Multiphase metal material phase change recrystallization coupled structure evolution method and system

Abstract

The invention discloses a structure evolution method and system for phase change recrystallization coupling of a multiphase metal material, the orientation during martensite generation is calculated through martensite crystallography, martensite growth is carried out, and then the martensite phase change result of the material in a multiphase state is predicted through a metallographic structure determination method. The method is established by utilizing a geometrical relationship between different crystal structures of alpha and beta titanium during martensite phase transformation of the titanium alloy, so that the method can be suitable for predicting martensite phase transformation and dynamic recrystallization coupling of the two-phase titanium alloy. The method can be used for predicting the proportion of martensite phases generated in the actual production process of titanium alloys of different marks, further predicts the surface performance, can remarkably reduce the cost and improve the simulation and analysis efficiency in the actual industrial production, and has important significance on computer-aided analysis and design in the part machining deformation process.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat treatment of multiphase metal materials, and in particular relates to a method and system for microstructure evolution oriented to phase transformation and recrystallization coupling of multiphase metal materials. Background Art

[0002] Under the combined effects of extreme external loading conditions (such as high strain rates, high temperatures, and rapid cooling), the processed surface of a material often undergoes complex microstructural evolution due to dynamic phase transformation effects. Murr, Staudhammer, and Hecker (1982) used systematic cyclic loading experiments to reveal the synergistic effect of strain and strain rate in 304 stainless steel: the formation of martensitic laths arises from the nucleation of densely distributed α' phase embryos at the intersections of shear bands, rather than the traditionally assumed mechanism of single embryo extension. They proposed that due to the nanoscale spacing at the shear band intersections (typically smaller than the critical nucleus size), adjacent embryos undergo topological interlocking and coalescence under stress, ultimately forming a lath structure with continuous orientation characteristics. Notably, when the initial embryos fail to fully occupy the intersection volume, the residual γ phase and α' phase form a coherent interface at the submicron scale, resulting in the light-dark contrast of the γ / α' complex observed in transmission electron microscopy. This discovery provides a new perspective on the dynamics of shear band-mediated solid-state phase transformations. At the same time, Ahmed and Rack (1998) quantified the critical cooling rate threshold (>410°C / s) of TC4 titanium alloy through laser rapid solidification experiments, confirming that ultrafast cooling rates can completely suppress the β→α phase transformation path, prompting the metastable β phase to directly transform into a single-phase structure of orthorhombic martensite (α''). This phenomenon is highly consistent with the classical theory of martensitic crystallography (PTMC) pioneered by Bain (1924). Based on the principle of minimum strain energy, Bain first mathematically derived the lattice distortion path of the austenite to martensite transformation by constructing the fcc→bcc crystallographic correspondence. His proposed Bain distortion model remains the fundamental framework for understanding diffusionless phase transformations.

[0003] However, some current visualization models of microstructural evolution mainly focus on the recrystallization process or twinning behavior prediction of single-phase materials. Although they achieve dynamic characterization of grain boundary migration and dislocation evolution in single-phase systems, they generally ignore the coupling mechanism between phase transformation and grain evolution in multiphase materials. When simulating two-phase or multiphase materials, these models fail to fully integrate key factors such as element interdiffusion between heterogeneous phases, interfacial energy anisotropy, and phase transformation-induced strain field reconstruction. This leads to significant limitations in predicting the phase interface migration path, the preferred orientation of the nucleation of the new phase, and the phase transformation-recrystallization co-evolution process in multiphase systems. Therefore, it is necessary to consider the elements and crystal structure, incorporate the influence of effects such as strain rate and temperature in the modeling process, and combine the different phase compositions of the material to develop a visualization model for the martensitic phase transformation of titanium alloy materials. Summary of the Invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a method and system for the organizational evolution of phase transformation and recrystallization coupling of multiphase metal materials, so as to fill the gap in the visualization prediction of phase transformation and recrystallization coupling of multiphase metal materials.

[0005] In order to achieve the above objectives, in a first aspect, the present invention provides a method for microstructure evolution of a multiphase metal material coupled with phase transformation and recrystallization, which specifically comprises the following steps: Taking the structural information and basic physical parameters of the target multiphase metal material as input parameters, the crystal structure evolution method based on martensitic crystallography is used to obtain the orientation change of the target material when it undergoes martensitic phase transformation under set conditions; The structural information and basic physical parameters of the target multiphase metal material and the physical orientation of the target material are input into the cellular automaton to calculate the dynamic recrystallization process. The martensitic phase transformation results and the dynamic recrystallization evolution results are merged and output to obtain a visualized phase transformation and recrystallization result.

[0006] Furthermore, the structural information of the target multi-phase metal material includes the phase components of the target multi-phase metal material.

[0007] Furthermore, the basic physical parameters include the crystal structure and physical parameters of the target multiphase metal material in each pure phase state; the EBSD data of the material is obtained through the crystal phase characterization software, including the initial grain distribution information of the material and the physical orientation information of each grain. The physical orientation information is the Euler angle of the three axes.

[0008] Furthermore, when obtaining the orientation change of the target material when undergoing martensitic phase transformation under set conditions, the possible geometric relationship when the phase transformation occurs is determined by analyzing the hexagonal closest packing and face-centered cubic crystal structures.

[0009] Furthermore, the change in crystal orientation during phase transition satisfies the Burgers relation:

[0010] in, and Indicates two different phases.

[0011] Furthermore, when performing visual modeling, the physical orientation is defined in the form of Euler angles in the cellular automaton. The crystal structure information and physical parameters in the microstructure characterization results are input into the cellular automaton model to obtain the microstructure after phase transition, and the output result is also Euler angles.

[0012] Furthermore, the basic physical parameters are all reflected in the properties of the cellular automaton, including physical orientation, the grain and the phase.

[0013] In a second aspect, the present invention provides a multiphase metal material phase transformation and recrystallization coupled structure evolution system, comprising an orientation calculation module and a visualization module; The orientation calculation module uses the structural information and basic physical parameters of the target multiphase metal material as input parameters, and obtains the orientation change of the target material when it undergoes martensitic phase transformation under set conditions through a crystal structure evolution method based on martensitic crystallography; The visualization module is used to input the structural information and basic physical parameters of the target multiphase metal material and the physical orientation of the target material into the cellular automaton to calculate the dynamic recrystallization process, merge and output the martensitic phase transformation results with the dynamic recrystallization evolution results, and obtain visualized phase transformation and recrystallization results.

[0014] In a third aspect, the present invention may also provide a computer device comprising a processor and a memory. The memory is configured to store a computer executable program, and the processor reads and executes the computer executable program from the memory. When the processor executes the computer executable program, the method and system for microstructure evolution coupled with phase transformation and recrystallization of multiphase metal materials described in the present invention can be implemented.

[0015] At the same time, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method and system for structural evolution of phase transformation and recrystallization coupling of multiphase metal materials described in the present invention can be realized.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: the present invention provides a method and system for the coupled microstructural evolution of phase transformation and recrystallization of multiphase metal materials. With a strong theoretical basis, it can predict the results of the martensitic phase transformation of multiphase metal materials based on the initial microstructure, crystal structure and physical information. It can be used as a prediction of the final surface microstructure of dual-phase titanium alloy materials under different processes, and can provide a basis for material design and subsequent predictions.

[0017] The method described in this paper, based on the theoretical foundations of solid-state phase transformation thermodynamics, crystal plasticity theory, and dislocation dynamics, achieves a coordinated analysis of the martensitic phase transformation process and dynamic recrystallization behavior by constructing a quantitative correlation model between initial microstructural characteristics (including grain orientation distribution, interfacial energy, and dislocation density), crystal structural parameters (such as the Burgers vector and slip system activation energy), and constitutive physical parameters (phase transformation driving force and nucleation barrier). This method accurately predicts the characteristics of phase transformation products in multiphase systems under the influence of a thermomechanical coupling field, including martensitic variant selection patterns, microstructural topology, and phase distribution gradients. It is particularly suitable for simulating the surface microstructure evolution of dual-phase titanium alloys under process conditions such as thermomechanical processing (rolling / forging) and surface laser shock peening, where the β→α' phase transformation competes with dynamic recrystallization. This method provides a theoretical calculation framework for establishing a mapping between process parameters, microstructure, and mechanical properties, supporting the reverse design of material properties and process window optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of an implementable method of the present invention.

[0019] Figure 2 To extract and reconstruct the obtained microscopic tissue image.

[0020] Figure 3 Schematic diagram of martensitic phase transformation. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] refer to Figure 1 The present invention provides a method for the coupled phase transformation and recrystallization microstructure evolution of a multiphase metal material, which specifically includes the following steps: Inputting structural information and basic physical parameters of the target multiphase metal material into the cellular automaton; Calculate whether the material undergoes martensitic transformation under the input boundary conditions based on the input parameters; If the target multiphase metal material undergoes a martensitic phase transformation, the initial physical orientation of each grain is first assigned based on the input parameters. The number of martensite nucleations in each different grain is calculated based on the grain size, and new martensite is generated at random positions on the grain boundary. The growth direction of the newly generated martensite is calculated based on the Burgers relationship between the physical orientations before and after the martensitic transformation. The newly generated martensite grows inside the grain along the calculated growth direction. It is determined whether the calculated number of martensite nucleations has been reached. If not, the process returns to the martensite nucleation step and continues the cycle. If the number of nucleations has been reached, the martensite phase transformation step ends and the phase transformation result is recorded. If the target multiphase metal material does not undergo martensitic transformation, the nucleation rate is calculated based on the input parameters, and further calculation is made on whether the cell at that position undergoes recrystallization; if recrystallization does not occur, a direct judgment is made; if recrystallization occurs, the grain boundary energy and driving force of the grains around the nucleated grains are calculated based on relevant parameters; the driving force between other cells in its neighborhood and the nucleated cell is calculated in the area surrounding the new nucleated cell, and the new nucleated cell is urged to grow in the direction with the largest driving force; the grain misorientation angle and grain growth rate are calculated to determine the speed of grain growth; the dislocation density of the new grain is calculated and the statistical information of the grain is updated; it is determined whether the recrystallization process is over. If not, the process returns to the recrystallization nucleation step and the cycle continues until the end condition is met; The martensitic phase transformation results and the dynamic recrystallization evolution results are combined and output to obtain visualized phase transformation and recrystallization results.

[0023] The present invention constructs crystallographic constraints on martensitic phase transformation based on the Burgers orientation relationship to ensure the consistency of the phase transformation direction with crystallographic principles. The energy gradient driving mechanism of the recrystallization process is realized through dynamic calculation of grain boundary energy and driving force. The dislocation density evolution equation and grain orientation difference dynamics are integrated to achieve cross-scale correlation between microstructure evolution and macroscopic mechanical response. The conditional judgment branch structure is used to realize the autonomous selection of phase transformation / recrystallization path. The cyclic iterative mechanism supports the dynamic tracking of microstructure evolution.

[0024] The coupled model constructed within the framework of cellular automata transcends the limitations of traditional isolated analysis of phase transitions or recrystallization, revealing the synergistic effects of the two types of microstructural evolution mechanisms through real-time interactive calculations. Visual simulation results spatially map the evolution of crystallographic orientations with macroscopic phase deformation morphologies, forming a cross-scale correlation from atomic-scale structural evolution to mesoscopic microstructural morphology. This method provides a dynamically traceable digital experimental platform for microstructural control of multiphase metallic materials. Its physical parameter-driven nature gives the simulation predictions engineering value, enabling direct guidance on compositional design and process optimization.

[0025] The present invention provides a method for the coupled phase transformation and recrystallization microstructure evolution of a multiphase metal material, comprising the following steps: S1), obtaining material structure information and basic physical parameters of the target multiphase metal material; The EBSD data of the material is obtained through crystal phase characterization software, including information on the initial grain distribution of the material and the physical orientation of each grain. For TC21 titanium alloy, the phase transformation involves only two types of crystal structures, namely α-Ti (hcp) and β-Ti (bcc). The hcp structure uses the l direction as the crystal axis of the grain, while the bcc structure randomly selects a line passing through the body center and perpendicular to the side as the crystal axis. Based on these two crystal axes, the Euler angle of each grain's crystal axis relative to the cellular space can be obtained.

[0026] S2) Calculate whether the martensitic phase transformation occurs by temperature. If it occurs, enter the martensitic phase transformation module. If it does not occur, determine whether the dynamic recrystallization process occurs.

[0027] S3) If martensitic transformation occurs, first calculate the number of nuclei of martensitic transformation in a certain phase transformation grain based on the material structure information and basic physical parameters, then calculate the possible physical orientation of the target material after martensitic transformation based on martensitic crystallography (PTMC), refer to Figure 3 ; According to the theory of martensitic crystallography, when a martensitic phase transformation occurs in a two-phase metal material, its crystal orientation changes satisfy the Burgers relationship: (1) Due to crystal symmetry, a single parent β grain has six faces that can serve as The basal plane of the phase has two stacking modes, so each β grain can be converted into 2*6=12 types Grain, that is, there are 12 variations, such as Figure 2 As shown, in the grains of high temperature parent phase β phase, New face The ground of the phase, so the orientation relationship between the parent phase and the new phase can be obtained.

[0028] According to the Euler angle data of the new grain obtained in step 1), the normal vectors v of the six faces of the initial unit cell can be obtained in the original space coordinate system. The matrix R around the three coordinate axes x, y, and z is calculated by the Euler angle of the mother grain. x , R y , R z , multiplying the three together can give the spatial rotation matrix Then, through v1=R·v, we can get the vector of the new grain orientation in the spatial coordinate system after the phase change. The row vectors in v1 are the direction vectors of the six surface normal vectors of the grain after rotation. Then, the value of the vector is used to solve the Euler angle of the new grain in space, that is, the physical orientation.

[0029] S4) Based on the physical orientation of the new grain obtained in S3), the grain will grow in this direction or in the opposite direction, with the same probability. After calculating the thickness of the new grain, the new grain will extend along this direction to the grain boundary position and complete its own growth. Thereafter, S3) to S4) will be cycled until the calculated number of martensite nucleations is grown. Growth vector constraints are established based on the crystallographic orientation to ensure that the direction of grain expansion strictly corresponds to the intrinsic crystal structure of the material. A bidirectional equiprobable growth strategy is adopted to maintain orientation correlation while being compatible with the principle of crystallographic symmetry. The grain boundary contact mechanism automatically switches the growth stage and establishes an adaptive boundary for microstructural evolution. The target-oriented cyclic control of the nucleation number ensures that the phase transformation process converges to the preset microstructural characteristics.

[0030] S5) If martensitic transformation does not occur, whether the grain has nucleated is calculated based on multiple physical parameters such as grain dislocation density and grain boundary activation energy.

[0031] S6) If nucleation occurs, the current cell is transformed into a new crystal nucleus. At the same time, the other cells in the four neighborhoods around the cell are selected, and the driving forces between them and the central cell are calculated respectively. The cell with the largest driving force is selected as the growth direction of the new grain.

[0032] S7), calculating the grain orientation difference angle and grain growth rate between the current cell and the surrounding cells, and calculating the dislocation density updated by the current cell after nucleation.

[0033] Steps S5) through S7) are repeated throughout the entire cellular space until the dislocation density in the space is reduced to a point where dynamic recrystallization cannot occur. The recrystallization module terminates. The martensitic transformation results are then combined with the dynamic recrystallization evolution results and output to produce a visual representation of the phase transformation and recrystallization results.

[0034] The present invention establishes a dynamic correlation between energy dissipation and recrystallization phase transformation based on the multi-parameter nucleation criterion of dislocation density and grain boundary activation energy. The neighborhood driving force optimization algorithm realizes the strict correspondence between the grain boundary migration direction and the local energy gradient. The grain orientation difference angle calculation constructs the interface migration dynamics model under crystallographic constraints. The cellular state conversion rule realizes the automatic topological reconstruction of the crystal nucleus initiation and the parent phase matrix. The neighborhood optimization growth strategy reproduces the continuous medium growth characteristics while maintaining the discrete cellular framework. The dislocation density dynamic update algorithm synchronously reflects the redistribution effect of the nucleation process on micro defects. The grain growth rate calculation integrates the macro driving force and micro orientation difference parameters. The closed-loop data flow of dislocation density-grain boundary energy-driving force ensures the physical consistency of parameters at different scales. The iterative cycle of nucleation-growth-update realizes the coordinated advancement of organizational evolution and performance evolution.

[0035] In Example 2, the present invention provides a multiphase metal material phase transformation and recrystallization coupled microstructure evolution system, comprising an orientation calculation module and a visualization module; The orientation calculation module uses the structural information and basic physical parameters of the target multiphase metal material as input parameters, and obtains the orientation change of the target material when it undergoes martensitic phase transformation under set conditions through a crystal structure evolution method based on martensitic crystallography; The visualization module is used to input the structural information and basic physical parameters of the target multiphase metal material and the physical orientation of the target material into the cellular automaton to calculate the dynamic recrystallization process, merge and output the martensitic phase transformation results with the dynamic recrystallization evolution results, and obtain visualized phase transformation and recrystallization results.

[0036] The orientation calculation module also includes a basic physical parameter acquisition unit, which obtains the crystal structure and physical parameters of the target multiphase metal material in each pure phase state; the EBSD data of the material is obtained through the crystal phase characterization software, including the initial grain distribution information of the material and the physical orientation information of each grain. The physical orientation information is the Euler angle of the three axes.

[0037] When obtaining the orientation change of the target material when undergoing martensitic phase transformation under set conditions, the orientation calculation module determines the possible geometric relationship when the phase transformation occurs by analyzing the hexagonal closest packing and face-centered cubic crystal structures.

[0038] When performing visual modeling, the visualization module defines the physical orientation in the form of Euler angles in the cellular automaton. The crystal structure information and physical parameters in the microstructure characterization results are input into the cellular automaton model to obtain the microstructure after phase transition. The output result is also Euler angles.

[0039] The present invention also provides a computer device comprising a processor and a memory. The memory is configured to store a computer-executable program, and the processor reads and executes the computer-executable program from the memory. When the processor executes the computer-executable program, the method for microstructure evolution coupled with phase transformation and recrystallization of multiphase metal materials described in the present invention can be implemented.

[0040] On the other hand, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the organizational evolution method for phase transformation and recrystallization coupling of multiphase metal materials described in the present invention.

[0041] The computer device may be a laptop computer, a desktop computer or a workstation.

[0042] The processor of the present invention may be a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC) or a readily available field programmable gate array (FPGA).

[0043] The memory of the present invention may be an internal storage unit of a laptop computer, desktop computer or workstation, such as a memory or a hard disk; or an external storage unit, such as a mobile hard disk or a flash memory card.

[0044] Computer-readable storage media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSD) or optical disks, etc. Among them, random access memory may include resistance random access memory (ReRAM) and dynamic random access memory (DRAM).

Claims

1. A method for the coupled microstructure evolution of multiphase metal materials by phase transformation and recrystallization, characterized in that: The following steps are involved: Taking the structural information and basic physical parameters of the target multiphase metal material as input parameters, the crystal structure evolution method based on martensitic crystallography is used to obtain the orientation change of the target material when it undergoes martensitic phase transformation under set conditions; The structural information and basic physical parameters of the target multiphase metal material and the physical orientation of the target material are input into the cellular automaton to calculate the dynamic recrystallization process. The martensitic phase transformation results and the dynamic recrystallization evolution results are merged and output to obtain a visualized phase transformation and recrystallization result.

2. The method for coupled phase transformation and recrystallization microstructure evolution of a multiphase metal material according to claim 1, characterized in that: The structural information in the target multi-phase metal material includes the phase composition of the target multi-phase metal material.

3. The method for coupled phase transformation and recrystallization microstructure evolution of a multiphase metal material according to claim 1, characterized in that: The basic physical parameters include the crystal structure and physical parameters of the target multiphase metal material in each pure phase state; the EBSD data of the material is obtained through the crystal phase characterization software, including the initial grain distribution information of the material and the physical orientation information of each grain. The physical orientation information is the Euler angle of the three axes.

4. The method for coupled phase transformation and recrystallization microstructure evolution of a multiphase metal material according to claim 1, characterized in that: When obtaining the orientation change of the target material when undergoing martensitic phase transformation under set conditions, the possible geometric relationship when the phase transformation occurs is determined by analyzing the hexagonal closest packing and face-centered cubic crystal structures.

5. The method and system for coupled phase transformation and recrystallization structural evolution of multiphase metal materials according to claim 4, characterized in that: The change in crystal orientation during phase transition satisfies the Burgers relationship: in, and Indicates two different phases.

6. The method for microstructure evolution of multiphase metal materials coupled with phase transformation and recrystallization according to claim 4, characterized in that: When performing visual modeling, the physical orientation is defined in the form of Euler angles in the cellular automaton. The crystal structure information and physical parameters in the microstructure characterization results are input into the cellular automaton model to obtain the microstructure after phase transition, and the output result is also Euler angles.

7. The method for coupled phase transformation and recrystallization microstructure evolution of multiphase metal materials according to claim 1, characterized in that: The basic physical parameters are all reflected in the properties of the cellular automaton, including physical orientation, grains and phases.

8. A multiphase metal material phase transformation and recrystallization coupled microstructure evolution system, characterized in that: Including orientation calculation module and visualization module; The orientation calculation module uses the structural information and basic physical parameters of the target multiphase metal material as input parameters, and obtains the orientation change of the target material when it undergoes martensitic phase transformation under set conditions through a crystal structure evolution method based on martensitic crystallography; The visualization module is used to input the structural information and basic physical parameters of the target multiphase metal material and the physical orientation of the target material into the cellular automaton to calculate the dynamic recrystallization process, merge and output the martensitic phase transformation results with the dynamic recrystallization evolution results, and obtain visualized phase transformation and recrystallization results.

9. A computer device, characterized in that: It includes a processor and a memory, the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and when the processor executes the computer executable program, it can implement the organizational evolution method for phase transformation and recrystallization coupling of multiphase metal materials as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored in a computer-readable storage medium. When the computer program is executed by a processor, the method for microstructure evolution of multiphase metal material phase transformation and recrystallization coupling according to any one of claims 1 to 7 can be implemented.

Images