Visual modeling method for cutting surface microstructure deformation and evolution coupling

Abstract

The invention relates to the technical field of cutting machining, and discloses a cutting surface microstructure deformation and evolution coupling visual modeling method which comprises the following steps: acquiring microstructure characteristic parameters of a material; generating a multi-physics field coupling boundary condition based on the obtained microstructure characteristic parameters; establishing a grain dynamic recrystallization model according to microstructure characteristic parameters and multi-physics field coupling boundary conditions of the material, and introducing a grain dragging evolution rule at the same time; and a cellular automaton (CA) is used for carrying out adaptive iterative calculation conforming to a grain dragging evolution rule on the grain dynamic recrystallization model, and a two-dimensional model of the microstructure of the material under the grain refinement and grain dragging phenomena is obtained through prediction. According to a grain dynamic recrystallization theory and a grain dragging rule, microstructure evolution of a metal material under a high-strain-rate processing condition is predicted through initial microstructure information of the material and other parameters, and grain dragging is visually simulated.

Description

Technical Field

[0001] This invention relates to the field of cutting technology, specifically to a visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces. Background Technology

[0002] In precision machining processes such as turning and milling of metal materials, the machined surface is subjected to extreme working conditions, specifically characterized by severe plastic deformation and high strain rates (up to 10). 3 -10 6 s -1 The process involves instantaneous high temperatures (local temperatures exceeding the material's recrystallization temperature) and rapid cooling, accompanied by localized stress concentration. This multi-field coupling leads to significant non-uniform deformation of the grains on the processed surface, manifested as rapid grain elongation and fibrosis along the processing direction, while simultaneously forming shear band structures aligned with the processing direction. This deformation mechanism drives the dynamic recrystallization process of the grains, resulting in a micrometer-thick white layer structure on the processed surface, characterized by grain nanostructuring and a unique microstructure.

[0003] In existing technologies, researchers such as M. Brown have characterized the white layer structure using non-destructive testing techniques (such as electron backscatter diffraction EBSD), revealing that its microstructure exhibits a substrate-hexagonal close-packed (BCC / HCP) texture. They also pointed out that the grain drag effect alters the grain boundary migration direction, causing the grain refinement direction to deviate from the traditional recrystallization pattern, thus forming a surface structure distinct from conventional recrystallization. However, current research on the white layer formation mechanism still suffers from the following technical limitations: Although the academic community has conducted extensive research on the dominant factors in the formation of white layers (such as dynamic recrystallization, phase transformation, and adiabatic shear), a unified theoretical model has not yet been formed, especially regarding the grain evolution path under multi-field coupling. Current research on grain dragging effects is mostly limited to macroscopic experimental observation (such as metallographic microscopy), lacking quantitative descriptions of microscopic mechanisms such as grain orientation evolution and strain gradient distribution, resulting in a lack of theoretical basis for process optimization. Although existing visualization models of microstructure evolution can predict dynamic recrystallization and phase transformation phenomena, they do not integrate modules for grain torsional deformation, fibrosis, and recrystallization texture evolution, and cannot truly reflect the complex formation process of white layers on processed surfaces.

[0004] The aforementioned technical bottlenecks directly restrict the optimization of machining processes for key components of high-end equipment (such as aero-engine blades and nuclear power plant main pump impellers). These components have extremely stringent requirements for surface integrity, and the thickness, texture characteristics, and residual stress distribution of the white layer on the machined surface directly affect the fatigue life and corrosion resistance of the components. Summary of the Invention

[0005] To address existing problems, this invention aims to provide a visual modeling method for the coupling of microstructure deformation and evolution on cutting surfaces. Based on the theory of dynamic recrystallization of grains and the grain dragging rule, the method predicts the microstructure evolution of metallic materials under high strain rate processing conditions by using initial microstructure information and parameters such as stress, strain, temperature, strain rate, and dislocation density at the integration point. The method visualizes and simulates grain dragging, improving the accuracy of microstructure visualization prediction and providing a basis for subsequent processing design and surface integrity analysis.

[0006] To achieve the above objectives, the present invention provides the following technical solution.

[0007] In a first aspect, the present invention provides a visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces, comprising the following steps: A100: Obtain microstructure characteristics of the material; A200: Generate multiphysics coupled boundary conditions based on the obtained microstructure characteristic parameters; A300: Based on the microstructure characteristics of the material and the multi-physics field coupled boundary conditions, a dynamic recrystallization model of grains is established, and a grain dragging evolution rule is introduced. A400: Using cellular automata (CA), an adaptive iterative calculation conforming to the grain dragging evolution rule is performed on the grain dynamic recrystallization model to predict the two-dimensional model of the material's microstructure under grain refinement and grain dragging phenomena.

[0008] As a further improvement of the present invention, the grain dragging evolution rule is to achieve cell movement by adjusting the fitting parameter c in the control equation to simulate dragging evolution, wherein the control equation is... y is the drag strength, ε max is the maximum surface strain, h is the depth of cut influence, and c is the fitting parameter.

[0009] As a further improvement of the present invention, the method of obtaining the microstructure characteristics of the material includes the following steps: EBSD technology is used to perform a full-domain scan of the unprocessed material to obtain the initial grain morphology, orientation distribution and microstructure characteristics, and then discretize them into an initial state matrix.

[0010] As a further improvement of the present invention, the generation of multiphysics coupled boundary conditions includes the following steps: A thermo-mechanical coupling model of the processing process is constructed using the finite element method. The distribution data of stress field, strain field, strain rate field and temperature field on the processing surface are extracted and mapped to the dynamic boundary conditions of the cellular automata model after spatial interpolation.

[0011] As a further improvement of the present invention, the generation of multi-physics coupled boundary conditions includes the following steps: using high-speed photography combined with digital image correlation technology to conduct in-situ observation of the first deformation zone of the cutting, obtaining the spatiotemporal distribution data of strain-strain rate in the plastic deformation zone, and constructing experimental data-driven boundary conditions through machine learning algorithms.

[0012] As a further improvement of the present invention, the establishment of the grain dynamic recrystallization model includes the following steps: A grain boundary migration driving force model based on dislocation density is established and coupled with the stress gradient field in the processing direction; an anisotropic drag coefficient dependent on grain orientation is developed to quantify the difference in deformation resistance of grains with different orientations; and a strain rate strengthening term is constructed to achieve dynamic enhancement of the drag effect under high strain rate conditions.

[0013] As a further improvement of the present invention, the establishment of the grain dynamic recrystallization model includes the following steps: A grain boundary migration driving force model based on dislocation density is established and coupled with the stress gradient field in the processing direction; an anisotropic drag coefficient dependent on grain orientation is developed to quantify the difference in deformation resistance of grains with different orientations; and a strain rate strengthening term is constructed to achieve dynamic enhancement of the drag effect under high strain rate conditions.

[0014] As a further improvement of the present invention, a cellular automaton (CA) is used to perform adaptive iterative calculations on the grain dynamic recrystallization model in accordance with the grain dragging evolution rules, including the following steps: The cell space size of the cellular automaton model is selected, and an NxN cell matrix is ​​established for calculation. The cellular automaton model adopts u neighbor rules, and in some regions, v neighbor rules are adopted, where v is greater than u, to ensure the accuracy of the calculation. The parameters assigned to each cell in the NxN cell matrix by the cellular automaton model include: dislocation density, stress-strain rate, temperature, grain orientation, grain number, grain boundary marker, and recrystallization number. The recrystallization process employs a temperature-dependent probability density function to control the nucleation of the cellular automata model. Each iteration step synchronously updates the finite element field variables and the microstructure state of the cellular automata; The drag coefficient is calibrated in real time using a multi-objective optimization algorithm to improve the consistency between the prediction results and the experimental data.

[0015] Secondly, this invention also discloses a visualization modeling system for the coupling of microstructure deformation and evolution of cutting surfaces, comprising: The dataset construction module is used to acquire the microstructure characteristics of materials in real time, including the initial grain morphology and orientation distribution microstructure characteristics. The prediction model building module constructs a grain dynamic recrystallization model based on the microstructure characteristic parameter dataset. The prediction result acquisition module is used to analyze grain refinement and grain dragging phenomena based on the grain dynamic recrystallization model, and obtain visualized prediction results.

[0016] Thirdly, the present invention also discloses a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform a visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces as described in any one of the first aspects.

[0017] Fourthly, the present invention also discloses a computer program product containing instructions that, when the computer program product is run on an electronic device, causes the electronic device to perform a visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces as described in any one of the first aspects.

[0018] The present invention has the following beneficial effects: This method considers the microstructure characteristics and multi-physics coupled boundary conditions of materials in the fields of grain dynamic recrystallization theory and grain dragging rule simulation. By establishing a grain dynamic recrystallization model and introducing grain dragging evolution rules, and then using cellular automata adaptive iterative calculation, it can accurately predict the two-dimensional model of material microstructure under grain refinement and grain dragging phenomena. This provides an effective visualization means for a deeper understanding of the coupling process of microstructure deformation and evolution of cutting surfaces in precision machining such as turning (milling), especially under conditions of severe plastic deformation, high strain rate, instantaneous high temperature and rapid cooling effect. It can be used to predict the microstructure evolution results brought about by plastic deformation and recrystallization, and thus predict surface properties. In actual industrial production, it can significantly reduce costs and improve simulation and analysis efficiency, and has important significance for computer-aided analysis and design of part machining deformation processes. The visualization model construction method can fill the gap in the visualization prediction of plastic deformation during cutting.

[0019] Furthermore, by clarifying the grain dragging evolution rule as adjusting the fitting parameter c in the control equation to achieve cell movement to simulate dragging evolution, the simulation of grain dragging phenomenon becomes more accurate and controllable, and can more realistically reflect the dynamic process of grain dragging in the microstructure of the cutting surface, improving the accuracy and reliability of modeling, and thus enhancing the simulation effect of the deformation and evolution coupling process of the microstructure of the cutting surface.

[0020] Furthermore, EBSD technology is used to perform a full-domain scan of the unprocessed material to obtain microstructure characteristics such as initial grain morphology and orientation distribution, and then discretizes them into an initial state matrix. This method can obtain comprehensive and accurate initial microstructure information of the material, providing a reliable data foundation for the subsequent establishment of an accurate grain dynamic recrystallization model.

[0021] Optionally, a thermo-mechanical coupling model of the processing process can be constructed using the finite element method to extract the distribution data of stress field, strain field, strain rate field and temperature field on the processing surface. After spatial interpolation, these data are mapped to the dynamic boundary conditions of the cellular automaton model. This method can accurately obtain the distribution of multiple physical fields during the processing and reasonably transform them into the boundary conditions of the cellular automaton model.

[0022] Optionally, high-speed photography combined with digital image correlation technology can be used to conduct in-situ observations of the first deformation zone of the cutting process, obtain the spatiotemporal distribution data of strain-strain rate in the plastic deformation zone, and construct experimental data-driven boundary conditions through machine learning algorithms. This method of constructing boundary conditions based on actual experimental data can more directly reflect the actual stress and deformation of the microstructure during the cutting process.

[0023] Furthermore, establishing a grain boundary migration driving force model based on dislocation density and coupling it with a processing direction stress gradient field can more accurately describe the driving force source of grain boundary migration, making the grain dynamic recrystallization model more consistent with the actual physical process. Developing a grain orientation-dependent anisotropic drag coefficient quantifies the difference in deformation resistance of grains with different orientations, considering the influence of grain orientation on deformation resistance, and improving the model's simulation accuracy of the behavior of grains with different orientations during cutting. Constructing a strain rate strengthening term realizes the dynamic enhancement of the drag effect under high strain rate conditions, enabling the model to better adapt to the simulation of microstructure deformation and evolution under high strain rate cutting conditions, thus broadening the model's application scope.

[0024] Furthermore, by selecting an appropriate cell size for the cellular automata model and establishing an NxN cell matrix for calculation, and employing different neighbor rules (u neighbor rules and v neighbor rules for some regions, where v is greater than u), the accuracy of the calculation is improved while ensuring computational efficiency, enabling a more precise simulation of the dynamic changes in grains. Each cell in the cellular automata model is endowed with rich parameters (dislocation density, stress-strain rate, temperature, grain orientation, grain number, grain boundary marker, and recrystallization number), comprehensively considering various factors affecting the dynamic recrystallization of grains, making the model more complete and accurate. The recrystallization process uses a temperature-dependent probability density function to control the nucleation of the cellular automata model, which better reflects the relationship between nucleation and temperature in actual recrystallization, improving the accuracy of recrystallization simulation. Through real-time calibration of the drag coefficient using a multi-objective optimization algorithm, the model parameters can be continuously optimized according to actual conditions, improving the agreement between predicted results and experimental data, and enhancing the model's predictive ability and reliability. Attached Figure Description

[0025] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and are not intended to specifically limit the shapes and proportions of the components. In the drawings: Figure 1 A flowchart illustrating a visualization modeling method for the coupling of microstructure deformation and evolution of a cutting surface provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of the simulation process for Example 1. Figure 3 The initial microstructure of the titanium alloy material; Figure 4 This is a diagram showing the microstructure evolution exhibiting grain dragging. Figure 5 This is a schematic diagram of the operation process of a visualization modeling system for coupling the microstructure deformation and evolution of a cutting surface, provided in an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0027] It should be noted that when an element is referred to as being "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is stated to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] Example 1 like Figure 1 As shown, a visualization modeling method for the coupling of microstructure deformation and evolution on a cutting surface includes the following steps: A100: Obtain microstructure characteristics of the material; A200: Generate multiphysics coupled boundary conditions based on the obtained microstructure characteristic parameters; A300: Based on the microstructure characteristics of the material and the multi-physics field coupled boundary conditions, a dynamic recrystallization model of grains is established, and a grain dragging evolution rule is introduced. A400: Using cellular automata (CA), an adaptive iterative calculation conforming to the grain dragging evolution rule is performed on the grain dynamic recrystallization model to predict the two-dimensional model of the material's microstructure under grain refinement and grain dragging phenomena.

[0030] The grain dragging evolution rule is achieved by adjusting the fitting parameter c in the control equation to move the cell, thereby simulating dragging evolution. The control equation is... y is the drag strength, ε max is the maximum surface strain, h is the depth of cut influence, and c is the fitting parameter.

[0031] Obtaining the fitting parameter c involves the following steps: A301: Obtain the maximum surface strain ε according to DIC (or other strain rate testing experiments). max ; A302: Determine the depth of cut influence h using EBSD (or a more precise characterization method); A303: Determine the plastic deformation region based on the depth of the affected layer, and combine the cutting influence depth h and the maximum surface strain ε. maxSubstitute the parameters into the governing equation to determine the fitting parameter c, and determine the range of c values ​​for the corresponding materials through multiple experiments.

[0032] The process of obtaining the microstructure characteristics of the material includes the following steps: using EBSD technology to perform a full-domain scan of the unprocessed material to obtain the initial grain morphology and orientation distribution microstructure characteristics, and discretizing them into an initial state matrix.

[0033] The generation of multi-physics coupled boundary conditions includes the following steps: constructing a thermo-mechanical coupled model of the processing process using the finite element method, extracting the distribution data of stress field, strain field, strain rate field and temperature field of the processing surface, and mapping them to the dynamic boundary conditions of the cellular automata model after spatial interpolation.

[0034] The generation of multi-physics coupled boundary conditions includes the following steps: using high-speed photography combined with digital image correlation technology to conduct in-situ observation of the first deformation zone of the cut, obtaining the spatiotemporal distribution data of strain-strain rate in the plastic deformation zone, and constructing experimental data-driven boundary conditions through machine learning algorithms.

[0035] The establishment of the grain dynamic recrystallization model includes the following steps: establishing a grain boundary migration driving force model based on dislocation density and coupling it with the stress gradient field in the processing direction; developing an anisotropic drag coefficient dependent on grain orientation to quantify the difference in deformation resistance of grains with different orientations; and constructing a strain rate strengthening term to achieve dynamic enhancement of the drag effect under high strain rate conditions.

[0036] The process involves using cellular automata (CA) to perform adaptive iterative calculations on a grain dynamic recrystallization model that conform to the grain dragging evolution rules. This includes the following steps: The CA model selects a cell size of 30μm × 30μm, dividing it into 300 × 300 cells for calculation. The model primarily uses the 8-neighbor rule, with a 27-neighbor rule used in some regions to ensure calculation accuracy. The cellular automata model assigns the following main parameters to each cell: dislocation density, stress-strain rate, temperature, grain orientation, grain number, grain boundary marker, and recrystallization number. The recrystallization process uses a temperature-dependent probability density function for nucleation control; Each iteration step synchronously updates the finite element field variables and the microstructure state of the cellular automata; The drag coefficient is calibrated in real time using a multi-objective optimization algorithm to ensure that the predicted results match the experimental data.

[0037] like Figure 2 As shown, the specific implementation of the above method starts from "starting". First, "assign initial data to the cell". The initial data includes temperature, element, grain, initial phase, strain, strain rate, etc. If no plastic deformation occurs: Proceed to "Calculate dynamic recrystallization energy consumption", then determine "whether nucleation occurs during recrystallization": If nucleation does not occur, return to the energy consumption calculation step; if nucleation occurs, execute the following in sequence: calculate the grain boundary energy and driving force of the grains surrounding the nucleated grain, then determine the growth direction of the new grain based on the driving force, then calculate the grain orientation difference angle and growth rate, then calculate the grain dislocation density, and finally update the grain statistics information; after completion, determine "whether the recrystallization process has ended". If it has not ended, return to the "whether nucleation occurs during recrystallization" step and repeat until the process ends. If plastic deformation occurs, proceed to the "crystal phase deformation" process: calculate the grain deformation criterion based on the maximum plastic strain on the surface, calculate the strain of each layer according to the depth, perform positional changes based on each cell, and identify the crystal phase parameters; after completion, return to the "calculate dynamic recrystallization energy consumption" step and connect to the subsequent process; When the recrystallization process ends, dynamic recrystallization is complete, and the simulation concludes.

[0038] Taking titanium alloy as an example, the following steps implement a visual modeling of the coupling of microstructure deformation and evolution on the cutting surface: Step 1) Heat-treat the titanium alloy material by stress-relief annealing at the β-phase transformation temperature (approximately 700°C) and slow cooling to obtain a material with an initial phase that is almost entirely composed of equiaxed α-phase and an average grain size of 10 μm.

[0039] Step 2) The material parameters are calibrated. The basic mechanical parameters and physical constitutive parameters of the material are obtained through calibration experiments. The EBSD data of the material is obtained by metallographic characterization, and the initial grain distribution information and grain size parameters of the material are obtained.

[0040] Step 3) Input the initial grain data of the titanium alloy material into the cellular automaton (CA) to generate an initial image of the microstructure evolution.

[0041] Step 4) Write a dynamic recrystallization model for grains in the cellular automaton and add a formula to describe the grain dragging rules. Through this formula, the lateral displacement y of cells at different positions along the processing direction is obtained. The control coefficient c is obtained by fitting the cellular automaton (CA) and h is the depth from the processing surface.

[0042]

[0043] Step 5) Establish a finite element model for cutting titanium alloy materials. Perform cutting simulation with real machining parameters, extract data such as stress, strain, strain rate and dislocation density at the integral points of the machining surface, and input them into the cellular automaton as boundary conditions for microstructure evolution. Perform dynamic recrystallization judgment and grain boundary migration within the cellular automaton. Finally, after multiple iterations, obtain the microstructure evolution prediction results with grain drag phenomenon.

[0044] Figure 3 Showing the initial microstructure of titanium alloy materials, Figure 4 The diagram shows the microstructure evolution exhibiting grain dragging, with the values ​​on the right representing Euler angles.

[0045] Example 2 like Figure 5 As shown, this embodiment provides a visualization modeling system for the coupling of microstructure deformation and evolution of cutting surfaces, characterized by comprising: The dataset construction module is used to acquire the microstructure characteristics of materials in real time, including the initial grain morphology and orientation distribution microstructure characteristics. The prediction model building module constructs a grain dynamic recrystallization model based on the microstructure characteristic parameter dataset. The prediction result acquisition module is used to analyze grain refinement and grain dragging phenomena based on the grain dynamic recrystallization model, and obtain visualized prediction results.

[0046] Example 3 This application provides a computer-readable storage medium storing instructions, characterized in that, when the instructions are executed on an electronic device, the electronic device performs a visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces as described in any one of Embodiments 1. The computer-readable storage medium includes, but is not limited to, USB flash drives, hard drives, portable hard drives, cloud storage under cloud technology, and even web pages (here, a web page specifically refers to a web page capable of recording the aforementioned computer program).

[0047] Example 4 This application provides a computer program product containing instructions, characterized in that, when the computer program product is run on an electronic device, the electronic device executes a visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces as described in any one of Embodiment 1.

[0048] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0049] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0050] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that an article or apparatus comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or apparatus that includes said element.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for visualizing modeling of microstructure evolution and deformation coupling of a cutting surface, characterized in that, Includes the following steps: A100: Obtain the microstructure characteristics of the material; A200: Generate multiphysics coupled boundary conditions based on the obtained microstructure characteristic parameters; A300: Based on the microstructure characteristics of the material and the multi-physics field coupled boundary conditions, a dynamic recrystallization model of grains is established, and a grain dragging evolution rule is introduced. A400: Using cellular automata (CA) to perform adaptive iterative calculations on the grain dynamic recrystallization model in accordance with the grain dragging evolution rules, a two-dimensional model of the material's microstructure under grain refinement and grain dragging phenomena is predicted.

2. The visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces according to claim 1, characterized in that, The grain drag evolution rule is to realize the movement of the cell by adjusting the fitting parameter c in the control equation to simulate the drag evolution, wherein the control equation is , y is the drag strength, ε max is the maximum surface strain, h is the cutting influence depth, and c is the fitting parameter.

3. The visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces according to claim 1, characterized in that, The process of obtaining the microstructure characteristics of the material includes the following steps: EBSD technology is used to perform a full-domain scan of the unprocessed material to obtain the initial grain morphology, orientation distribution and microstructure characteristics, and then discretize them into an initial state matrix.

4. The visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces according to claim 1, characterized in that, The generation of multiphysics coupled boundary conditions includes the following steps: A thermo-mechanical coupling model of the processing process is constructed using the finite element method. The distribution data of stress field, strain field, strain rate field and temperature field on the processing surface are extracted and mapped to the dynamic boundary conditions of the cellular automata model after spatial interpolation.

5. The visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces according to claim 1, characterized in that, The generation of multiphysics coupled boundary conditions includes the following steps: using high-speed photography combined with digital image correlation technology to conduct in-situ observation of the first deformation zone of the cut, obtaining the spatiotemporal distribution data of strain-strain rate in the plastic deformation zone, and constructing experimental data-driven boundary conditions through machine learning algorithms.

6. The visualization modeling method for the coupling of microstructure deformation and evolution of cutting surfaces according to claim 1, characterized in that, The establishment of the grain dynamic recrystallization model includes the following steps: A grain boundary migration driving force model based on dislocation density is established, coupled with the stress gradient field along the processing direction; Develop an anisotropic drag coefficient that depends on grain orientation to quantify the differences in deformation resistance of grains with different orientations; A strain rate strengthening term is constructed to achieve dynamic enhancement of the drag effect under high strain rate conditions.

7. The visualization modeling method for coupling microstructure deformation and evolution of a cutting surface according to claim 1, characterized in that, The following steps are included in the adaptive iterative calculation of the grain dynamic recrystallization model using cellular automata (CA) that conforms to the grain drag evolution rules: The cell space size of the cellular automaton model is selected, and an NxN cell matrix is ​​established for calculation. The cellular automaton model adopts u neighbor rules, and in some regions, v neighbor rules are adopted, where v is greater than u, to ensure the accuracy of the calculation. The parameters assigned to each cell in the NxN cell matrix by the cellular automaton model include: dislocation density, stress-strain rate, temperature, grain orientation, grain number, grain boundary marker, and recrystallization number. The recrystallization process employs a temperature-dependent probability density function to control the nucleation of the cellular automata model. Each iteration step synchronously updates the finite element field variables and the microstructure state of the cellular automata; The drag coefficient is calibrated in real time using a multi-objective optimization algorithm to improve the consistency between the prediction results and the experimental data.

8. A visualization modeling system for the coupling of microstructure deformation and evolution of a cutting surface according to any one of claims 1-7, characterized in that, include: The dataset construction module is used to acquire the microstructure characteristics of materials in real time, including the initial grain morphology and orientation distribution microstructure characteristics. The prediction model building module constructs a grain dynamic recrystallization model based on the microstructure characteristic parameter dataset. The prediction result acquisition module is used to analyze grain refinement and grain dragging phenomena based on the grain dynamic recrystallization model, and obtain visualized prediction results.

9. A computer-readable storage medium storing instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device performs a visualization modeling method for the coupling of microstructure deformation and evolution of the cutting surface as described in any one of claims 1-7.

10. A computer program product containing instructions, characterized in that, When a computer program product is run on an electronic device, the electronic device performs a visualization modeling method for the coupling of microstructure deformation and evolution of the cutting surface as described in any one of claims 1-7.

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