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

Abstract

The present invention discloses a method and system for simulating the microstructure of aluminum alloy based on the cellular automaton method. A material parameter calculation module calculates the parameters related to the CA simulation of the aluminum alloy microstructure according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy. An initial microstructure generation module simulates the aluminum alloy microstructure using the cellular automaton according to the calculated parameters related to the CA simulation of the aluminum alloy microstructure. The recrystallization microstructure simulation module of the aluminum-lithium alloy realizes the evolution simulation and prediction of the recrystallized microstructure of the aluminum alloy to be measured and displays the evolution results in real time according to the parameters related to the CA simulation of the aluminum alloy microstructure, the aluminum alloy microstructure, and the established recrystallization CA model of the aluminum-lithium alloy. When the present invention uses the cellular automaton to simulate the aluminum alloy microstructure, the continuous and discontinuous dynamic recrystallization mechanisms of the aluminum-lithium alloy under medium and high temperature deformation conditions are considered, and the simulation system can simulate the recrystallized grains formed by the growth of sub-grains by rotation.

Description

Technical Field

[0001] The present invention relates to the technical field of microstructure simulation, and in particular discloses a method and system for simulating the microstructure of aluminum alloy based on the cellular automata method. Background Art

[0002] Various microstructure simulation methods provide a fast, economical and effective way for the research on the recrystallization microstructure evolution behavior of materials. In recent years, the multi-scale calculation and simulation methods based on materials science have developed rapidly. From the atomic scale, mesoscopic scale to the macroscopic scale, many researchers have used methods such as first-principles, molecular dynamics, discrete dislocation dynamics, continuous dislocation dynamics, crystal plasticity finite element and macroscopic finite element simulation to conduct in-depth research from multiple angles on aspects such as material property prediction, material microstructure evolution and microstructure visualization.

[0003] Based on the comprehensive research results of existing microstructure simulations, currently, the Monte Carlo method, phase field method and cellular automata method are mainly used. Although many scholars at home and abroad have used the Monte Carlo method, phase field method and cellular automata method to simulate the microstructure evolution during the hot deformation process of various aluminum alloys under different process conditions, the cellular automata has a large calculation scale and a fast calculation speed, and can well reproduce the microstructure evolution behavior during the hot working process. Therefore, the cellular automata (abbreviated as CA) based on physical experiments provides an effective means for the research on the law of material microstructure evolution.

[0004] Currently, many researchers at home and abroad have explored the use of the cellular automata method to simulate the recrystallization behavior during the hot deformation or heat treatment process.

[0005] 1) Research Status Abroad

[0006] Many researchers abroad have studied the simulation of material microstructure. For example, Yazdipour N et al. used cellular automata (CA) to simulate the dynamic recrystallization (DRX) evolution during hot deformation; Sitko M et al. simulated the influence of time step and cell size on the CA model simulation process during dynamic recrystallization through CA. Based on the CA simulation theory, Yanfeng L et al. established a three-dimensional cellular automata model for grain growth from aspects such as grain orientation, grain size distribution, grain growth kinetics, and grain topology. Bararpour et al. used the cellular automata model to predict the dynamic recrystallized microstructure of Al-Mg alloys, combined the cellular automata model with a three-dimensional finite element model to obtain temperature and strain rate results, and predicted the microstructure of the coating. In addition to using CA to simulate the dynamic recrystallization process, CA can also be used to simulate the static recrystallization process. Mukhopadhyay P et al. established a static recrystallization CA model, which adopted an extensible sub-grid technique that could effectively track local changes during recrystallization. The model included grain boundary nucleation, transition zone nucleation, and particle-driven nucleation.

[0007] De Jaeger J et al. used a three-dimensional cellular automata method to couple a crystal finite element model with a recrystallization model and applied it to a three-dimensional compressed deformation polycrystalline aggregate, which comprehensively demonstrated the coupling effect between microscopic grain evolution and macroscopic deformation.

[0008] Foreign scholars have combined cellular automata with various algorithms to simulate the static recrystallized microstructure. For example, Madani Mitra used a newly improved probabilistic cellular automata (PCA) model to simulate static recrystallization (SRX) during non-isothermal annealing. Sitko Mateusz proposed a new grain nucleation adaptive algorithm that was insensitive to the CA space size and was applicable to the simple SRX grain nucleation process. Hashemi Sepideh used the cellular automata method to generate a simulation dataset under a machine learning framework and demonstrated the temporal evolution of the microstructure during the static recrystallization of face-centered cubic (FCC) polycrystalline materials. Amir Asgharzadeh established a kinetic model for static recrystallization (SRX) of fluid-formed steel pipes based on the cellular automata (CA) method.

[0009] 2) Domestic research status

[0010] Many domestic scholars have conducted extensive research on the simulation of the microstructure of aluminum alloys using cellular automata. For example, Wang Yongjian established a dynamic recrystallization model for single-phase materials and materials containing second-phase particles, and used the cellular automata method to simulate the influence of second-phase particles on dynamic recrystallization behavior. Shen Gang

[30] established a coupled model of deformation crystal plasticity finite element (CPFEM) and cellular automata (CA) for polyphase and polycrystalline materials, and simulated the influence of different annealing temperatures, heating rates, holding times, etc. on the evolution of the microstructure. In general, the application of cellular automata in the field of recrystallization simulation is gradually becoming mature. Zhang Jie et al. established a CA model for the evolution of the microstructure during the hot deformation of 7085 aluminum alloy, taking the initial microstructure and thermodynamic parameters as the input data of the CA model, and setting the dislocation density as the internal state variable. The feasibility and predictability of the CA model were verified through experimental data. Liu Lei et al. determined that the main softening mechanism during the hot deformation of 2219 aluminum alloy is continuous dynamic recrystallization (CDRX). A method for forming a microstructure with initial equiaxed grains and dispersed local sub-grains was proposed, and the CDRX during the hot deformation of 2219 aluminum alloy was simulated on the Matlab platform.

[0011] In addition, domestic scholars have also conducted a lot of research on the prediction of static recrystallization microstructure. For example, Tang Xiaole used the cellular automata method to simulate the evolution of static recrystallization microstructure and texture, and analyzed the characteristics of nucleation, grain growth, as well as the change trends of energy and grain boundaries. However, the dislocation density and sub-grain changes during the deformation process were not introduced into the cellular automata, making it difficult to achieve the coupled calculation of the hot deformation + heat treatment process and accurately predict the entire process of deformation recrystallization. Guo Yina took as-cast 42CrMo steel as the research object and used the CA method to simulate the grain growth process of static recrystallization. The simulated grain morphology was described and analyzed from multiple angles such as grain size, number of grain sides, and relative area distribution of grains, optimizing the grain topology deformation technology. However, this recrystallization model is based on the grain boundary bulging theory and is only applicable to the simple recrystallization mechanism of steel materials, not suitable for the complex recrystallization mechanism of aluminum alloy materials. Although many scholars have currently studied the simulation of the evolution of the recrystallization microstructure of materials using the cellular automata method from different angles. However, the exploration of the simulation of the evolution of the microstructure during the deformation process of aluminum alloy by the CA method is less and imperfect. The reasons are as follows. Firstly, most of the research work on aluminum alloys focuses on fields such as improving formability and optimizing mechanical properties. In addition, during the hot deformation process of aluminum-lithium alloy, dislocation movement promotes dynamic recovery, forming a polygonized structure and sub-grains; with the continuation of deformation, dynamic recrystallization occurs, forming recrystallized grains; static recovery and static recrystallization also occur during the heat treatment process, forming static recrystallized grains. Therefore, the microstructure evolution mechanism of aluminum-lithium alloy materials during the hot plastic deformation process is very complex.

[0012] Although many scholars at home and abroad have used the cellular automaton method to simulate the microstructure evolution of aluminum alloys during hot deformation under different process conditions, previous simulations were all based on the traditional grain boundary bulging theory, ignoring the coupling effect between the recovery behavior and intragranular recrystallization behavior of aluminum alloys. Existing recrystallization simulation systems do not comprehensively consider the synergistic effect among dislocations, subgrains, dynamically recrystallized grains and statically recrystallized grains, resulting in the inability to accurately reveal the recrystallization evolution law of aluminum-lithium alloys during the hot deformation process by using the traditional recrystallization simulation method, and unable to truly reflect the actual microstructure, which affects the authenticity and accuracy of the simulation results, thus restricting the in-depth development of the research work on the recrystallization microstructure simulation of aluminum-lithium alloys. In addition, there is currently a lack of a systematic grain structure evolution simulation platform for simulating the recrystallization microstructure evolution of aluminum-lithium alloys, making it difficult to adapt to the research and development of aluminum-lithium alloy materials and process experiments. Summary of the Invention

[0013] The present invention provides a method and system for simulating the microstructure of aluminum alloys based on the cellular automaton method, aiming to solve at least one of the above technical problems.

[0014] One aspect of the present invention relates to a method for simulating the microstructure of aluminum alloys based on the cellular automaton method, including the following steps:

[0015] The material parameter calculation module calculates the relevant parameters for the CA simulation of the aluminum alloy microstructure according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy.

[0016] The initial microstructure generation module simulates the aluminum alloy microstructure by using the cellular automaton according to the calculated relevant parameters for the CA simulation of the aluminum alloy microstructure.

[0017] The recrystallization microstructure simulation module of the aluminum-lithium alloy realizes the evolution simulation and prediction of the recrystallized microstructure of the aluminum alloy to be measured, and displays the evolution results in real time according to the relevant parameters for the CA simulation of the aluminum alloy microstructure, the aluminum alloy microstructure, and the established recrystallization CA model of the aluminum-lithium alloy.

[0018] Furthermore, the deformation conditions include deformation parameters, the heat treatment conditions include heat treatment parameters, and the relevant parameters for the CA simulation of the aluminum alloy microstructure include dislocation density coefficient, grain boundary mobility M, grain boundary migration driving force P, critical dislocation density ρ for recrystallization c , critical stored energy E for recrystallization c and maximum stored energy E max . The steps for the material parameter calculation module to calculate the relevant parameters for the CA simulation of the aluminum alloy microstructure according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy include:

[0019] Calculate the dislocation density coefficient, grain boundary mobility M, grain boundary migration driving force P, and critical dislocation density ρ for recrystallization according to the deformation parameters and heat treatment parameters. c, the recrystallization critical storage energy E c and the maximum storage energy E max , the dislocation density coefficient includes the work hardening coefficient k 1 and the dynamic recovery coefficient k 2 , the work hardening coefficient k 1 and the dynamic recovery coefficient k 2 are obtained by the following formulas:

[0020]

[0021] where ρ def is the dislocation density during the deformation process, 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 when the strain is 0; σ sat is the saturation stress; σ 0 is the initial stress; α is a material constant equal to 0.5; μ is the shear modulus; b is the Burgers vector.

[0022] Furthermore, the grain boundary mobility M is calculated by the following formula:

[0023] If the pinning of the grain boundary by the second-phase particles is not considered, then the grain boundary mobility M can be expressed by the following formula:

[0024]

[0025] where M is the grain boundary mobility, b is the Burgers vector, δ is the grain boundary thickness, Q gb is the grain boundary self-diffusion activation energy, k is the Boltzmann constant, T is the temperature, R is the universal gas constant, which is 8.314 J / mol*K;

[0026] If the pinning of the second-phase particles is considered, then the grain boundary mobility M can be expressed by the following formula:

[0027]

[0028] where M is the grain boundary mobility, M p is the second-phase particle pinning factor, b is the Burgers vector, δ is the grain boundary thickness, Q gb is the grain boundary self-diffusion activation energy, k is the Boltzmann constant, T is the temperature, R is the universal gas constant, which is 8.314 J / mol*K.

[0029] Furthermore, the grain boundary migration driving force P is calculated by the following formula:

[0030] p = p s + p G = τΔρ - γk GB

[0031] Among them, p is the driving force for grain boundary migration; p s is the dislocation energy accumulated near the grain boundary, and p G is the dislocation energy that constitutes the grain boundary; τ is the shear stress; Δρ is the change in dislocation density during the recrystallization process; γ is the grain boundary energy, and k GB is the grain boundary curvature.

[0032] Furthermore, the critical dislocation density ρ for recrystallization c is obtained through the following formula:

[0033]

[0034] Among them, ρ c is the critical dislocation density for recrystallization, and E c is the critical stored energy for recrystallization; τ is the shear stress;

[0035] The critical stored energy E for recrystallization c is obtained through the following formula:

[0036]

[0037] Among them, E c is the critical stored energy for recrystallization, ε c is the critical strain for recrystallization; a and b are constants; γ LAG is the small-angle grain boundary energy;

[0038] The maximum stored energy E max is obtained through the following formula:

[0039] E max = τ × ρ max

[0040] Among them, E max is the maximum stored energy, τ is the shear stress, and ρ max is the maximum dislocation density.

[0041] Furthermore, the parameters related to the CA simulation of the microstructure of aluminum alloy also include the continuous recrystallization area fraction f CDRX and the discontinuous recrystallization area fraction f DDRX , and the continuous recrystallization area fraction f CDRX and the discontinuous recrystallization area fraction f DDRX are obtained through the following formula:

[0042] f CDRX = S CDRX / S

[0043] f DDRX = S DDRX / S

[0044] Among them, f CDRX is the continuous recrystallization area fraction, and f DDRX is the discontinuous recrystallization area fraction. S CDRX is the total area of continuous dynamic recrystallization grains, and S is the total area of dynamic recrystallization.

[0045] Furthermore, the steps of simulating the aluminum alloy microstructure by using the cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure by the initial microstructure generation module include:

[0046] Judging whether dynamic recrystallization occurs according to the stored energy of the cell; when the stored energy E(i, j) of the cell Cell(i, j) is greater than the critical stored energy E CDRX of continuous dynamic recrystallization and the recrystallization probability is greater than a random number, continuous dynamic recrystallization occurs at this cell;

[0047] If the stored energy E(i, j) at this cell Cell(i, j) is less than the critical stored energy E CDRX of continuous dynamic recrystallization, then further judge whether the stored energy E(i, j) is greater than the critical stored energy E DDR X of discontinuous recrystallization;

[0048] If the stored energy E(i, j) is greater than the critical stored energy E DDRX of discontinuous recrystallization, and this cell Cell(i, j) is at the grain boundary and its recrystallization probability is greater than a random number, then discontinuous dynamic recrystallization nucleation and growth occur at this cell Cell(i, j); if the stored energy E(i, j) at this cell Cell(i, j) is less than the critical stored energy E DDRX of discontinuous dynamic recrystallization, then only dynamic recovery occurs at this cell Cell(i, j).

[0049] Furthermore, the steps of simulating the aluminum alloy microstructure by using the cellular automaton according to the calculated CA simulation related parameters of the aluminum alloy microstructure by the initial microstructure generation module further include:

[0050] During the holding stage, judging whether static recrystallization occurs according to the stored energy of the cell; when the stored energy E(i, j) at the cell Cell(i, j) is greater than or equal to the critical stored energy E CDRX of continuous dynamic recrystallization, then static recovery and static recrystallization occur, and the cell transforms into a static recrystallized grain cell;

[0051] When the stored energy E(i, j) at the cell Cell(i, j) is less than the critical stored energy E CDRX of continuous dynamic recrystallization, then only static recovery occurs.

[0052] Further, the recrystallization microstructure simulation module of the aluminum-lithium alloy realizes the simulation and prediction of the evolution of the recrystallized microstructure of the aluminum alloy to be measured according to the relevant parameters of the CA simulation of the aluminum alloy microstructure, the aluminum alloy microstructure, and the established recrystallization CA model of the aluminum-lithium alloy, and the steps of real-time displaying the evolution results include:

[0053] Output the average size of DRX grains, the misorientation angle of DRX grains, the DRX volume fraction, the misorientation angle of SRX grains, and the SRX volume fraction of the recrystallized microstructure of the aluminum alloy to be measured.

[0054] Another aspect of the present invention relates to an aluminum alloy microstructure simulation system based on the cellular automaton method, which is applied to the above-mentioned aluminum alloy microstructure simulation method based on the cellular automaton method. The aluminum alloy microstructure simulation system based on the cellular automaton method includes an initial microstructure module, a dynamic recovery module, a dynamic recrystallization module, and a static recrystallization module. Among them,

[0055] The initial microstructure module is used to input the deformation parameters and heat treatment parameters of the aluminum alloy to be measured, and calculate the relevant parameters of the CA simulation of the aluminum alloy microstructure;

[0056] The dynamic recovery module is used to, if the stored energy of the cell Cell(i, j) is greater than the critical stored energy for the generation of subgrains, and the orientation angles ori of the four neighbors of the cell are the same; then the cell Cell(i, j) is used as a new subgrain cell and marked with newsubflag; if the current subgrain size δ sub is smaller than the steady-state subgrain size δ ss , then calculate the driving force p for subgrain growth and the cell displacement increment through the stored energy difference between cells; if the distance drxdistance between subgrains is greater than one cell size and there is a new subgrain marker newsubflag equal to 1 among the four neighbor cells of the cell Cell(i, j), then update the state variable State(i, j), the grain boundary variable GB(i, j), the dislocation variable D(i, j), and the energy variable E(i, j) of the cell Cell(i, j) according to the subgrain growth rule, and mark with newsubflag = 1;

[0057] The dynamic recrystallization module is used to judge whether dynamic recrystallization occurs according to the stored energy of the cell; when the stored energy E(i, j) of the cell Cell(i, j) is greater than the critical stored energy E CDRX for continuous dynamic recrystallization and the recrystallization probability is greater than a random number, then continuous dynamic recrystallization occurs at this cell; if the stored energy E(i, j) at this cell Cell(i, j) is less than the critical stored energy E CDRX for continuous dynamic recrystallization, then further judge whether the stored energy E(i, j) is greater than the critical stored energy E DDRX; if the stored energy E(i, j) is greater than the critical stored energy E for discontinuous recrystallization DDRX , and the cell Cell(i, j) is at the grain boundary, and its recrystallization probability is greater than a random number, then discontinuous dynamic recrystallization nucleation and growth occur at the cell Cell(i, j); if the stored energy E(i, j) at the cell Cell(i, j) is less than the critical stored energy E for discontinuous dynamic recrystallization DDRX , then only dynamic recovery occurs at the cell Cell(i, j);

[0058] A static recrystallization module, used in the holding stage to determine whether static recrystallization occurs according to the stored energy of the cell; when the stored energy E(i, j) at the cell Cell(i, j) is greater than or equal to the critical stored energy E for continuous dynamic recrystallization CDRX , then static recovery and static recrystallization occur, and the cell transforms into a static recrystallized grain cell; when the stored energy E(i, j) at the cell Cell(i, j) is less than the critical stored energy E for continuous dynamic recrystallization CDRX , then only static recovery occurs.

[0059] The beneficial effects achieved by the present invention are as follows:

[0060] The present invention provides a method and system for simulating the microstructure of aluminum alloy based on the cellular automaton method. The material parameter calculation module calculates the relevant parameters for CA simulation of the aluminum alloy microstructure according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy; the initial microstructure generation module simulates the aluminum alloy microstructure using the cellular automaton according to the calculated relevant parameters for CA simulation of the aluminum alloy microstructure; the recrystallized microstructure simulation module of the aluminum-lithium alloy realizes the simulation and prediction of the evolution of the recrystallized microstructure of the aluminum alloy to be measured according to the relevant parameters for CA simulation of the aluminum alloy microstructure, the aluminum alloy microstructure, and the established recrystallization CA model of the aluminum-lithium alloy, and displays the evolution results in real time. When the method and system for simulating the microstructure of aluminum alloy based on the cellular automaton method provided by the present invention use the cellular automaton to simulate the aluminum alloy microstructure, the continuous and discontinuous dynamic recrystallization mechanisms of the aluminum-lithium alloy under medium and high temperature deformation conditions are considered. This simulation system can simulate the recrystallized grains formed by the growth of sub-grains by rotation; this CA simulation system for aluminum alloy microstructure can simulate the situation where multiple dynamic recrystallization mechanisms occur simultaneously during the hot deformation process of the aluminum-lithium alloy, and can predict the comprehensive kinetic model of the aluminum-lithium alloy based on the CDRX and DDRX mechanisms; the influence of sub-grain boundary migration and recrystallized grain boundary migration on the dislocation density during the hot deformation process of the aluminum alloy material is considered during CA simulation, and thus an accurate dislocation density model is established, making the evolution process of the recrystallization behavior closer to the actual tissue evolution process; a full-process microstructure evolution CA method including hot deformation - dynamic recovery - dynamic recrystallization - static recrystallization is proposed, and a software for simulating and predicting the microstructure evolution suitable for the whole process of hot deformation of the aluminum-lithium alloy is developed. Description of the Drawings

[0061] Figure 1 It is a schematic flow chart of the aluminum alloy microstructure simulation method based on the cellular automaton method provided by the present invention. Detailed Implementation Modes

[0062] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation modes.

[0063] As Figure 1 shown, the first embodiment of the present invention proposes an aluminum alloy microstructure simulation method based on the cellular automaton method, including the following steps:

[0064] Step S100: The material parameter calculation module calculates the relevant parameters for the CA simulation of the aluminum alloy microstructure according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy.

[0065] Material parameter calculation module: According to the input deformation conditions and heat treatment conditions, calculate parameters such as the maximum dislocation density, time variable, simulation step size, critical strain, critical dislocation, critical stored energy, continuous dynamic recrystallization grain fraction and discontinuous dynamic recrystallization grain fraction, steady-state sub-grains and grain size.

[0066] Step S200: The initial microstructure generation module simulates the aluminum alloy microstructure using the cellular automaton according to the calculated relevant parameters for the CA simulation of the aluminum alloy microstructure.

[0067] Initial microstructure generation module: Simulate the initial microstructure using CA according to parameters such as the cellular space, initial grain size, initial sub-grain size and initial grain orientation difference angle.

[0068] Step S300: The aluminum-lithium alloy recrystallized microstructure simulation module realizes the evolution simulation and prediction of the recrystallized microstructure of the aluminum alloy to be tested, and displays the evolution results in real time according to the relevant parameters for the CA simulation of the aluminum alloy microstructure, the aluminum alloy microstructure, and the established aluminum-lithium alloy recrystallization CA model.

[0069] Aluminum-lithium alloy recrystallized microstructure simulation: On the basis of the above two modules, according to the recrystallization CA simulation process, realize the evolution simulation and prediction of the recrystallized microstructure of 2195 aluminum alloy, and display the evolution results in real time.

[0070] This embodiment elaborates on the implementation method of the CA simulation of the dynamic recovery, dynamic and static recrystallization grain evolution process.

[0071] (1) Calculation of k

[0072] (1) Calculate k 1 、k2

[0073] During the dynamic recovery process, it consists of the dislocation density accumulated by work hardening and the dislocation density consumed by dynamic recovery. First, as can be seen from Equation (1), the flow stress σ is proportional to μbρ 1 / 2 , and the dislocation density σ at a specific time is calculated accordingly.

[0074] σ = αμbρ 1 / 2 (1)

[0075] In Equation (1), σ represents the flow stress, α represents the material constant, which is equal to 0.5, μ represents the shear modulus, b represents the Burgers vector, and ρ represents the dislocation density.

[0076] Dynamic recovery not only reduces the work hardening effect but also changes the dislocation structure. Therefore, the change in the dislocation density ρ during the deformation process def can be expressed by Equation (2).

[0077]

[0078] In Equation (2), ρ def represents the dislocation density during the deformation process, ε represents the strain, and k 1 is the work hardening coefficient related to the statistically stored dislocations of thermal accumulation; k 2 is the thermal activation coefficient of dynamic recovery.

[0079] Integrating and solving Equation (2) gives the expression of the dislocation density during the dynamic recovery process, as shown in Equation (3).

[0080]

[0081] In Equation (3), k 1 is the work hardening coefficient related to the statistically stored dislocations of thermal accumulation; k 2 is the thermal activation coefficient of dynamic recovery; ρ 0 is the initial dislocation density corresponding to when the strain is 0; ρ def represents the dislocation density during the deformation process, and ε represents the strain.

[0082]

[0083] In Equation (4), ρ def is the dislocation density during the deformation process, 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 when the strain is 0; σ sat is the saturation stress; σ 0is the initial stress; α represents the material constant, equal to 0.5; μ is the shear modulus; b is the Burgers vector. σ 0 is the initial stress, which can be calculated by σ 0 = αμbρ 0 / 2; σ sat is the saturation stress; σ def is the same as the true stress before the occurrence of dynamic recrystallization; k 2 can be obtained by linearly fitting with -2ln[(σ sat - σ def ) / (σ sat - σ 0 )] and ε.

[0084]

[0085] In formula (5), σ sat is the saturation stress, α represents the material constant, equal to 0.5, μ represents the shear modulus, b represents the Burgers vector, k 1 is the work-hardening coefficient related to the statistically stored dislocations of thermal accumulation; k 2 is the thermal activation coefficient of dynamic recovery.

[0086] To calculate the work-hardening coefficient k 1 and the dynamic recovery coefficient k 2 , the key is to determine the saturation stress σ sat before the start of recrystallization according to the stress-strain curve. The critical recrystallization stress σ c and the saturation stress σ sat can be obtained from the curve of work-hardening rate θ and stress σ. Accordingly, the work-hardening coefficient k 1 and the dynamic recovery coefficient k 2 are obtained by the linear fitting method.

[0087] (2) Calculate K soft

[0088] Introduce the dynamic recrystallization softening factor K soft into the dislocation density model (Equation 2), and the dislocation density ρ drx in the discontinuous dynamic recrystallization process can be expressed as:

[0089]

[0090] In formula (6), ρ drx represents the dislocation density in the discontinuous dynamic recrystallization process, ε represents the strain, k 1 is the work-hardening coefficient related to the statistically stored dislocations of thermal accumulation; k 2 is the thermal activation coefficient of dynamic recovery; K soft represents the dynamic recrystallization softening factor.

[0091] Softening factor K soft The value range of is 0 < K soft < k 1 , and this value can reflect the dislocation density consumed by the migration of large-angle grain boundaries.

[0092] Steady-state stress σ ss is the stress value when work hardening, dynamic recovery, and dynamic recrystallization reach dynamic equilibrium. Therefore, the softening factor K soft can be obtained through σ ss = αμb(k 1 - K soft ) / k 2 as shown in formula (7).

[0093] K soft = 11.5×10 8 - 3.79×10 7 ×lnZ (7)

[0094] In formula (7), K soft represents the dynamic recrystallization softening factor, and Z represents the Zener-Hollomon parameter, that is, the Zener-Solomon parameter, which can be specifically obtained through the following formula.

[0095]

[0096] Among them, is the strain rate, Q is the deformation activation energy, T is the deformation temperature, R is the air constant, which is 8.314, and the unit is J / mol*K.

[0097] (3) Calculate the maximum dislocation density ρ max

[0098]

[0099] In formula (9), ρ max represents the maximum dislocation density, k 1 is the work hardening coefficient related to the statistically stored dislocations of thermal accumulation; k 2 is the dynamic recovery thermal activation coefficient, and K soft represents the dynamic recrystallization softening factor.

[0100] (4) Calculate the maximum stored energy E max

[0101] E max = τ×ρ max (10)

[0102] In formula (10), E max represents the maximum stored energy, τ represents the shear stress, and ρ maxRepresents the maximum dislocation density.

[0103] The dislocation density inside the new grains is set to the initial dislocation density ρ initial , which is close to 0.

[0104] (5) Predict the stress during the deformation of the Al-Li alloy

[0105]

[0106] In formula (11), σ drx represents the stress during the deformation of the Al-Li alloy, σ ss represents the steady-state stress, α represents the material constant, equal to 0.5, μ represents the shear modulus, b represents the Burgers vector, ρ initial represents the initial dislocation density inside the new grains, k 2 represents the dynamic recovery thermal activation coefficient, ε represents the strain, ε crit is equal to ε c , which is the critical strain for recrystallization.

[0107] (6) Calculate the critical stored energy for recrystallization E c

[0108] When the stored energy of the grains reaches the critical stored energy for recrystallization, new crystal nuclei begin to form inside the material. The critical stored energy for recrystallization corresponding to different deformation conditions is different. When calculating the critical stored energy, linear fitting can be used to solve it through the relationship between the macroscopic strain and the microscopic stored energy.

[0109] E s = αGb 2 ρ s = τ × ρ s (12)

[0110]

[0111] In formulas (12) and (13), Es is the stored energy; Ec is the critical stored energy for recrystallization; C E , n E are material constants; Q E is the activation energy for stored energy; τ represents the shear stress, α represents the material constant, equal to 0.5, G represents the shear modulus, b represents the Burgers vector, ρ s represents the stored dislocation density.

[0112]

[0113] In formula (14), E c is the critical stored energy for recrystallization, γ LAG is the low-angle grain boundary energy; a and b are constants, ε c is the critical strain for recrystallization.

[0114] (7) Calculate the critical dislocation density ρ for recrystallization c

[0115]

[0116] In formula (15), ρ c represents the critical dislocation density for recrystallization, R c represents the critical stored energy for recrystallization, and τ represents the shear stress.

[0117] (8) Calculate the grain boundary mobility M

[0118] During the grain growth process, the increase in the average grain size corresponds to the decrease in the total grain boundary area and the decrease in the grain boundary energy. The grain boundary energy affects the grain growth. Generally, formula (16) is used to represent the grain growth rate.

[0119] v = MP ress (16)

[0120] In formula (16), v is the grain boundary migration velocity; P ress is the driving force for grain boundary migration per unit area, which can be derived from the energy storage difference between the recrystallized grains and the deformed matrix, and M is the high-angle grain boundary mobility.

[0121] 1) Grain boundary mobility M and the pinning factor M of the second-phase particles p

[0122] If the case of the second-phase particles pinning the grain boundaries is not considered, then the grain boundary mobility M can be expressed by formula (17).

[0123]

[0124] In formula (17), M is the grain boundary mobility, b is the Burgers vector, δ is the grain boundary thickness, k is the Boltzmann constant, T is the deformation temperature, R is the universal gas constant, which is 8.314, with the unit J / mol*K, and Q gb is the activation energy for grain boundary self-diffusion.

[0125] If the pinning of the second-phase particles is considered, then the grain boundary mobility is expressed by formula (18).

[0126]

[0127] In formula (18), M is the grain boundary mobility, T is the deformation temperature, R is the universal gas constant, which is 8.314, with the unit J / mol*K, and M p is the pinning factor of the second-phase particles, b is the Burgers vector, δ is the grain boundary thickness, δ = 1.4×10 - 10 m; Q gb(J / mol) is the activation energy of grain boundary self-diffusion, k (J / K) is the Boltzmann constant, k = 1.380649×10 -23 J / K. The initial dislocation density ρ 0 = 10 10 m -2 .

[0128] The two-phase particle pinning factor M p can be calculated by formula (19).

[0129]

[0130] In formula (19), ρ c is the dislocation density of the recrystallized part, σ c is the recrystallization critical stress, γ m is the high-angle grain boundary energy, is the strain rate, l is the dislocation line length, τ is the shear stress, is the initial strain rate, equal to 1.

[0131] 2) The driving force P for grain boundary migration

[0132] If the cell Cell(i, j) has not recrystallized, while its neighboring cell Cell(i + 1, j) has recrystallized, and there is an energy storage difference between these two cells, then the cell Cell(i, j) is affected by the stored energy and undergoes grain boundary migration; when both the cell Cell(i, j) and the cell Cell(i + 1, j) have recrystallized, and these two cells have different crystal orientation angles, the driving force for grain boundary migration at this time is the grain boundary energy.

[0133] p is the driving force acting on the unit area, and can be specifically expressed as:[[]]

[0134] p = p s + p G = τΔρ - γk GB (20)

[0135] In formula (20), p is the driving force acting on the unit area, p s is the stored energy, p G is the grain boundary energy, τ is the shear stress, and the driving force p for grain boundary migration mainly comes from the reduction of the stored energy and the grain boundary energy. The stored energy p S and the grain boundary energy p G are related to dislocations. The stored energy ps refers to the dislocation energy accumulated near the grain boundary; the grain boundary energy p G refers to the dislocation energy that constitutes the grain boundary; Δρ is the change in dislocation density during the recrystallization process; k GB is the grain boundary curvature, as shown in formula (21). γ is the grain boundary energy, and γ can be divided into the high-angle grain boundary energy γHAG and the small-angle grain boundary energy γ LAG , which is specifically expressed as follows:

[0136]

[0137] θ i = π / 2 × |Δq| / q max (22)

[0138] In formulas (21) and (22), θ i is the misorientation angle between the i-th grain and its adjacent grain; θ 0 is the initial grain misorientation angle, γ HAG is the high-angle grain boundary energy, γ LAG is the small-angle grain boundary energy; π is equal to 3.14159267; Δq is the misorientation of adjacent grains, q max is the maximum grain orientation angle, and the high-angle misorientation angle of θ 0 is equal to 15°, 0 ≤ |Δq| / q max < 1.

[0139] The change value of the grain misorientation angle can be expressed by formula (23):

[0140]

[0141] In formula (23), dθ grain is the change value of the grain misorientation angle, k 2 is the dynamic recovery coefficient, ρ i is the dislocation density of the i-th grain, ε is the strain, b is the modulus of the Burgers vector, b = 2.86×10 -10 ; n is the number of dislocation sets at the grain boundary; for example, set n = 5*106; α is the percentage of dislocation density consumed in the recovery during the formation of sub-grains, α = 0.5; D is the average grain size, with the unit of μm.

[0142] The high-angle grain boundary energy can be expressed as:

[0143]

[0144] In formula (24), γ HAG is the high-angle grain boundary energy, θ 0 is the initial grain misorientation angle, π is the pi, ν is the Poisson's ratio, equal to 0.35; μ is the shear modulus. b is the scalar of the Burgers vector, b = 2.86×10 -10 . When driven by the grain boundary energy, the driving force P can be divided into two cases: one is the driving force when the pinning of the second-phase particles is not considered, specifically as shown in formula (25).

[0145] P G= γ HAG k GB (25)

[0146] In formula (25), P G is the driving force without considering the pinning of two-phase particles, γ HAG is the high-angle grain boundary energy, and k GB is the grain boundary curvature.

[0147] Second, the driving force considering particle pinning is as shown in formula (26).

[0148]

[0149] In formula (26), P G is the driving force considering particle pinning, γ LAG is the low-angle grain boundary energy; f is the volume fraction of spherical particles; r p is the particle radius. γ HAG is the high-angle grain boundary energy, and k GB is the grain boundary curvature. The volume fraction f of two-phase particles in formula (26) is the volume fraction occupied by all two-phase particles in the matrix. The two-phase particles located inside the grains have no pinning effect on the migration of grain boundaries. Therefore, during the grain growth process after recrystallization, the volume fraction of two-phase particles located at the grain boundaries is used to calculate the pinning force.

[0150] The grain boundary curvature k GB can be specifically expressed as:

[0151]

[0152] In formula (27), k GB is the grain boundary curvature, A is a fitting parameter; c s is the cell size; Kink is the cell belonging to grain i in the neighboring cells when the interface is assumed to be a flat interface, i.e., when k = 0, and Kink is equal to 14; N i is the cell belonging to grain i in the neighboring cells, and N + 1 is the number of all cells belonging to the long-range Moore neighbors (N + 1:N is the number of the 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 boundary is convex, k GB is greater than 0; when the grain boundary is concave, k GB is less than 0.

[0153] (9) Time step Δt

[0154] During the CA simulation process, the time step is often used. Therefore, it is necessary to convert the strain into the time step in the CA simulation. First, take the shortest time required to grow one cell as the time step of the model, that is, the cell size cs The ratio with the maximum grain boundary migration rate v max is as follows: The expression of the time step Δt is as follows:

[0155]

[0156] In formula (28), Δt is the time step, c s is the cell size; v max is the maximum grain boundary migration 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, K soft is the recrystallization softening coefficient.

[0157] (10) The initial sub - grain number n sub

[0158] According to the experiment, the average size of the initial sub - grains is δ, and the ratio of the sub - grain area is f sub .

[0159] Calculate the initial sub - grain number according to the initial sub - grain size and the sub - grain boundary area fraction.

[0160]

[0161] In formulas (29) and (30), n sub is the sub - grain number, π is the pi, δ is the sub - grain diameter, S sub is the sub - grain area, f sub is the ratio of the sub - grain area, S total is the area of the entire simulation region, n x is the number of transverse grids in the simulation region, n y is the number of longitudinal grids in the simulation region, L 0 is the initial grid length.

[0162] Calculate and obtain n sub equal to 687 according to formulas (29) and (30). Similarly, the number of initial grains in a certain simulation region can also be obtained according to the initial grain size. Among them, n x and n y are the grid numbers of the simulation region.

[0163] (11) The continuous recrystallization area fraction f CDRX , the discontinuous recrystallization area fraction f DDRX

[0164] f CDRX = S CDRX / S (31)

[0165] f DDRX = S DDRX / S (32)

[0166] In formulas (31) and (32), f CDRX is the continuous recrystallization area fraction, and f DDRX is the discontinuous recrystallization area fraction. It is assumed that the microstructure is composed of continuously dynamically recrystallized grains and discontinuously dynamically recrystallized grains. S is the total area of dynamic recrystallization in the EBSD map; S CDRX is the total area of continuously dynamically recrystallized grains, which can be obtained by counting the dynamically recrystallized grains within the deformed grains; S DDRX is the total area of discontinuously dynamically recrystallized grains, which can be obtained by counting the area of dynamically recrystallized grains on the deformed grain boundaries.

[0167] (12) Calculate the critical stored energy E CDRX for continuous dynamic recrystallization, E DDRX for discontinuous recrystallization. E is the stored energy at the current simulation step i.

[0168] (13) Glossary: The English expression for continuous dynamic recrystallization is Continue Dynamic Recrystallization, abbreviated as CDRX; the English expression for discontinuous dynamic recrystallization is Discontinue Dynamic Recrystallization, abbreviated as DDRX; the English expression for dynamic recrystallization is Dynamic Recrystallization, abbreviated as DRX; ρ def is the dislocation density of the deformed grains; ρ drx is the dislocation density of the dynamically recrystallized grains; the English expression for dynamic recovery is Dynamic Recovery, abbreviated as DRV; the English expression for static recrystallization is Static Recrystallization, abbreviated as SRX; the English expression for static recovery is Static Recovery, abbreviated as SRV. The English expression for cellular automata is Cellular automata, abbreviated as CA.

[0169] Furthermore, the method for simulating the microstructure of aluminum alloy based on the cellular automata method provided in this embodiment, step S200 includes:

[0170] Step S210, determine whether dynamic recrystallization occurs according to the stored energy of the cell; when the stored energy E(i, j) of the cell Cell(i, j) is greater than the critical stored energy E CDRX for continuous dynamic recrystallization and the recrystallization probability is greater than a random number, then continuous dynamic recrystallization occurs at this cell.

[0171] When the stored energy E(i, j) of the cell Cell(i, j) is greater than the critical stored energy E for continuous dynamic recrystallization CDRX and the recrystallization probability is greater than the random number, continuous dynamic recrystallization occurs at this cell. This means that the orientation angle of the cell Cell(i, j) changes continuously.

[0172] Step S220: If the stored energy E(i, j) at the cell Cell(i, j) is less than the critical stored energy E for continuous dynamic recrystallization CDRX then further determine whether the stored energy E(i, j) is greater than the critical stored energy E for discontinuous recrystallization DDRX .

[0173] If the stored energy at the cell Cell(i, j) is less than the critical stored energy E for continuous dynamic recrystallization CDRX , then determine whether this stored energy is greater than the critical stored energy E for discontinuous recrystallization DDRX .

[0174] Step S230: If the stored energy E(i, j) is greater than the critical stored energy E for discontinuous recrystallization DDRX , and the cell Cell(i, j) is at the grain boundary and its recrystallization probability is greater than the random number, then discontinuous dynamic recrystallization nucleation and growth occur at the cell Cell(i, j); if the stored energy E(i, j) at the cell Cell(i, j) is less than the critical stored energy E for discontinuous dynamic recrystallization DDRX , then only dynamic recovery occurs at the cell Cell(i, j).

[0175] If so, and the cell Cell(i,j) is at the grain boundary and its recrystallization probability is greater than the random number, then discontinuous dynamic recrystallization nucleation and growth occur at the cell Cell(i, j). If the stored energy at the cell is less than the critical stored energy E for discontinuous dynamic recrystallization DDRX , then only dynamic recovery occurs at this cell.

[0176] Preferably, for the aluminum alloy microstructure simulation method based on the cellular automaton method provided in this embodiment, step S200 further includes:

[0177] Step S240: During the holding stage, determine whether static recrystallization occurs according to the stored energy of the cell; when the stored energy E(i, j) at the cell Cell(i, j) is greater than or equal to the critical stored energy E for continuous dynamic recrystallization CDRX , then static recovery and static recrystallization occur, and the cell transforms into a static recrystallized grain cell.

[0178] When the stored energy of the cell is greater than or equal to the critical recrystallization stored energy, static recovery and static recrystallization occur, and the cell transforms into a static recrystallized grain cell.

[0179] Step S250: When the stored energy E(i, j) at the cell Cell(i, j) is less than the critical stored energy E for continuous dynamic recrystallization CDRX then only static recovery occurs.

[0180] When the stored energy is less than the critical stored energy for recrystallization, only static recovery occurs. Therefore, by comparing the stored energy of the cell with the critical stored energy for recrystallization, it is possible to determine whether recrystallized grains will form in the deformed microstructure and the heat-treated microstructure.

[0181] Furthermore, the method for simulating the microstructure of aluminum alloy based on the cellular automaton method provided in this embodiment, step S300 includes:

[0182] Output the average size of DRX grains, the misorientation angle of DRX grains, the DRX volume fraction, the misorientation angle of SRX grains, and the SRX volume fraction of the recrystallized microstructure of the aluminum alloy to be measured.

[0183] Output results: the average size of DRX grains, the misorientation angle of DRX grains, the DRX volume fraction, the size of SRX grains, the misorientation angle of SRX grains, and the SRX volume fraction.

[0184] Another aspect of the present invention relates to a system for simulating the microstructure of aluminum alloy based on the cellular automaton method, which is applied to the method for simulating the microstructure of aluminum alloy based on the cellular automaton method described above. The system for simulating the microstructure of aluminum alloy based on the cellular automaton method includes an initial microstructure module, a dynamic recovery module, a dynamic recrystallization module, and a static recrystallization module. Among them, the initial microstructure module is used to input the deformation parameters and heat treatment parameters of the aluminum alloy to be measured and calculate the relevant parameters for the CA simulation of the aluminum alloy microstructure; the dynamic recovery module is used to, if the stored energy of the cell Cell(i, j) is greater than the critical stored energy for the generation of subgrains and the orientation angles ori of the four neighbors of this cell are the same; then mark this cell Cell(i, j) as a new subgrain cell with newsubflag; if the current subgrain size δ sub is less than the steady-state subgrain size δ ss, the driving force p for subgrain growth and the incremental cell displacement are calculated based on the energy storage difference between cells; if the distance drx distance between subgrains is greater than one cell size and there is a new subgrain marker newsubflag equal to 1 among the four neighboring cells of cell Cell(i, j), then according to the subgrain growth rule, the state variable State(i, j), grain boundary variable GB(i, j), dislocation variable D(i, j), and energy variable E(i, j) of cell Cell(i, j) are updated and marked with newsubflag = 1; the dynamic recrystallization module is used to determine whether dynamic recrystallization occurs based on the cell stored energy; when the stored energy E(i, j) of cell Cell(i, j) is greater than the critical stored energy E for continuous dynamic recrystallization CDRX and the recrystallization probability is greater than a random number, then continuous dynamic recrystallization occurs at this cell; if the stored energy E(i, j) at this cell Cell(i, j) is less than the critical stored energy E for continuous dynamic recrystallization CDRX , then it is further determined whether the stored energy E(i, j) is greater than the critical stored energy E for discontinuous recrystallization DDRX ; if the stored energy E(i, j) is greater than the critical stored energy E for discontinuous recrystallization DDRX , and this cell Cell(i, j) is at the grain boundary and its recrystallization probability is greater than a random number, then discontinuous dynamic recrystallization nucleation and growth occur at this cell Cell(i, j); if the stored energy E(i, j) at this cell Cell(i, j) is less than the critical stored energy E for discontinuous dynamic recrystallization DDRX , then only dynamic recovery occurs at this cell Cell(i, j); the static recrystallization module is used to determine whether static recrystallization occurs during the holding stage based on the cell stored energy; when the stored energy E(i, j) at cell Cell(i, j) is greater than or equal to the critical stored energy E for continuous dynamic recrystallization CDRX , then static recovery and static recrystallization occur, and the cell transforms into a static recrystallized grain cell; when the stored energy E(i, j) at cell Cell(i, j) is less than the critical stored energy E for continuous dynamic recrystallization CDRX , then only static recovery occurs.

[0185] The initial microstructure module generates the initial simulation microstructure; then, the deformation parameters and heat treatment parameters of the aluminum-lithium alloy (deformation temperature T, strain rate, strain ε, holding T srx temperature and holding time t) are input to calculate the dislocation density coefficients k1, k2, k soft , grain boundary mobility M, driving force P, f CDRX , f DDRX , critical dislocation ρ c , critical stored energy E c and maximum stored energy E maxParameters such as etc.; Determine the number of CA simulation cycles Nstep according to the strain increment Δε, and start the simulation.

[0186] Obtain the matrix dislocation density and recrystallized dislocation density during the hot deformation of aluminum alloy according to the dislocation density evolution model of the recovery and recrystallization process. At the same time, calculate the stored energy E of the unit cell.

[0187] The dynamic recovery module is used for if the stored energy of the unit cell Cell(i, j) is greater than the critical stored energy generated by the sub-grain, and the orientation angles ori of the four neighbors of this unit cell are the same, which means this unit cell is inside the grain. Then take this unit cell Cell(i, j) as a new sub-grain unit cell, mark it with newsubflag, and its dislocation density is 1×10 -10 2) If the current sub-grain size δ sub is less than the steady-state sub-grain size δ ss , then calculate the driving force p for sub-grain growth and the displacement increment of the unit cell through the difference in stored energy between unit cells. 3) If the distance drxdistance between sub-grains is greater than the size of a unit cell and there is a new sub-grain marker newsubflag equal to 1 among the four neighbor unit cells of the unit cell Cell(i, j). This indicates that there are new sub-grains around the four neighbors of the unit cell Cell(i, j). Then, according to the sub-grain growth rule, update 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 unit cell Cell(i, j), and mark it with newsubflag = 1.

[0188] The dynamic recrystallization module is used to judge whether dynamic recrystallization occurs according to the stored energy of the unit cell.

[0189] 1) When the stored energy E(i, j) of the unit cell Cell(i, j) is greater than the critical stored energy E CDRX for continuous dynamic recrystallization, and the recrystallization probability is greater than a random number, then continuous dynamic recrystallization occurs at this unit cell. This means that the orientation angle of this unit cell Cell(i, j) changes continuously. 2) If the stored energy at this unit cell Cell(i, j) is less than the critical stored energy E CDRX for continuous dynamic recrystallization, then judge whether this stored energy is greater than the critical stored energy E DDRX for discontinuous recrystallization. If so, and this unit cell Cell(i, j) is at the grain boundary and its recrystallization probability is greater than a random number, then discontinuous dynamic recrystallization nucleation and growth occur at this unit cell Cell(i, j). 3) If the stored energy at this unit cell is less than the critical stored energy E DDRX for discontinuous dynamic recrystallization, then only dynamic recovery occurs at this unit cell.

[0190] During the holding stage, the static recrystallization module is used to cause static recovery and static recrystallization when the cell stored energy is greater than or equal to the critical stored energy for recrystallization, and the cell transforms into a static recrystallized grain cell. 2) When the stored energy is less than the critical stored energy for recrystallization, only static recovery occurs.

[0191] Therefore, by comparing the stored energy of the cell with the critical stored energy for recrystallization, it is possible to determine whether recrystallized grains will form in the deformed microstructure and the heat-treated microstructure.

[0192] The method and system for simulating the microstructure of aluminum alloy based on the cellular automaton method provided in this embodiment, compared with the prior art, calculate the relevant parameters for the CA simulation of the aluminum alloy microstructure through the material parameter calculation module according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy; the initial microstructure generation module uses the cellular automaton to simulate the aluminum alloy microstructure according to the calculated relevant parameters for the CA simulation of the aluminum alloy microstructure; the recrystallized microstructure simulation module of the aluminum-lithium alloy realizes the evolution simulation and prediction of the recrystallized microstructure of the aluminum alloy to be tested and displays the evolution result in real time according to the relevant parameters for the CA simulation of the aluminum alloy microstructure, the aluminum alloy microstructure, and the established recrystallization CA model of the aluminum-lithium alloy. When using the cellular automaton to simulate the aluminum alloy microstructure in the method and system for simulating the microstructure of aluminum alloy based on the cellular automaton method provided in this embodiment, the continuous and discontinuous dynamic recrystallization mechanisms of the aluminum-lithium alloy under medium and high temperature deformation conditions are considered. This simulation system can simulate the recrystallized grains formed by the growth of sub-grains by rotation; this CA simulation system for the aluminum alloy microstructure can simulate the situation where multiple dynamic recrystallization mechanisms occur simultaneously during the hot deformation process of the aluminum-lithium alloy, and can predict the comprehensive kinetic model of the aluminum-lithium alloy based on the CDRX and DDRX mechanisms; the influence of sub-grain boundary migration and recrystallized grain boundary migration on the dislocation density during the hot deformation process of the aluminum alloy material is considered during the CA simulation, and thus an accurate dislocation density model is established, making the evolution process of the recrystallization behavior closer to the actual tissue evolution process; a full-process microstructure evolution CA method including hot deformation - dynamic recovery - dynamic recrystallization - static recrystallization is proposed, and a software for simulating and predicting the microstructure evolution suitable for the entire hot deformation process of the aluminum-lithium alloy is developed.

[0193] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention. Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for simulating aluminum alloy microstructure based on cellular automata, characterized in that: The following steps are involved: The material parameter calculation module calculates the relevant parameters of the aluminum alloy microstructure CA simulation according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy; The initial structure generation module uses a cellular automaton to simulate the microstructure of the aluminum alloy according to the calculated CA simulation related parameters of the aluminum alloy microstructure; The aluminum-lithium alloy recrystallization structure simulation module realizes the evolution simulation and prediction of the recrystallization microstructure of the aluminum alloy 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; Critical dislocation density for recrystallization ρ c Obtained by the following formula: Among them, ρ c is the critical dislocation density for recrystallization, E c is the critical storage energy of recrystallization; τ is the shear stress; Recrystallization critical storage energy E c Obtained by the following formula: Among them, E c is the critical storage energy of recrystallization, ε c is the critical strain for recrystallization; a and b are constants; γ LAG is the small-angle grain boundary energy; Maximum storage capacity E max Obtained by the following formula: AND max =τ×ρ max Among them, E max is the maximum storage energy, τ is the shear stress, ρ max is the maximum dislocation density; The aluminum alloy microstructure CA simulation related parameters also include the continuous recrystallization area fraction f CDRX and the discontinuous recrystallization area fraction f DDRX , the continuous recrystallization area fraction f CDRX and the discontinuous recrystallization area fraction f DDRX Obtained by the following formula: f CDRX =S CDRX / S f DDRX =S DDRX / S Among them, f CDRX is the continuous recrystallization area fraction, f DDRX is the discontinuous recrystallization area fraction, S CDRX is the total area of ​​continuously dynamically recrystallized grains, and S is the total area of ​​dynamically recrystallized grains.

2. The aluminum alloy microstructure simulation method based on cellular automaton method according to claim 1, characterized in that: The deformation conditions include deformation parameters, the heat treatment conditions include heat treatment parameters, and the aluminum alloy microstructure CA simulation related parameters include dislocation density coefficient, grain boundary mobility M, grain boundary migration driving force P, recrystallization critical dislocation density ρ c , recrystallization critical storage energy E c and maximum storage energy E max The material parameter calculation module calculates the relevant parameters of the aluminum alloy microstructure CA simulation according to the input deformation conditions and heat treatment conditions of the aluminum-lithium alloy, including the following steps: According to the deformation parameters and the heat treatment parameters, the dislocation density coefficient, the grain boundary mobility M, the grain boundary migration driving force P, the recrystallization critical dislocation density ρ are calculated. c , recrystallization critical storage energy E c and maximum storage energy E max , the dislocation density coefficient includes a work hardening coefficient k1 and a dynamic recovery coefficient k2, and the work hardening coefficient k1 and the dynamic recovery coefficient k2 are obtained by the following formula: Among them, ρ def is the dislocation density during deformation, k1 is the work hardening coefficient, k2 is the work hardening coefficient, ε is the strain, ρ0 is the initial dislocation density corresponding to the strain of 0; σ sat is the saturation stress; σ0 is the initial stress; α is the material constant, equal to 0.5; μ is the shear modulus; b is the Burger vector.

3. The aluminum alloy microstructure simulation method based on cellular automaton method according to claim 2, characterized in that: The grain boundary mobility M is calculated by the following formula: If the pinning of the grain boundary by the two-phase particles is not considered, the grain boundary mobility M can be expressed by the following formula: Where M is the grain boundary mobility, b is the Burger vector, δ is the grain boundary thickness, and Q gb is the activation energy of grain boundary self-diffusion, k is the Boltzmann constant, T is the deformation temperature, R is the air constant, which is 8.314, unit J / mol*K; If the pinning of two-phase particles is considered, the grain boundary mobility M can be expressed by the following formula: Where M is the grain boundary mobility, M p is the pinning factor of the two-phase particles, b is the Burger vector, δ is the grain boundary thickness, Q gb is the activation energy of grain boundary self-diffusion, k is the Boltzmann constant, T is the deformation temperature, and R is the air constant, which is equal to 8.314 J / mol*K.

4. The aluminum alloy microstructure simulation method based on cellular automaton method according to claim 3, characterized in that: The grain boundary migration driving force P is calculated by the following formula: p=p s +p G =τΔρ-γk GB Where p is the driving force for grain boundary migration; p s is the dislocation energy accumulated near the grain boundary, p G is the dislocation energy constituting the grain boundary; τ is the shear stress; Δρ is the change in dislocation density during recrystallization; γ is the grain boundary energy, k GB is the grain boundary curvature.

5. The aluminum alloy microstructure simulation method based on cellular automaton method according to claim 1, characterized in that: The initial structure generation module uses a cellular automaton to simulate the microstructure of the aluminum alloy according to the calculated CA simulation related parameters of the aluminum alloy microstructure, including: Whether dynamic recrystallization occurs is determined based on the cell storage energy; when the storage energy E(i, j) of the cell Cell(i, j) is greater than the critical storage energy E(i, j) of continuous dynamic recrystallization, CDRX When the recrystallization probability is greater than the random number, continuous dynamic recrystallization occurs at the cell; If the storage energy E(i, j) at the cell Cell(i, j) is less than the critical storage energy E of continuous dynamic recrystallization CDRX When , it is further determined whether the storage energy E(i, j) is greater than the discontinuous recrystallization critical storage energy E DDRX ; If the storage energy E(i, j) is greater than the discontinuous recrystallization critical storage energy E DDRX , and the recrystallization probability of the cell Cell(i, j) at the grain boundary is greater than the random number, then discontinuous dynamic recrystallization nucleation and growth occurs at the cell Cell(i, j); if the storage energy E(i, j) at the cell Cell(i, j) is less than the critical storage energy E of discontinuous dynamic recrystallization DDRX When , only dynamic recovery occurs at the cell Cell(i, j).

6. The aluminum alloy microstructure simulation method based on cellular automaton method according to claim 5, wherein 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, and the step further comprises: During the heat preservation stage, whether static recrystallization occurs is determined based on the cell storage energy; When the storage energy E(i, j) at the cell Cell(i, j) is greater than or equal to the critical storage energy E of continuous dynamic recrystallization CDRX When , static recovery and static recrystallization occur, and the cell is transformed into a static recrystallization grain cell; When the storage energy E(i, j) at the cell Cell(i, j) is less than the critical storage energy E of continuous dynamic recrystallization CDRX , only static response occurs.

7. The aluminum alloy microstructure simulation method based on cellular automaton method according to claim 6, characterized in that: The aluminum-lithium alloy recrystallization structure simulation module realizes the evolution simulation and prediction of the recrystallization microstructure of the aluminum alloy 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 the steps of displaying the evolution result in real time include: Output the DRX average grain size, DRX grain misorientation angle, DRX volume fraction, SRX grain misorientation angle and SRX volume fraction of the recrystallized microstructure of the aluminum alloy to be tested.

8. A cellular automaton-based aluminum alloy microstructure simulation system, applied to the cellular automaton-based aluminum alloy microstructure simulation method as claimed in any one of claims 1 to 7, characterized in that: The aluminum alloy microstructure simulation system based on the cellular automaton method includes an initial structure module, a dynamic recovery module, a dynamic recrystallization module and a static recrystallization module, wherein the initial structure module is used to input deformation parameters and heat treatment parameters of the aluminum alloy to be tested, and calculate the aluminum alloy microstructure CA simulation related parameters; The dynamic recovery module is used to take the cell Cell(i, j) as a new subcrystal cell and mark it with newsubflag if the storage energy of the cell Cell(i, j) is greater than the critical storage energy generated by the subcrystal and the orientation angles ori of the four neighbors of the cell are the same; if the current subcrystal size δ sub Smaller than the subgrain steady-state size δ ss , the driving force p for subgrain growth and the cell displacement increment are calculated by the energy storage difference between cells; if the distance drxdistance between subgrains is greater than a cell size and there is a new subgrain marker newsubflag equal to 1 in the four neighboring cells of the cell Cell(i, j), then according to the subgrain growth rule, 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 Cell(i, j) are updated and marked with newsubflag=1; The dynamic recrystallization module is used to determine whether dynamic recrystallization occurs according to the cell storage energy; when the storage energy E(i, j) of the cell Cell(i, j) is greater than the critical storage energy E of continuous dynamic recrystallization CDRX When the recrystallization probability is greater than the random number, continuous dynamic recrystallization occurs at the cell; if the storage energy E(i, j) at the cell Cell(i, j) is less than the critical storage energy E of continuous dynamic recrystallization CDRX When , it is further determined whether the storage energy E(i, j) is greater than the discontinuous recrystallization critical storage energy E DDRX ; If the storage energy E(i, j) is greater than the discontinuous recrystallization critical storage energy E DDRX , and the recrystallization probability of the cell Cell(i, j) at the grain boundary is greater than the random number, then discontinuous dynamic recrystallization nucleation and growth occurs at the cell Cell(i, j); if the storage energy E(i, j) at the cell Cell(i, j) is less than the critical storage energy E of discontinuous dynamic recrystallization DDRX When , only dynamic recovery occurs at the cell Cell(i, j); The static recrystallization module is used to determine whether static recrystallization occurs according to the cell storage energy during the insulation stage; when the storage energy E(i, j) at the cell Cell(i, j) is greater than or equal to the critical storage energy E of continuous dynamic recrystallization CDRX When the storage energy E(i, j) at the cell Cell(i, j) is less than the critical storage energy E(i, j) of continuous dynamic recrystallization, static recovery and static recrystallization occur, and the cell is transformed into a static recrystallization grain cell. CDRX , only static response occurs.

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