, a recrystallization critical dislocation density rho _c, a recrystallization critical storage E _c and a maximum storage energy E _max, wherein the dislocation density coefficient comprises a work hardening coefficient k ₁ and a dynamic recovery coefficient k ₂, and the work hardening coefficient k ₁ and the dynamic recovery coefficient k ₂ are obtained by the following formulas:

wherein ρ _def is dislocation density during deformation, k ₁ is work hardening coefficient, k ₂ is work hardening coefficient, ε is strain, ρ ₀ is initial dislocation density corresponding to strain 0; σ _sat is the saturation stress; σ ₀ is the initial stress; alpha is a material constant equal to 0.5; mu is the shear modulus; b is a berger vector.

Further, the grain boundary mobility M is calculated by the following formula:

If the case of the two-phase particles pinning the grain boundary is not considered, the grain boundary mobility M can be expressed by the following formula:

wherein M is grain boundary mobility, b is a berg vector, δ is grain boundary thickness, Q _gb is grain boundary self-diffusion activation energy, K is Boltzmann constant, T is temperature, R is air constant, 8.314J/mol×k;

If two-phase particle pinning is considered, the grain boundary mobility M can be expressed by the following formula:

Wherein M is grain boundary mobility, M _p is a two-phase particle pinning factor, b is a berg vector, δ is grain boundary thickness, Q _gb is grain boundary self-diffusion activation energy, K is Boltzmann constant, T is temperature, R is air constant, and 8.314J/mol×k.

Further, the grain boundary migration driving force P is calculated by the following formula:

p＝p_s+p_G＝τΔρ-γk_GB

Wherein p is the driving force for grain boundary migration; p _s is dislocation energy accumulated near the grain boundary, and p _G is dislocation energy constituting the grain boundary; τ is the shear stress; Δρ is the amount of change in the dislocation density during recrystallization; gamma is the grain boundary energy and k _GB is the grain boundary curvature.

Further, the recrystallization critical dislocation density ρ _c is obtained by the following formula:

Wherein ρ _c is the recrystallization critical dislocation density, and E _c is the recrystallization critical energy storage; τ is the shear stress;

The recrystallization critical storage energy E _c is obtained by the following formula:

Wherein E _c is the recrystallization critical energy storage, ε _c is the recrystallization critical strain; a and b are constants; gamma _LAG is the small angle grain boundary energy;

The maximum storage energy E _max is obtained by the following formula:

E_max＝τ×ρ_max

Where E _max is the maximum stored energy, τ is the shear stress, ρ _max is the maximum dislocation density.

Further, the aluminum alloy microstructure CA simulation-related parameters further include a continuous recrystallized area fraction f _CDRX and a discontinuous recrystallized area fraction f _DDRX, and the continuous recrystallized area fraction f _CDRX and the discontinuous recrystallized area fraction f _DDRX are obtained by the following formulas:

f_CDRX＝S_CDRX/S

f_DDRX＝S_DDRX/S

Where f _CDRX is the fraction of continuous recrystallized area, f _DDRX is the fraction of discontinuous recrystallized area, S _CDRX is the total area of continuous dynamic recrystallized grains, and S is the total area of dynamic recrystallization.

Further, the step of simulating the aluminum alloy microstructure by the initial microstructure generating module according to the calculated related parameters of the aluminum alloy microstructure CA by using a cellular automaton comprises the following steps:

Judging whether dynamic recrystallization occurs according to cell storage; when the stored energy E (i, j) of the Cell (i, j) is larger than the critical stored energy E _CDRX of continuous dynamic recrystallization and the recrystallization probability is larger than a random number, continuous dynamic recrystallization occurs at the Cell;

If the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical stored energy E _CDRX, then further determining if the stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDR X;

If the stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX, and the Cell (i, j) is at the grain boundary with a recrystallization probability greater than a random number, discontinuous dynamic recrystallization nucleation and growth occurs at the Cell (i, j); if the stored energy E (i, j) at the Cell (i, j) is less than the discontinuous dynamic recrystallization threshold storage E _DDRX, then only dynamic recovery occurs at the Cell (i, j).

Further, the step of simulating the aluminum alloy microstructure by using the cellular automaton according to the calculated related parameters of the aluminum alloy microstructure CA simulation by the initial microstructure generation module further comprises the following steps:

In the heat preservation stage, judging whether static recrystallization occurs according to cell storage energy; when the stored energy E (i, j) at the Cell (i, j) is greater than or equal to the continuous dynamic recrystallization critical stored energy E _CDRX, then static reversion and static recrystallization occur, the Cell is converted into a static recrystallized grain Cell;

When the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical energy storage E _CDRX, then only a static reply occurs.

Further, the aluminum-lithium alloy recrystallization microstructure simulation module simulates related parameters, an aluminum alloy microstructure and an established aluminum-lithium alloy recrystallization CA model according to the aluminum alloy microstructure CA, realizes the evolution simulation and forecast of the aluminum alloy recrystallization microstructure to be tested, and displays the evolution result in real time, wherein the steps comprise:

Outputting the average size of DRX grains, the angle of DRX grain orientation difference, the volume fraction of DRX, the angle of SRX grain orientation difference and the volume fraction of SRX of the aluminum alloy recrystallization microstructure to be detected.

Another aspect of the invention relates to an aluminum alloy microstructure simulation system based on a cellular automaton method, which is applied to the aluminum alloy microstructure simulation method based on the cellular automaton method, wherein the aluminum alloy microstructure simulation system based on the cellular automaton method comprises an initial microstructure module, a dynamic recovery module, a dynamic recrystallization module and a static recrystallization module,

The initial organization module is used for inputting deformation parameters and heat treatment parameters of the aluminum alloy to be measured and calculating CA simulation related parameters of the microstructure of the aluminum alloy;

The dynamic recovery module is used for if the storage energy of the Cell (i, j) is larger than the critical storage energy generated by the subgrain, and the orientation angles ori of the four neighbors of the Cell are the same; then the Cell (i, j) is identified as a new subcell Cell, labeled newsubflag; if the current sub-crystal size delta _sub is smaller than the sub-crystal steady-state size delta _ss, calculating a sub-crystal growth driving force p and a cell displacement increment through the energy storage difference between cells; if the spacing drxdistance between the subgrystals is greater than one Cell size and a new subgrystal marker newsubflag equal to 1 is present in four neighbor cells of the Cell (i, j), the State variable State (i, j), grain boundary variable GB (i, j), dislocation variable D (i, j), and energy variable E (i, j) of the Cell (i, j) are updated according to the subgrystal growth rule, and marked with newsubflag =1;

the dynamic recrystallization module is used for judging whether dynamic recrystallization occurs according to the cell storage energy; when the stored energy E (i, j) of the Cell (i, j) is larger than the critical stored energy E _CDRX of continuous dynamic recrystallization and the recrystallization probability is larger than a random number, continuous dynamic recrystallization occurs at the Cell; if the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical stored energy E _CDRX, then further determining if stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX; if the stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX, and the Cell (i, j) is at the grain boundary with a recrystallization probability greater than a random number, discontinuous dynamic recrystallization nucleation and growth occurs at the Cell (i, j); if the stored energy E (i, j) at the Cell (i, j) is less than the discontinuous dynamic recrystallization threshold energy E _DDRX, then only dynamic replies occur at the Cell (i, j);

The static recrystallization module is used for judging whether static recrystallization occurs according to cell storage in the heat preservation stage; when the stored energy E (i, j) at the Cell (i, j) is greater than or equal to the continuous dynamic recrystallization critical stored energy E _CDRX, then static reversion and static recrystallization occur, the Cell is converted into a static recrystallized grain Cell; when the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical energy storage E _CDRX, then only a static reply occurs.

The beneficial effects obtained by the invention are as follows:

The invention provides an aluminum alloy microstructure simulation method and system based on a cellular automaton method, wherein a material parameter operation module calculates a CA simulation related parameter of an aluminum alloy microstructure according to an input deformation condition and a heat treatment condition of an aluminum lithium alloy; the initial structure generation module simulates the aluminum alloy microstructure by adopting a cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure; the aluminum-lithium alloy recrystallization structure simulation module simulates related parameters, an aluminum alloy microstructure and an established aluminum-lithium alloy recrystallization CA model according to the aluminum alloy microstructure CA, realizes the evolution simulation and forecast of the aluminum alloy recrystallization microstructure to be tested, and displays the evolution result in real time. According to the aluminum alloy microstructure simulation method and system based on the cellular automaton method, when the cellular automaton is adopted to simulate the aluminum alloy microstructure, a continuous and discontinuous dynamic recrystallization mechanism of the aluminum lithium alloy under the medium-high temperature deformation condition is considered, and the simulation system can simulate recrystallized grains formed by the large rotary growth of sub-crystals; the aluminum alloy microstructure CA simulation system can simulate the simultaneous occurrence of multiple dynamic recrystallization mechanisms in the thermal deformation process of the aluminum lithium alloy, and can predict an aluminum lithium alloy comprehensive dynamics model based on CDRX and DDRX mechanisms; the influence of subgrain boundary migration and recrystallization grain boundary migration on dislocation density in the thermal deformation process of the aluminum alloy material is considered in CA simulation, so that an accurate dislocation density model is established, and the recrystallization behavior evolution process is more close to the actual tissue evolution process; the method provides a total-process microstructure evolution CA method comprising thermal deformation, dynamic recovery, dynamic recrystallization and static recrystallization, and develops microstructure evolution simulation prediction software suitable for the total thermal deformation process of the aluminum-lithium alloy.

Drawings

Fig. 1 is a schematic flow chart of an aluminum alloy microstructure simulation method based on a cellular automaton method.

Detailed Description

In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.

As shown in fig. 1, a first embodiment of the present invention provides an aluminum alloy microstructure simulation method based on a cellular automaton method, which includes the following steps:

and S100, calculating the simulation related parameters of the microstructure CA of the aluminum alloy by the material parameter operation module according to the input deformation condition and heat treatment condition of the aluminum lithium alloy.

The material parameter operation module: and calculating parameters such as maximum dislocation density, time variable, simulation step length, critical strain, critical dislocation, critical energy storage, continuous dynamic recrystallization grain fraction and discontinuous dynamic recrystallization grain fraction, steady-state sub-crystal, grain size and the like according to the input deformation conditions and heat treatment conditions.

Step S200, the initial structure generation module simulates the aluminum alloy microstructure by adopting a cellular automaton according to the calculated related parameters of the aluminum alloy microstructure CA.

An initial organization generation module: and simulating an initial microstructure by adopting CA according to parameters such as a cellular space, an initial grain size, an initial sub-grain size, an initial grain orientation difference angle and the like.

And S300, an aluminum-lithium alloy recrystallization microstructure simulation module simulates related parameters, an aluminum alloy microstructure and an established aluminum-lithium alloy recrystallization CA model according to the aluminum alloy microstructure CA, realizes the evolution simulation and forecast of the aluminum alloy recrystallization microstructure to be tested, and displays the evolution result in real time.

Simulation of aluminum lithium alloy recrystallization structure: based on the two modules, according to a recrystallization CA simulation flow, the simulation and forecast of the evolution of the recrystallization microstructure of the 2195 aluminum alloy are realized, and the evolution result is displayed in real time.

This example illustrates the implementation of a CA to simulate the dynamic recovery, dynamic and static recrystallization grain evolution process.

CA simulation related parameter calculation of aluminum alloy microstructure

(1) Calculation of k ₁、k₂

During dynamic reversion, dislocation density work hardens the accumulated dislocation density and the consumed dislocation density composition of the dynamic reversion. First, as can be seen from equation (1), the rheological stress σ is proportional to μb ρ ^1/2, from which the dislocation density σ at a specific time is calculated.

σ＝αμbρ^1/2 (1)

In formula (1), σ represents the rheological stress, α represents the material constant, equal to 0.5, μ represents the shear modulus, b represents the berger vector, and ρ represents the dislocation density.

Dynamic reversion not only reduces the work hardening effect, but also alters the dislocation structure. Thus, the variation in dislocation density ρ _def during deformation can be expressed by formula (2).

In formula (2), ρ _def represents dislocation density during deformation, ε represents strain, and k ₁ is work hardening coefficient related to statistically stored dislocations of heat accumulation; k ₂ is the dynamic recovery thermal activation coefficient.

And (3) carrying out integral solution on the formula (2) to obtain a dislocation density expression in the dynamic recovery process, wherein the dislocation density expression is shown in the formula (3).

In equation (3), k ₁ is the work hardening coefficient associated with the statistically stored dislocations for heat accumulation; k ₂ is the dynamic recovery thermal activation coefficient; ρ ₀ is the corresponding initial dislocation density at strain 0; ρ _def represents dislocation density during deformation and ε represents strain.

In formula (4), ρ _def is the dislocation density during deformation, k ₁ is the work hardening coefficient, k ₂ is the work hardening coefficient, ε is the strain, ρ ₀ is the initial dislocation density corresponding to the strain of 0; σ _sat is the saturation stress; σ ₀ is the initial stress; alpha represents a material constant equal to 0.5; mu is the shear modulus; b is a berger vector. σ ₀ is the initial stress, which can be calculated by σ ₀＝αμbρ₀/2; σ _sat is the saturation stress; σ _def is the same as the true stress before dynamic recrystallization occurs; k ₂ can be obtained by linear fitting of-2 ln [ (sigma _sat-σ_def)/(σ_sat-σ₀) ] to epsilon.

In equation (5), σ _sat is the saturation stress, α represents the material constant, equal to 0.5, μ represents the shear modulus, b represents the berg vector, and k ₁ is the work hardening coefficient related to the statistically stored dislocation of heat accumulation; k ₂ is the dynamic recovery thermal activation coefficient.

In order to calculate the work hardening coefficient k ₁ and the dynamic recovery coefficient k ₂, it is critical to determine the saturation stress σ _sat before the recrystallization starts from the stress strain curve. The recrystallization critical stress σ _c and the saturation stress σ _sat can be obtained by a work hardening rate θ versus stress σ curve. Accordingly, the work hardening coefficient k ₁ and the dynamic recovery coefficient k ₂ are obtained by a linear fitting method.

(2) Calculation of K _soft

The introduction of the dynamic recrystallization softening factor K _soft into the dislocation density model (equation 2), the discontinuous dynamic recrystallization process dislocation density ρ _drx can be expressed as:

In formula (6), ρ _drx represents the dislocation density of the discontinuous dynamic recrystallization process, ε represents the strain, and k ₁ is the work hardening coefficient related to the statistically stored dislocations of heat accumulation; k ₂ is the dynamic recovery thermal activation coefficient; k _soft represents a dynamic recrystallization softening factor.

The softening factor K _soft has a value in the range 0<K _soft<k₁, which represents the dislocation density consumed by the large angle grain boundary migration.

Steady state stress σ _ss is the stress value at which work hardening, dynamic recovery and dynamic recrystallization reach dynamic equilibrium. Thus, the softening factor K _soft can be calculated by σ _ss＝αμb(k₁-K_soft)/k₂ as shown in formula (7).

K_soft＝11.5×10⁸-3.79×10⁷×lnZ (7)

In the formula (7), K _soft represents a dynamic recrystallization softening factor, and Z represents a Zener-Hollomon parameter, that is, a Zener-solomon parameter, which can be obtained specifically by the following formula.

Wherein, Is the strain rate, Q is the deformation activation energy, T is the deformation temperature, R is the air constant, 8.314, unit J/mol K.

(3) Calculating the maximum dislocation density ρ _max

In formula (9), ρ _max represents the maximum dislocation density, and k ₁ is the work hardening coefficient related to the statistically stored dislocations of heat accumulation; k ₂ is the dynamic recovery heat activation coefficient and K _soft is the dynamic recrystallization softening factor.

(4) Calculating maximum storage energy E _max

E_max＝τ×ρ_max (10)

In formula (10), E _max represents the maximum stored energy, τ represents the shear stress, ρ _max represents the maximum dislocation density.

The dislocation density in the new crystal grain is set to be the initial dislocation density ρ _initial, close to 0.

(5) Predicting stress in deformation process of aluminum-lithium alloy

In the formula (11), σ _drx represents stress during deformation of the aluminum-lithium alloy, σ _ss represents steady-state stress, α represents material constant, equal to 0.5, μ represents shear modulus, b represents a berg vector, ρ _initial represents initial dislocation density inside new crystal grains, k ₂ represents dynamic recovery thermal activation coefficient, ε represents strain, ε _crit is equal to ε _c, and the recrystallization critical strain is obtained.

(6) Calculation of the recrystallization critical energy storage E _c

When the grain energy storage reaches the recrystallization critical energy storage, new crystal nuclei begin to form inside the material. The recrystallization critical energy storage corresponding to different deformation conditions is different. When calculating critical energy storage, linear fitting solution can be carried out through the relation between macroscopic strain and microscopic energy storage.

E_s＝αGb²ρ_s＝τ×ρ_s (12)

In formulas (12) and (13), es is the stored energy; ec is the recrystallization critical energy storage; c _E、n_E is the material constant; q _E is stored energy activation energy; τ represents the shear stress, α represents the material constant equal to 0.5, g represents the shear modulus, b represents the berger vector, ρ _s represents the storage dislocation density.

In formula (14), E _c is the recrystallization critical storage, γ _LAG is the small angle grain boundary energy; a and b are constants and ε _c is the recrystallization critical strain.

(7) Calculation of the recrystallization critical dislocation Density ρ _c

In formula (15), ρ _c represents the recrystallization critical dislocation density, R _c represents the recrystallization critical storage energy, and τ represents the shear stress.

(8) Calculation of grain boundary mobility M

In the process of grain growth, the increase of the average grain size corresponds to the decrease of the total area of the grain boundary, and the grain boundary energy decreases. Grain boundaries can in turn affect grain growth. The grain growth rate is generally expressed by formula (16).

v＝MP_ress (16)

In the formula (16), v is the grain boundary moving speed; p _ress is the driving force for grain boundary movement per unit area, which can be derived from the difference in energy storage between the recrystallized grains and the deformed matrix, and M is the high angle grain boundary mobility.

1) Grain boundary mobility M and two-phase particle pinning factor M _p

If the case of the two-phase particles pinning the grain boundary is not considered, the grain boundary mobility M can be expressed by the formula (17).

In formula (17), M is the grain boundary mobility, b is the berg vector, δ is the grain boundary thickness, K is the Boltzmann constant, T is the deformation temperature, R is the air constant, 8.314, unit J/mol×k, and Q _gb is the grain boundary self-diffusion activation energy.

If two-phase particle pinning is considered, the grain boundary mobility is expressed by formula (18).

In formula (18), M is grain boundary mobility, T is deformation temperature, R is air constant, 8.314, unit J/mol×k, M _p is a two-phase particle pinning factor, b is a berger vector, δ is grain boundary thickness, δ=1.4x10- ¹⁰m;Q_gb (J/mol) is grain boundary self-diffusion activation energy, K (J/K) is Boltzmann constant, and k= 1.380649 ×10 ^-23 J/K. The initial dislocation density ρ ₀＝10¹⁰m^-2.

The two-phase particle pinning factor M _p can be calculated by equation (19).

In the formula (19), ρ _c is dislocation density of the recrystallized component, σ _c is critical stress of recrystallization, γ _m is large-angle grain boundary energy,For strain rate, l is dislocation line length, τ is shear stress,Is the initial strain rate, equal to 1.

2) Grain boundary migration driving force P

If the Cell (i, j) is not recrystallized, but the neighbor Cell (i+1, j) is recrystallized, and an energy storage difference exists between the two cells, the Cell (i, j) is affected by the storage energy and has grain boundary migration; when both Cell (i, j) and Cell (i+1, j) are recrystallized and the two cells have different crystal orientation angles, the driving force for grain boundary migration is grain boundary energy.

P is the driving force acting on the unit area, and can be expressed specifically as:

p＝p_s+p_G＝τΔρ-γk_GB (20)

In the formula (20), p is a driving force acting on a unit area, p _s is stored energy, p _G is grain boundary energy, τ is shear stress, and grain boundary migration driving force p mainly results from reduction of stored energy and grain boundary energy. The stored energy p _S and the grain boundary energy p _G are dislocation dependent. The stored energy ps refers to dislocation energy accumulated near the grain boundary; grain boundary energy p _G refers to dislocation energy constituting the grain boundary; Δρ is the amount of change in the dislocation density during recrystallization; k _GB is the grain boundary curvature as shown in formula (21). Gamma is the grain boundary energy, and gamma can be divided into a large angle grain boundary energy gamma _HAG and a small angle grain boundary energy gamma _LAG, which are specifically expressed as follows:

θ_i＝π/2×|Δq|/q_max (22)

in formulas (21) and (22), θ _i is the orientation difference angle between the i-th crystal grain and its adjacent crystal grain; θ ₀ is the initial grain orientation difference angle, γ _HAG is the high angle grain boundary energy, γ _LAG is the low angle grain boundary energy; pi is equal to 3.14159267; Δq is the orientation difference of adjacent grains, q _max is the maximum orientation angle of the grains, and the large angle orientation difference angle of θ ₀ is equal to 15 °, 0+|Δq|/q _max <1.

The grain orientation difference angle change value can be expressed by the formula (23):

In formula (23), dθ _grain is the difference in the angle change of the grain orientation, k ₂ is the dynamic recovery coefficient, ρ _i is the dislocation density of the ith grain, ε is the strain, b is the modulus of the bergs vector, and b=2.86×10 ^-10; n is the number of dislocation sets at the grain boundaries; for example, set n=5×106; α is the percentage of dislocation density consumed in recovery during formation of the sub-crystals, α=0.5; d is the average grain size in μm.

The high angle grain boundary energy can be expressed as:

In the formula (24), gamma _HAG is the large-angle grain boundary energy, theta ₀ is the initial orientation difference angle of the crystal grains, pi is the circumference ratio, and v is the poisson ratio and is equal to 0.35; mu is the shear modulus. b is the scalar of the berger vector, b=2.86×10 ^-10. When the grain boundary can be driven, the driving force P can be divided into two cases: the first is that the driving force at the time of two-phase particle pinning is not considered, specifically as shown in formula (25).

P_G＝γ_HAGk_GB (25)

In the formula (25), P _G is a driving force when two-phase particle pinning is not considered, γ _HAG is a large angle grain boundary energy, and k _GB is a grain boundary curvature.

And secondly, the driving force of particle pinning is considered as shown in a formula (26).

In the formula (26), P _G is a driving force considering particle pinning, and γ _LAG is a small-angle grain boundary energy; f is the volume fraction of spherical particles; r _p is the particle radius. Gamma _HAG is the high angle grain boundary energy and k _GB is the grain boundary curvature. The volume fraction f of the two-phase particles in formula (26) is the volume fraction of all the two-phase particles in the matrix. The two-phase particles located inside the grains have no pinning effect on migration of grain boundaries. Therefore, the pinning force is calculated using the volume fraction of the two-phase particles located at the grain boundaries during the grain growth after the recrystallization is completed.

The grain boundary curvature k _GB can be expressed specifically as:

in the formula (27), k _GB is the curvature of the grain boundary, and A is a fitting parameter; c _s is the cell size; kink is a cell belonging to the grain i in the neighbor cell when the interface is assumed to be a flat interface, i.e., k=0, and Kink is equal to 14; n _i is the cell belonging to grain i among the neighbor cells, n+1 is the number of all cells belonging to the long range Moore neighbor (n+1: N is the number of first and second nearest neighbors), n=24, where a=1.28. When the grain boundary shape is straight, k _GB is equal to 0; when the grain boundaries are convex, k _GB is greater than 0; when the grain boundaries are concave, k _GB is less than 0.

(9) Time step Δt

In the CA simulation, a time step is often used. Therefore, the strain needs to be translated into a time step in the CA simulation. First, the shortest time required for growing one cell is taken as the time step of the model, i.e., the ratio of the cell size c _s to the maximum grain boundary movement rate v _max. The expression for the time step Δt is as follows:

In equation (28), Δt is the time step and c _s is the cell size; v _max is the maximum grain boundary movement rate, M is the grain boundary mobility, τ is the shear stress, ρ _max is the maximum dislocation density, K ₂ is the dynamic recovery coefficient, K ₁ is the work hardening coefficient, and K _soft is the recrystallization softening coefficient.

(10) Number of initial sub-crystals n _sub

According to the test, the average size of the initial sub-crystals is delta, and the ratio of the area of the sub-crystals is f _sub.

And calculating the initial number of the subgrain according to the initial subgrain size and the subgrain boundary area fraction.

In the formulas (29) and (30), n _sub is the number of subgrain, pi is the circumference ratio, δ is the subgrain diameter, S _sub is the subgrain area, f _sub is the ratio of the subgrain area, S _total is the area of the whole simulation area, n _x is the number of transverse grids of the simulation area, n _y is the number of longitudinal grids of the simulation area, and L ₀ is the initial grid length.

N _sub is equal to 687 calculated according to equation (29) and equation (30). Similarly, the number of initial grains in a certain simulation area can be obtained according to the initial grain size. Where n _x and n _y are the number of analog region meshes.

(11) Continuous recrystallized area fraction f _CDRX, discontinuous recrystallized area fraction f _DDRX

f_CDRX＝S_CDRX/S (31)

f_DDRX＝S_DDRX/S (32)

In the formulas (31) and (32), f _CDRX is a continuous recrystallized area fraction, and f _DDRX is a discontinuous recrystallized area fraction. It is assumed that the microstructure is composed of continuous dynamic recrystallized grains and discontinuous dynamic recrystallized grains. S is the total area of dynamic recrystallization in the EBSD map; s _CDRX is the total area of the continuous dynamic recrystallization grains, which can be obtained by counting the dynamic recrystallization grains in the deformed grains; s _DDRX is the total discontinuous dynamic recrystallized grain area, which can be obtained by counting the dynamic recrystallized grain area on the deformed grain boundaries.

(12) The calculation of the continuous dynamic recrystallization critical energy storage E _CDRX, the discontinuous recrystallization critical energy storage E _DDRX, E is the energy storage when simulating step i.

(13) Noun interpretation: the English representation of continuous dynamic recrystallization is Continue Dynamic Recrystallization, CDRX for short; the English representation of discontinuous dynamic recrystallization is Discontinue Dynamic Recrystallization, abbreviated as DDRX; the English representation of dynamic recrystallization is Dynamic Recrystallization, DRX for short; ρ _def is the dislocation density of the deformed grains; ρ _drx is the dislocation density of the dynamic recrystallized grains; dynamic reply is that English is Dynamic Recovery, called DRV for short; the English representation of static recrystallization is Static Recrystallization, abbreviated as SRX; the English representation of Static reply is Static Recovery, SRV for short. The english expression of cellular automata is Cellular automata, abbreviated CA.

Further, the aluminum alloy microstructure simulation method based on the cellular automaton method provided in this embodiment, step S200 includes:

Step S210, judging whether dynamic recrystallization occurs according to the cell storage energy; when the stored energy E (i, j) of a Cell (i, j) is greater than the critical stored energy E _CDRX for continuous dynamic recrystallization and the probability of recrystallization is greater than a random number, continuous dynamic recrystallization occurs at that Cell.

When the stored energy E (i, j) of a Cell (i, j) is greater than the critical stored energy E _CDRX for continuous dynamic recrystallization and the probability of recrystallization is greater than a random number, continuous dynamic recrystallization occurs at the Cell. This means that the Cell (i, j) orientation angle continuously changes.

Step S220 further determines whether the stored energy E (i, j) at the Cell (i, j) is greater than the discontinuous dynamic recrystallization critical stored energy E _DDRX if the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical stored energy E _CDRX.

If the storage energy at the Cell (i, j) is less than the continuous dynamic recrystallization critical storage E _CDRX, then a determination is made as to whether the storage energy is greater than the discontinuous recrystallization critical storage E _DDRX.

Step S230, if the stored energy E (i, j) is larger than the discontinuous recrystallization critical stored energy E _DDRX, and the Cell (i, j) is at the crystal boundary, when the recrystallization probability is larger than the random number, discontinuous dynamic recrystallization nucleation and growth occur at the Cell (i, j); if the stored energy E (i, j) at the Cell (i, j) is less than the discontinuous dynamic recrystallization threshold storage E _DDRX, then only dynamic recovery occurs at the Cell (i, j).

If so, and the Cell (i, j) is at the grain boundary with a recrystallization probability greater than the random number, discontinuous dynamic recrystallization nucleation and growth occurs at the Cell (i, j). If the cell memory energy is less than the discontinuous dynamic recrystallization threshold energy storage E _DDRX, then only dynamic reversion occurs at the cell.

Preferably, the aluminum alloy microstructure simulation method based on the cellular automaton method provided in this embodiment, step S200 further includes:

Step S240, in the heat preservation stage, judging whether static recrystallization occurs according to cell storage energy; when the stored energy E (i, j) at the Cell (i, j) is greater than or equal to the continuous dynamic recrystallization critical stored energy E _CDRX, then a static reversion and static recrystallization occurs, the Cell transitions to a static recrystallized grain Cell.

When the cell energy storage is greater than or equal to the recrystallization critical energy storage, static reversion and static recrystallization occur, and the cell is converted into a static recrystallized grain cell.

In step S250, only static recovery occurs when the storage energy E (i, j) at the Cell (i, j) is smaller than the continuous dynamic recrystallization critical storage energy E _CDRX.

Only static recovery occurs when the storage energy is less than the recrystallization threshold energy storage. Therefore, by comparing the stored energy of the cells with the recrystallization critical stored energy, it can be judged whether recrystallized grains are formed in the deformed microstructure and the heat-treated microstructure.

Further, the aluminum alloy microstructure simulation method based on the cellular automaton method provided in this embodiment, step S300 includes:

Outputting a result: DRX grain average size, DRX grain orientation difference angle, DRX volume fraction, SRX grain size, SRX grain orientation difference angle, and SRX volume fraction.

The invention further relates to an aluminum alloy microstructure simulation system based on a cellular automaton method, which is applied to the aluminum alloy microstructure simulation method based on the cellular automaton method, and comprises an initial microstructure module, a dynamic recovery module, a dynamic recrystallization module and a static recrystallization module, wherein the initial microstructure module is used for inputting deformation parameters and heat treatment parameters of an aluminum alloy to be measured and calculating CA simulation related parameters of the aluminum alloy microstructure; the dynamic recovery module is used for if the storage energy of the Cell (i, j) is larger than the critical storage energy generated by the subgrain, and the orientation angles ori of the four neighbors of the Cell are the same; Then the Cell (i, j) is identified as a new subcell Cell, labeled newsubflag; if the current sub-crystal size delta _sub is smaller than the sub-crystal steady-state size delta _ss, calculating a sub-crystal growth driving force p and a cell displacement increment through the energy storage difference between cells; if the spacing drxdistance between the subgrystals is greater than one Cell size and a new subgrystal marker newsubflag equal to 1 is present in four neighbor cells of the Cell (i, j), the State variable State (i, j), grain boundary variable GB (i, j), dislocation variable D (i, j), and energy variable E (i, j) of the Cell (i, j) are updated according to the subgrystal growth rule, and marked with newsubflag =1; The dynamic recrystallization module is used for judging whether dynamic recrystallization occurs according to the cell storage energy; when the stored energy E (i, j) of the Cell (i, j) is larger than the critical stored energy E _CDRX of continuous dynamic recrystallization and the recrystallization probability is larger than a random number, continuous dynamic recrystallization occurs at the Cell; if the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical stored energy E _CDRX, then further determining if stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX; If the stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX, and the Cell (i, j) is at the grain boundary with a recrystallization probability greater than a random number, discontinuous dynamic recrystallization nucleation and growth occurs at the Cell (i, j); if the stored energy E (i, j) at the Cell (i, j) is less than the discontinuous dynamic recrystallization threshold energy E _DDRX, then only dynamic replies occur at the Cell (i, j); The static recrystallization module is used for judging whether static recrystallization occurs according to cell storage in the heat preservation stage; when the stored energy E (i, j) at the Cell (i, j) is greater than or equal to the continuous dynamic recrystallization critical stored energy E _CDRX, then static reversion and static recrystallization occur, the Cell is converted into a static recrystallized grain Cell; when the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical energy storage E _CDRX, then only a static reply occurs.

The initial organization module generates an initial simulation organization; then, inputting deformation parameters and heat treatment parameters (deformation temperature T, strain rate, strain epsilon, heat preservation T _srx temperature and heat preservation time T) of the aluminum-lithium alloy, and calculating parameters such as dislocation density coefficients k1, k2, k _soft, grain boundary mobility M, driving force P, f _CDRX、f_DDRX, critical dislocation rho _c, critical energy storage E _c, maximum energy storage E _max and the like; the CA simulation cycle number Nstep is determined based on the strain delta epsilon and the simulation is started.

And obtaining the matrix dislocation density and the recrystallization dislocation density in the thermal deformation process of the aluminum alloy according to the dislocation density evolution model in the recovery and recrystallization processes. At the same time, the cell storage energy E is calculated.

The dynamic recovery module is used for if the storage energy of the Cell (i, j) is larger than the critical storage energy generated by the sub-crystal, and the orientation angles ori of the four neighbors of the Cell are the same, which means that the Cell is inside the crystal grain. Then the Cell (i, j) is labeled newsubflag as a new subcell Cell and has a dislocation density of 1 x 10 ^-10. 2) If the current subcellular size delta _sub is smaller than the subcellular steady-state size delta _ss, the subcellular long driving force p and the cell displacement increment are calculated by the energy storage difference between the cells. 3) If the spacing drxdistance between the sub-crystals is greater than one Cell size and there is a new sub-crystal flag newsubflag equal to 1 in four neighbor cells of the Cell (i, j). This indicates that there are new sub-crystals around the four neighbors of the Cell (i, j). Then the State variable State (i, j) of the Cell (i, j), the grain boundary variable GB (i, j), the dislocation variable D (i, j), the energy variable E (i, j) are updated according to the sub-grain growth rule, and marked with newsubflag =1.

The dynamic recrystallization module is used for judging whether dynamic recrystallization occurs according to the cell storage energy.

1) When the stored energy E (i, j) of a Cell (i, j) is greater than the critical stored energy E _CDRX for continuous dynamic recrystallization and the probability of recrystallization is greater than a random number, continuous dynamic recrystallization occurs at the Cell. This means that the Cell (i, j) orientation angle continuously changes. 2) If the storage energy at the Cell (i, j) is less than the continuous dynamic recrystallization critical storage E _CDRX, then a determination is made as to whether the storage energy is greater than the discontinuous recrystallization critical storage E _DDRX. If so, and the Cell (i, j) is at the grain boundary with a recrystallization probability greater than the random number, discontinuous dynamic recrystallization nucleation and growth occurs at the Cell (i, j). 3) If the cell memory energy is less than the discontinuous dynamic recrystallization threshold energy storage E _DDRX, then only dynamic reversion occurs at the cell.

In the heat preservation stage, the static recrystallization module is used for generating static reversion and static recrystallization when the cell energy storage is greater than or equal to the recrystallization critical energy storage, and the cell is converted into a static recrystallization grain cell. 2) Only static recovery occurs when the storage energy is less than the recrystallization threshold energy storage.

Therefore, by comparing the stored energy of the cells with the recrystallization critical stored energy, it can be judged whether recrystallized grains are formed in the deformed microstructure and the heat-treated microstructure.

Compared with the prior art, the aluminum alloy microstructure simulation method and system based on the cellular automaton method provided by the embodiment calculate CA simulation related parameters of the aluminum alloy microstructure according to the input deformation conditions and heat treatment conditions of the aluminum lithium alloy through the material parameter operation module; the initial structure generation module simulates the aluminum alloy microstructure by adopting a cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure; the aluminum-lithium alloy recrystallization structure simulation module simulates related parameters, an aluminum alloy microstructure and an established aluminum-lithium alloy recrystallization CA model according to the aluminum alloy microstructure CA, realizes the evolution simulation and forecast of the aluminum alloy recrystallization microstructure to be tested, and displays the evolution result in real time. According to the aluminum alloy microstructure simulation method and system based on the cellular automaton method, when the cellular automaton is adopted to simulate the aluminum alloy microstructure, a continuous and discontinuous dynamic recrystallization mechanism of the aluminum lithium alloy under the medium-high temperature deformation condition is considered, and the simulation system can simulate recrystallized grains formed by the large rotary growth of sub-crystals; the aluminum alloy microstructure CA simulation system can simulate the simultaneous occurrence of multiple dynamic recrystallization mechanisms in the thermal deformation process of the aluminum lithium alloy, and can predict an aluminum lithium alloy comprehensive dynamics model based on CDRX and DDRX mechanisms; the influence of subgrain boundary migration and recrystallization grain boundary migration on dislocation density in the thermal deformation process of the aluminum alloy material is considered in CA simulation, so that an accurate dislocation density model is established, and the recrystallization behavior evolution process is more close to the actual tissue evolution process; the method provides a total-process microstructure evolution CA method comprising thermal deformation, dynamic recovery, dynamic recrystallization and static recrystallization, and develops microstructure evolution simulation prediction software suitable for the total thermal deformation process of the aluminum-lithium alloy.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. The aluminum alloy microstructure simulation method based on the cellular automaton method is characterized by comprising the following steps of:

The initial structure generation module simulates the microstructure of the aluminum alloy by adopting a cellular automaton according to the calculated CA simulation related parameters of the microstructure of the aluminum alloy;

And the aluminum-lithium alloy recrystallization structure simulation module realizes the evolution simulation and forecast of the aluminum alloy recrystallization microstructure to be tested according to the aluminum alloy microstructure CA simulation related parameters, the aluminum alloy microstructure and the established aluminum-lithium alloy recrystallization CA model, and displays the evolution result in real time.

2. The cellular automaton-based aluminum alloy microstructure simulation method of claim 1, wherein the deformation condition includes a deformation parameter, the heat treatment condition includes a heat treatment parameter, the aluminum alloy microstructure CA simulation-related parameter includes a dislocation density coefficient, a grain boundary mobility M, a grain boundary migration driving force P, a recrystallization critical dislocation density ρ _c, a recrystallization critical storage energy E _c, and a maximum storage energy E _max, and the material parameter operation module calculates the aluminum alloy microstructure CA simulation-related parameter according to the input deformation condition and heat treatment condition of the aluminum lithium alloy includes:

Calculating a dislocation density coefficient, a grain boundary mobility M, a grain boundary migration driving force P, a recrystallization critical dislocation density ρ _c, a recrystallization critical storage energy E _c and a maximum storage energy E _max according to the deformation parameter and the heat treatment parameter, wherein the dislocation density coefficient comprises a work hardening coefficient k ₁ and a dynamic recovery coefficient k ₂, and the work hardening coefficient k ₁ and the dynamic recovery coefficient k ₂ are obtained by the following formula:

3. The aluminum alloy microstructure simulation method based on the cellular automaton method according to claim 2, wherein the grain boundary mobility M is calculated by the following formula:

Wherein M is grain boundary mobility, b is a berg vector, δ is grain boundary thickness, Q _gb is grain boundary self-diffusion activation energy, K is Boltzmann constant, T is deformation temperature, R is air constant, 8.314, unit J/mol×k;

Wherein M is grain boundary mobility, M _p is a two-phase particle pinning factor, b is a berg vector, δ is grain boundary thickness, Q _gb is grain boundary self-diffusion activation energy, K is Boltzmann constant, T is deformation temperature, R is air constant, equal to 8.314J/mol×k.

4. The aluminum alloy microstructure simulation method based on the cellular automaton method according to claim 3, wherein the grain boundary migration driving force P is calculated by the following formula:

o＝o_s+o_G＝τΔρ-γk_GB

wherein o is a grain boundary migration driving force; p _s is dislocation energy accumulated near the grain boundary, and p _G is dislocation energy constituting the grain boundary; τ is the shear stress; Δρ is the amount of change in the dislocation density during recrystallization; gamma is the grain boundary energy and k _GB is the grain boundary curvature.

5. The cellular automaton-based aluminum alloy microstructure simulation method of claim 4, wherein the recrystallization critical dislocation density ρ _c is obtained by the following formula:

The recrystallization critical energy storage E _c is obtained by the following formula:

The maximum storage energy E _max is obtained by the following formula:

E_max＝τ×ρ_max

6. The cellular automaton-based aluminum alloy microstructure simulation method according to claim 5, wherein the aluminum alloy microstructure CA simulation-related parameters further include a continuous recrystallized area fraction f _CDRX and a discontinuous recrystallized area fraction f _DDRX, the continuous recrystallized area fraction f _CDRX and the discontinuous recrystallized area fraction f _DDRX being obtained by the following formulas:

f_CDRX＝S_CDRX/S

f_DDRX＝S_DDRX/S

7. The aluminum alloy microstructure simulation method based on a cellular automaton method according to claim 1, wherein the initial microstructure generating module simulates the aluminum alloy microstructure by using the cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure, comprising:

If the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical stored energy E _CDRX, then further determining if stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX;

8. The aluminum alloy microstructure simulation method based on a cellular automaton method according to claim 7, wherein the initial microstructure generating module simulates the aluminum alloy microstructure by using the cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure, further comprising:

9. The aluminum alloy microstructure simulation method based on a cellular automaton method according to claim 8, wherein the aluminum lithium alloy recrystallization microstructure simulation module realizes evolution simulation and prediction of an aluminum alloy recrystallization microstructure to be measured according to the aluminum alloy microstructure CA simulation related parameters, the aluminum alloy microstructure and the established aluminum lithium alloy recrystallization CA model, and the step of displaying the evolution result in real time comprises:

10. The aluminum alloy microstructure simulation system based on the cellular automaton method is applied to the aluminum alloy microstructure simulation method based on the cellular automaton method according to any one of claims 1 to 9, and is characterized by comprising an initial microstructure module, a dynamic recovery module, a dynamic recrystallization module and a static recrystallization module, wherein the initial microstructure module is used for inputting deformation parameters and heat treatment parameters of an aluminum alloy to be measured and calculating CA simulation related parameters of the aluminum alloy microstructure;

The dynamic recovery module is used for if the storage energy of the Cell (i, j) is larger than the critical storage energy generated by the sub-crystal, and the orientation angles ori of the four neighbors of the Cell are the same; then the Cell (i, j) is identified as a new subcell Cell, labeled newsubflag; if the current sub-crystal size delta _sub is smaller than the sub-crystal steady-state size delta _ss, calculating a sub-crystal growth driving force p and a cell displacement increment through the energy storage difference between cells; if the spacing drxdistance between the subgrystals is greater than one Cell size and a new subgrystal marker newsubflag equal to 1 is present in four neighbor cells of the Cell (i, j), the State variable State (i, j), grain boundary variable GB (i, j), dislocation variable D (i, j), and energy variable E (i, j) of the Cell (i, j) are updated according to the subgrystal growth rule, and marked with newsubflag =1;

The dynamic recrystallization module is used for judging whether dynamic recrystallization occurs according to cell storage energy; when the stored energy E (i, j) of the Cell (i, j) is larger than the critical stored energy E _CDRX of continuous dynamic recrystallization and the recrystallization probability is larger than a random number, continuous dynamic recrystallization occurs at the Cell; if the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical stored energy E _CDRX, then further determining if stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX; if the stored energy E (i, j) is greater than the discontinuous recrystallization critical stored energy E _DDRX, and the Cell (i, j) is at the grain boundary with a recrystallization probability greater than a random number, discontinuous dynamic recrystallization nucleation and growth occurs at the Cell (i, j); if the stored energy E (i, j) at the Cell (i, j) is less than the discontinuous dynamic recrystallization threshold energy E _DDRX, then only dynamic replies occur at the Cell (i, j);

The static recrystallization module is used for judging whether static recrystallization occurs according to cell storage energy in the heat preservation stage; when the stored energy E (i, j) at the Cell (i, j) is greater than or equal to the continuous dynamic recrystallization critical stored energy E _CDRX, then static reversion and static recrystallization occur, the Cell is converted into a static recrystallized grain Cell; when the stored energy E (i, j) at the Cell (i, j) is less than the continuous dynamic recrystallization critical energy storage E _CDRX, then only a static reply occurs.