A method, device and storage medium for simulating dynamic recrystallization under a polyphase condition

Abstract

The present invention discloses a method, device and storage medium for simulating dynamic recrystallization under complex phase conditions, belonging to the technical field of computational materials science. By establishing an initial tissue geometry model based on cellular automata, it is possible to effectively simulate the dynamic recrystallization evolution process under the condition of second-phase particles with morphologies such as rod-shaped and needle-shaped, and accurately reflect the morphological characteristics and transformation kinetics of the internal microstructure of the material under the combined action of second-phase particles with morphologies such as rod-shaped and needle-shaped, applied strain and thermal activation, which cannot be achieved by traditional cellular automata simulation methods. The present invention fills the gap in the prior art, provides a theoretical basis for engineering practice, and has good application prospects.

Description

Technical Field

[0001] The present invention belongs to the technical field of computational materials science, and particularly relates to a method, device and storage medium for simulating dynamic recrystallization under multi-phase conditions. Background Art

[0002] Dynamic recrystallization (DRX), as the main mechanism of metal microstructure evolution, is considered an effective method for grain refinement under hot plastic deformation. The new grains generated during the DRX process and grain refinement are important factors affecting the load, microstructure, and product forming quality during hot processing. Therefore, it is crucial to deeply study the recrystallization kinetics and microstructure evolution during the DRX process.

[0003] Currently, due to significant advantages such as not requiring interface tracking, high computational efficiency, and simplicity in implementation, the Cellular Automaton (CA) method has become a powerful tool for studying the evolution of material microstructures. Moreover, the CA method has been widely applied and has a good research foundation in predicting microstructure evolution such as the recrystallization process and grain growth. However, the current CA method models can only simulate the DRX process of materials under single-phase alloys or conditions containing regular spherical second phases. In fact, the morphology of the second phase in many metal materials is not spherical but rod-shaped or needle-shaped, such as the δ phase of GH4169 alloy, the AlCuMn phase of 2297 aluminum-lithium alloy, etc. Therefore, the original CA model still needs to be further improved and perfected.

[0004] When the existing CA model simulates the DRX process under single-phase conditions, it only considers the driving force for the growth of recrystallized grains caused by grain boundary energy and distortion energy caused by deformation; when simulating the DRX process under multi-phase conditions, in addition to considering the driving forces of grain boundary energy and distortion energy caused by deformation, the second phase is regarded as spherical particles to calculate the resistance of pinning grain boundary migration. The resistance of spherical particles to grain boundary migration in all directions is the same. Obviously, when the second-phase particles are not spherical in morphology, the resistance of the second-phase particles to grain boundary migration in all directions is not the same. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method, device and storage medium for simulating dynamic recrystallization under multi-phase conditions, which can effectively simulate the DRX evolution process under conditions of second-phase particles with rod-shaped, needle-shaped and other morphologies, and accurately reflect the morphological characteristics and transformation kinetics of the internal microstructure of materials under the combined action of second-phase particles with rod-shaped, needle-shaped and other morphologies, applied strain, and thermal activation.

[0006] The present invention is realized through the following technical solutions:

[0007] The present invention discloses a method for simulating dynamic recrystallization under a multi-phase condition, including:

[0008] S1: Establish an initial microstructure geometric model based on the cellular automaton method according to the initial microstructure metallographic picture;

[0009] S2: Input deformation conditions into the initial microstructure geometric model established in S1;

[0010] S3: Determine the number of dynamically recrystallized nuclei according to the nucleation rate;

[0011] S4: Determine the step size of the calculation time step, the number of cyclic time steps, and the strain increment of each time step;

[0012] S5: At each calculation time step, randomly select a cell at the grain boundary, judge whether there are second-phase particles in the neighbors of the current cell, and control whether the current cell changes from the matrix microstructure to a dynamically recrystallized grain nucleus by comparing the nucleation probability obtained by comparing a random number between 0 and 1 with the number of nuclei. If the current cell meets the nucleation condition, the recrystallization state variable changes from 0 to 1; otherwise, the current cell does not meet the nucleation condition, and the recrystallization state variable remains unchanged;

[0013] S6: Traverse all cells in the current calculation domain, sequentially judge whether each cell meets the dynamically recrystallized cell transformation rule, transform the cell state of the cells that meet the nucleation condition, and migrate the dynamically recrystallized grain boundary to realize the growth of dynamically recrystallized grains;

[0014] S7: Calculate the current strain through the step size of the calculation time step, the number of cyclic time steps, and the strain increment of each time step determined in S4, and compare it with the preset strain to judge whether the calculation is over. If the preset strain is reached, terminate the calculation and output the simulation result. If the preset strain is not reached, return to S3 until the current strain reaches the preset strain.

[0015] Preferably, in S1, the initial microstructure geometric model includes the characteristics of the grain microstructure and the second-phase particles, and the morphology of the second-phase particles is rod-shaped or needle-shaped.

[0016] Preferably, in S2, the deformation conditions include the simulation temperature, the strain rate, and the strain.

[0017] Preferably, in S3, the nucleation rate includes the DRX nucleation rate at the single-phase matrix grain boundary and the DRX nucleation rate induced by the second-phase particles. Among them, the DRX nucleation rate at the single-phase matrix grain boundary is calculated by the following formula:

[0018]

[0019] In the formula, C DRX is a material constant; is the strain rate; m * is the strain rate sensitivity coefficient, taking 1; Q DRX_nuc is the activation energy required for the nucleation of DRX; R is the gas constant; T is the hot deformation temperature of the material, taking the Kelvin temperature;

[0020] The DRX nucleation rate induced by the second-phase particles is calculated by the following formula:

[0021]

[0022] In the formula, c sec is a constant introduced to reduce the error; a0 is the major axis length of the second-phase particle; b0 is the minor axis length of the second-phase particle;

[0023] The DRX nucleation rate model considering the influence of the second phase is:

[0024]

[0025] Preferably, in S4, the step size of the calculation time step and the number of loop time steps are determined according to the cell unit size and the grain boundary migration velocity, where the cell unit size is the smallest calculation unit during the calculation of the initial microstructure geometric model; the grain boundary migration velocity v is calculated by the following formula:

[0026] v = MP

[0027] In the formula, M is the grain boundary mobility; P is the grain boundary migration driving force;

[0028] The grain boundary mobility M is calculated by the following formula:

[0029]

[0030] In the formula, M0 is the interface mobility coefficient; Q is the interface diffusion activation energy; R and T are the universal gas constant and the absolute temperature respectively;

[0031] The grain boundary migration driving force P is calculated by the following formula:

[0032] P = P drive -P drag

[0033] In the formula, P drive is the driving force considering the grain boundary energy and the distortion energy caused by deformation; P drag is the resistance to the grain boundary migration when the rod-shaped or needle-shaped second-phase particle is at 90° to the grain boundary migration direction;

[0034] Among them, P drag is calculated by the following formula:

[0035] P drag = fsec γ / (b0((1 + ε axis )ε axis 1 / 3 ))

[0036] In the formula, f sec is the volume fraction of the second-phase particles; γ is the grain boundary energy; b0 is the length of the minor axis of the second-phase particles; ε axis is the ratio of the major axis length to the minor axis length of the second-phase particles.

[0037] Further preferably, when the second-phase particles meet the grain boundary at different angles, the area S of pinning the grain boundary migration is different, and the pinning force is different. The effective pinning force P drag-effective is used to correct the pinning force model:

[0038] P drag-effectived = P ragg S / 2b0

[0039] The driving force P for grain boundary migration is:

[0040] P = P drive - P drag-effective .

[0041] Preferably, in S5, the newly nucleated dynamically recrystallized grains maintain an orientation difference of more than 15° with the surrounding grains.

[0042] Preferably, in S7, the simulation results include a microstructure diagram and a dynamic recrystallization kinetics curve.

[0043] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for simulating dynamic recrystallization under the above-mentioned multi-phase conditions are realized.

[0044] The present invention discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for simulating dynamic recrystallization under the above-mentioned multi-phase conditions are realized.

[0045] Compared with the prior art, the present invention has the following beneficial technical effects:

[0046] For the traditional CA model, under the conditions of single-phase grain structure and the structure containing granular second-phase particles, there are relatively mature physical metallurgy models and reasonable cell transformation rules for simulating the dynamic recrystallization process. However, for the conditions of second-phase particles with rod-shaped, needle-shaped and other morphologies, the traditional physical metallurgy models and cell transformation rules are not applicable. It is necessary to correct the traditional physical metallurgy models and formulate cell transformation rules applicable to the dynamic recrystallization process under the conditions of second-phase particles with rod-shaped or needle-shaped morphologies.

[0047] The method for simulating dynamic recrystallization under simulated polyphase conditions disclosed by the present invention can effectively simulate the dynamic recrystallization evolution process under the condition of second-phase particles with rod-shaped, needle-shaped and other morphologies, and can accurately reflect the morphological characteristics and transformation kinetics of the internal microstructure of the material under the combined action of second-phase particles with rod-shaped, needle-shaped and other morphologies, applied strain and thermal activation, etc. This cannot be achieved by the traditional cellular automaton simulation method. The present invention makes up for the gaps in the prior art, provides a theoretical basis for engineering practice, and has good application prospects. Brief Description of the Drawings

[0048] Figure 1 It is a schematic flow chart of the method of the present invention;

[0049] Figure 2 It is the initial microstructure diagram of GH4169 alloy containing δ-phase particles in the embodiment;

[0050] Figure 3 It is the initial microstructure model of the cellular automaton method established based on the actual initial microstructure in the embodiment;

[0051] Figure 4 At T = 980 °C, Comparison of experimental results and calculation results of microstructure using the method of the present invention under different strain conditions: (a, d) Experimental results at ε = 0.3; (b, e) Experimental results at ε = 0.5; (c, f) Experimental results at ε = 0.7; (g) Simulation results at ε = 0.3; (h) Simulation results at ε = 0.5; (i) Simulation results at ε = 0.3. Detailed Embodiment

[0052] The following further describes the present invention in detail with reference to the drawings and specific embodiments, which is an explanation rather than a limitation of the present invention.

[0053] As Figure 1 , the method for simulating dynamic recrystallization under simulated polyphase conditions of the present invention includes:

[0054] S1: Establish an initial microstructure geometric model based on the cellular automaton method according to the initial microstructure metallographic picture;

[0055] S2: Input deformation conditions into the initial microstructure geometric model established in S1;

[0056] S3: Determine the number of nuclei for dynamic recrystallization according to the nucleation rate;

[0057] S4: Determine the step size of the calculation time step, the number of loop time steps and the strain increment for each time step;

[0058] S5: At each computational time step, randomly select a cell at the grain boundary, and determine whether there are second-phase particles in the neighbors of the current cell. By comparing the random number between 0 and 1 with the nucleation probability obtained from the number of nucleations, control whether the current cell transforms from the matrix structure to a dynamically recrystallized nucleus. If the current cell meets the nucleation condition, the recrystallization state variable changes from 0 to 1; otherwise, the current cell does not meet the nucleation condition, and the recrystallization state variable remains unchanged.

[0059] S6: Traverse all cells in the current computational domain, and sequentially determine whether each cell meets the transformation rule of dynamically recrystallized cells. For cells that meet the nucleation condition, transform the cell state, and migrate the dynamically recrystallized grain boundary to achieve the growth of dynamically recrystallized grains.

[0060] S7: Calculate the current strain based on the time step size, the number of cyclic time steps, and the strain increment per time step determined by S4, and compare it with the preset strain to determine whether the calculation is completed. If the preset strain is reached, terminate the calculation and output the simulation results. If the preset strain is not reached, return to S3 until the current strain reaches the preset strain.

[0061] In a preferred embodiment of the present invention, in S1, the initial tissue geometric model includes the characteristics of the grain structure and the second-phase particles, and the morphology of the second-phase particles is rod-shaped or needle-shaped.

[0062] In a preferred embodiment of the present invention, in S2, the deformation conditions include the simulation temperature, the strain rate, and the strain.

[0063] In a preferred embodiment of the present invention, in S3, the nucleation rate includes the DRX nucleation rate at the single-phase matrix grain boundary and the second-phase particle-induced DRX nucleation rate. Among them, the DRX nucleation rate at the single-phase matrix grain boundary is calculated by the following formula:

[0064]

[0065] In the formula, C DRX is a material constant; is the strain rate; m * is the strain rate sensitivity coefficient, taking 1; Q DRX_nuc is the activation energy required for the nucleation of DRX; R is the gas constant; T is the hot deformation temperature of the material, taking the Kelvin temperature;

[0066] The second-phase particle-induced DRX nucleation rate is calculated by the following formula:

[0067]

[0068] In the formula, c secThe constant introduced to reduce the error; a0 is the major axis length of the second-phase particles; b0 is the minor axis length of the second-phase particles;

[0069] The DRX nucleation rate model considering the influence of the second phase is:

[0070]

[0071] In a preferred embodiment of the present invention, in S4, the step size of the time step and the number of loop time steps are determined according to the cell unit size and the grain boundary migration velocity, where the cell unit size is the smallest calculation unit during the calculation of the initial microstructure geometric model; the grain boundary migration velocity v is calculated by the following formula:

[0072] v = MP

[0073] In the formula, M is the grain boundary mobility; P is the grain boundary migration driving force;

[0074] The grain boundary mobility M is calculated by the following formula:

[0075]

[0076] In the formula, M0 is the interface mobility coefficient; Q is the interface diffusion activation energy; R and T are the universal gas constant and the absolute temperature respectively;

[0077] The grain boundary migration driving force P is calculated by the following formula:

[0078] P = P drtive -P arag

[0079] In the formula, P drive is the driving force considering the grain boundary energy and the distortion energy caused by deformation; P drag is the resistance to grain boundary migration when the rod-shaped or needle-shaped second-phase particles are at 90° to the grain boundary migration direction;

[0080] Among them, P drag is calculated by the following formula:

[0081] P dirag = f sec γ / (b0((1 + ε axis )ε axis 1 / 3 ))

[0082] In the formula, f sec is the volume fraction of the second-phase particles; γ is the grain boundary energy; b0 is the length of the minor axis of the second-phase particles; ε axis is the ratio of the major and minor axis lengths of the second-phase particles.

[0083] When the second-phase particles meet the grain boundary at different angles, the area S of pinning the grain boundary migration is different, and the pinning force is different. The effective pinning force P is used. drag-effective Modify the pinning force model:

[0084] P arag-effective = P drag S / 2b0

[0085] The driving force P for grain boundary migration is:

[0086] P = P drive - P drag-effective .

[0087] In a preferred embodiment of the present invention, in S5, the newly nucleated dynamic recrystallized grains maintain an orientation difference of more than 15° from the surrounding grains.

[0088] In a preferred embodiment of the present invention, in S7, the simulation results include a microstructure map and a dynamic recrystallization kinetic curve.

[0089] The present invention will be further explained below with a specific embodiment:

[0090] This embodiment simulates the cellular automaton method of DRX under the condition that the GH4169 alloy contains δ-phase particles, the simulation temperature is 980 °C, and the strain rate is 0.1 s -1 , and the DRX process of the hot deformation process of the GH4169 alloy is simulated under the condition of a strain of 1.2. This method mainly includes three stages: initialization, cyclic simulation for each time step, and output of simulation results. Specifically, the steps are as follows:

[0091] Step 1: Establish an initial tissue geometric model according to the initial tissue metallographic picture of the GH4169 alloy (such as Figure 2 ), such as Figure 3 . Among them, the initial tissue geometric model includes the morphology, distribution and other characteristics of the grain tissue and δ-phase particles.

[0092] Step 2: Input the deformation conditions, the simulation temperature is 980 °C, and the strain rate is 0.1 s -1 , and the strain is 1.2.

[0093] Step 3: Determine the number of DRX nuclei, and the number of nuclei is determined by the nucleation rate. The nucleation rate consists of two parts: the DRX nucleation rate at the grain boundary of the single-phase matrix and the DRX nucleation rate induced by the second-phase particles (Particle Stimulated Nucleation, PSN). Among them, the DRX nucleation rate at the grain boundary of the single-phase matrix is calculated by the following formula:

[0094]

[0095] In the formula, is the nucleation rate of DRX for single-phase materials; C DRX is the material constant; is the strain rate; m * is the strain rate sensitivity coefficient, generally taken as 1; Q DRX_nuc is the activation energy required for nucleation of DRX; R is the gas constant, generally taken as 8.314 J·mol -1 .K -1 ; T is the hot deformation temperature of the material, taken as the Kelvin temperature, with a value of 1053 K.

[0096] The nucleation rate of PSN is calculated by the following formula:

[0097]

[0098] In the formula, is the nucleation rate of DRX caused by PSN; c δ is the constant introduced to reduce the error; a0 is the major axis length of the δ phase; b0 is the minor axis length of the δ phase. Then the DRX nucleation rate model considering the influence of the δ phase can be expressed as:

[0099]

[0100] Step 4: Determine the step size of the time step, the number of cyclic time steps, and the strain increment of each time step. The above step size and number of steps must be determined according to the cell unit size and the grain boundary migration speed. In this embodiment, the cell unit size is 1 μm, and the DRX grain boundary migration speed of the GH4169 alloy is calculated by the following formula:

[0101] v = MP

[0102] In the formula, v is the grain boundary migration speed; M is the grain boundary mobility; P is the grain boundary migration driving force. The grain boundary mobility can be calculated by the following formula:

[0103]

[0104] In the formula, M0 is the mobility coefficient of the austenite grain boundary of the GH4169 alloy; Q is the activation energy of interface diffusion, with a value of 273.1 KJ·mol -1 ; The grain boundary migration driving force P can be calculated by the following formula:

[0105] P = P drive -P drag

[0106] In the formula, P drive is the driving force considering the grain boundary energy and the distortion energy caused by deformation; P drag is the resistance of the rod-shaped or needle-shaped δ-phase particles to the grain boundary migration when the angle with the grain boundary migration direction is 90°.

[0107] Furthermore, the resistance to grain boundary migration can be calculated by the following formula:

[0108] P drag = f sec γ / (b0((1 + ε axis ))ε axis 1 / 3 ))

[0109] where f sec is the volume fraction of δ-phase particles; γ is the grain boundary energy; b0 is the length of the short axis of the δ-phase particles; ε axis is the ratio of the lengths of the long and short axes of the δ-phase particles.

[0110] Furthermore, when the δ-phase particles meet the grain boundary at different angles, the area S of pinning the grain boundary migration is different, and the pinning force is different. The effective pinning force P drag-effective is used to correct the pinning force model:

[0111] P drag-effective = P drag S / 2b0

[0112] Furthermore, the driving force P for grain boundary migration is:

[0113] P = P dive - P drag-effective

[0114] Step Five: At each calculation time step, randomly select a cell at the grain boundary, determine whether there are δ-phase particles in the neighbors of the current cell, and control whether the current cell transforms from the matrix structure to the DRX nucleus by comparing the random number r rand between 0 and 1 with the nucleation probability calculated from the number of nucleations. If the current cell meets the nucleation condition, the recrystallization state variable changes from 0 to 1; otherwise, the current cell does not meet the nucleation condition, and the recrystallization state variable remains unchanged. The DRX grain orientation values are assigned in the same way for newly nucleated DRX grains, that is, the orientation difference from the surrounding grains is kept greater than 15°.

[0115] Step Six: Traverse all the cells in the current calculation domain, and successively determine whether each cell meets the aforementioned DRX cell transformation rules. For the cells that meet all the cell transformation rules simultaneously, transform the cell state, migrate the DRX grain boundary, and realize the growth of DRX grains.

[0116] Step Seven: Calculate the current strain through the strain increment Δε and the calculation time step, and compare it with the preset strain of 1.2 to determine whether the calculation is over. If the DRX simulation set strain is reached, terminate the calculation and output the required simulation results, including the microstructure diagram, DRX kinetics curve, etc.; if not, return to Step Three to continue the calculation until the strain is calculated to the set strain value.

[0117] Figure 4 At T = 980 °C, Comparison of the experimental results under different strain conditions with the results of calculating the microstructure by using the method of the present invention. It can be seen from the figure that the method of the present invention accurately simulates the results of the dynamic recrystallization of the second-phase particles in the GH4169 alloy.

[0118] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for simulating dynamic recrystallization under the polyphase condition of the present invention are implemented.

[0119] The method for simulating dynamic recrystallization under the polyphase condition of the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. If the method for simulating dynamic recrystallization under the polyphase condition of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0120] Based on such an understanding, in an exemplary embodiment, a computer-readable storage medium is also provided. To implement all or part of the processes of the method in the above embodiments, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in this computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals. Among them, the computer storage medium can be any available medium or data storage device accessible by a computer, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSD)).

[0121] In an exemplary embodiment, a computer device is further provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for simulating dynamic recrystallization under a complex phase condition are implemented. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0122] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0123] The above are only specific embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for simulating dynamic recrystallization under a polyphase condition, characterized in that, Including: S1: Establish an initial microstructure geometric model based on the cellular automaton method according to the initial microstructure metallographic picture; S2: Input deformation conditions into the initial microstructure geometric model established in S1; S3: Determine the number of dynamic recrystallization nuclei according to the nucleation rate; S4: Determine the step size of the calculation time step, the number of cyclic time steps, and the strain increment of each time step; S5: At each calculation time step, randomly select a cell at the grain boundary, judge whether there are second-phase particles in the neighbors of the current cell, and control whether the current cell transforms from the matrix microstructure to a dynamic recrystallization nucleus by comparing the random number between 0 and 1 with the nucleation probability obtained from the number of nuclei. If the current cell meets the nucleation condition, the recrystallization state variable changes from 0 to 1; otherwise, the current cell does not meet the nucleation condition, and the recrystallization state variable remains unchanged; S6: Traverse all cells in the current calculation domain, sequentially judge whether each cell meets the dynamic recrystallization cell transformation rule, transform the cell state of the cells that meet the nucleation condition, and migrate the dynamic recrystallization grain boundary to realize the growth of dynamic recrystallization grains; S7: Calculate the current strain through the step size of the calculation time step, the number of cyclic time steps, and the strain increment of each time step determined in S4, and compare it with the preset strain to judge whether the calculation is over. If the preset strain is reached, terminate the calculation and output the simulation results. If the preset strain is not reached, return to S3 until the current strain reaches the preset strain.

2. The method for dynamically recrystallizing under simulated polyphase conditions according to claim 1, wherein In S1, the initial microstructure geometric model includes the characteristics of grain microstructure and second-phase particles, and the morphology of the second-phase particles is rod-shaped or needle-shaped.

3. The method for dynamically recrystallizing under simulated polyphase conditions according to claim 1, wherein In S2, the deformation conditions include simulation temperature, strain rate, and strain.

4. The method for dynamically recrystallizing under simulated polyphase conditions according to claim 1, wherein In S3, the nucleation rate includes the DRX nucleation rate at the single-phase matrix grain boundary and the DRX nucleation rate induced by the second-phase particles. Among them, the DRX nucleation rate at the single-phase matrix grain boundary is calculated by the following formula: where C DRX is a material constant; is the strain rate; m * is the strain rate sensitivity coefficient, taking 1; Q DRX_nuc is the activation energy required for the nucleation of DRX; R is the gas constant; T is the hot deformation temperature of the material, taking the Kelvin temperature; The nucleation rate of DRX induced by the second-phase particles Calculated by the following formula: where c sec is a constant introduced to reduce the error; a0 is the major axis length of the second-phase particles; b0 is the minor axis length of the second-phase particles; The DRX nucleation rate model considering the influence of the second phase is:

5. The method for dynamic recrystallization under simulated polyphase conditions according to claim 1, wherein In S4, the step size of the calculation time step and the number of cyclic time steps are determined according to the cell unit size and the grain boundary migration speed. Among them, the cell unit size is the smallest calculation unit during the calculation of the initial microstructure geometric model; the grain boundary migration speed v is calculated by the following formula: v = MP In the formula, M is the grain boundary mobility; P is the grain boundary migration driving force; The grain boundary mobility M is calculated by the following formula: In the formula, M0 is the interface mobility coefficient; Q is the interface diffusion activation energy; R and T are the universal gas constant and the absolute temperature respectively; The grain boundary migration driving force P is calculated by the following formula: P = P drive -P drag Wherein, P drive is the driving force considering the grain boundary energy and the distortion energy caused by deformation; P drag is the resistance to grain boundary migration when the rod-shaped or needle-shaped second-phase particles are at 90° to the grain boundary migration direction; where P drag is calculated by the following formula: P drag = f sec γ / (b0((1 + ε axis ))ε axis 1 / 3 )) In the formula, f sec is the volume fraction of the second-phase particles; γ is the grain boundary energy; b0 is the length of the minor axis of the second-phase particles; ε axis is the ratio of the major axis length to the minor axis length of the second-phase particles.

6. The method for dynamically recrystallizing under simulated polyphase conditions according to claim 5, wherein When the second-phase particles meet the grain boundary at different angles, the area S pinned to the grain boundary migration is different, and the pinning force is different. The effective pinning force P is used drag-effective Modify the pinning force model: P drag-effective = P drag S / 2b0 The grain boundary migration driving force P is: P = P drive -P drag-effective 。 7. The method for dynamically recrystallizing under simulated polyphase conditions according to claim 1, wherein In S5, the newly nucleated dynamic recrystallization grains maintain an orientation difference of more than 15° from the surrounding grains.

8. The method for dynamically recrystallizing under simulated polyphase conditions according to claim 1, characterized in that, In S7, the simulation results include a microstructure diagram and a dynamic recrystallization kinetics curve.

9. A computer device, characterized in that, Including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for simulating dynamic recrystallization under multi-phase conditions as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for simulating dynamic recrystallization under multi-phase conditions as described in any one of claims 1 to 7.

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