Ultrasonic magnetic field assisted low-speed wire cut electrical discharge machining microstructure simulation method

Abstract

The invention provides an ultrasonic magnetic field assisted low-speed wire cut electrical discharge machining microstructure simulation method, belongs to the technical field of wire cut electrical discharge machining, and aims to introduce ultrasonic waves and a magnetic field into existing low-speed wire cut electrical discharge machining, establish a thermomechanical coupling model, and combine finite element analysis and a cellular automaton model to simulate the microstructure of the low-speed wire cut electrical discharge machining. Accurate simulation is performed on distribution of a temperature field, a stress field and a strain field in the machining process, and meanwhile, dynamic evolution of crystal grains in a material cutting surface metamorphic layer is simulated through a cellular automaton, so that a visual microstructure evolution simulation result is obtained. According to the method, surface microstructures of materials such as shape memory alloy are remarkably improved by introducing ultrasound and a magnetic field, the grain size is reduced, and the uniformity of grains is improved; ultrasonic, magnetic field and thermomechanical coupling are combined, the influence of different factors on the microstructure of the material can be comprehensively considered, the limitation of a single factor in the prior art is broken through, and therefore more efficient machining optimization is achieved under the complex technological condition.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wire cutting processing, and particularly relates to an ultrasonic magnetic field assisted slow wire electro-discharge machining microstructure simulation method. BACKGROUND

[0002] With the progress of modern material science, shape memory alloys (SMA) are widely used in aerospace, medical devices and precision machinery due to their unique super-elasticity and constant temperature shape memory effect. However, in the process of high-precision machining, SMA materials often have problems such as uneven surface microstructure, coarse grains and poor surface quality, which directly affect the mechanical properties, fatigue life and reliability of the materials.

[0003] Currently, slow wire electro-discharge machining technology is also widely used in machining SMA materials, but the existing methods still have certain limitations. For example, traditional electro-discharge machining is prone to generate excessive heat effect in the machining process, resulting in rough surface metamorphic layer, and the machining precision and efficiency are difficult to meet the demand of high-performance materials.

[0004] In the prior art, the patents "Simulation method for microstructure evolution in heat-affected zone of welding process", "BGA tin-lead solder ball solidification process simulation method based on cellular automata", "Method for predicting coupling response of isothermal forming and dynamic recrystallization evolution of titanium alloy", and "Method for predicting austenite dynamic recrystallization microstructure evolution in hot deformation of strip steel" are too narrow in practicality and can only be applied to specific machining methods, and cannot predict the microstructure evolution of workpieces in the process of slow wire electro-discharge machining, let alone the microstructure evolution of workpieces in the process of slow wire electro-discharge machining assisted by ultrasonic magnetic field.

[0005] When solving the problems of uneven surface microstructure, coarse grains and poor surface quality of SMA materials, and the problems of excessive heat effect in the process of traditional electro-discharge machining, resulting in rough surface metamorphic layer, and the machining precision and efficiency difficult to meet the demand of high-performance materials, the existing technology may have the following technical defects:

[0006] 1. Single technical path: Most of the existing technology focuses on a single factor such as temperature, stress, grain growth, etc., and fails to consider the interactive effects of multiple factors, especially the combined effect of ultrasonic and magnetic field.

[0007] 2. Lack of precision: The existing simulation method may not be accurate enough in predicting the microstructure and performance changes of materials under specific machining conditions, especially in complex machining environments such as magnetic field assisted WEDM, it may be difficult to capture all relevant physical and chemical phenomena.

[0008] 3. Model simplification: To facilitate calculations, some models may oversimplify the actual situation, ignoring some key factors such as material non-uniformity, instability during processing, etc., which may cause deviations between simulation results and actual processing results.

[0009] 4. Limitations of applicability: Some models and methods may perform well under certain materials or specific processing conditions, but may fail under other conditions. SUMMARY

[0010] The purpose of the present application is to solve the above problems, and a kind of ultrasonic magnetic field assisted slow wire electro discharge wire cutting microstructure simulation method is proposed.The visualization of workpiece surface modified layer organization evolution in slow wire electro discharge machining process is realized, which is for the prediction or regulation of surface modified layer organization and theoretical research.

[0011] An ultrasonic magnetic field assisted slow wire electro discharge wire cutting microstructure simulation method, characterized in that, in the process of slow wire electro discharge wire cutting, the surface microstructure of the material during cutting is improved by introducing ultrasonic waves and external magnetic field, a thermal-mechanical coupling model is established, combined with finite element analysis and cellular automata model, the distribution of temperature field, stress field and strain field during processing is accurately simulated, and the dynamic evolution of grain in surface modified layer during cutting is simulated by cellular automata, to obtain visualized microstructure evolution simulation results.

[0012] Further, the following steps are included:

[0013] S1: generate parent phase initial grain;

[0014] S2: use finite element analysis to simulate the mechanical response and heat conduction process of the material during ultrasonic magnetic field assisted processing, and obtain the loading condition;

[0015] S3: update the dislocation density state of the material at the current time step;

[0016] S4: update the strain and dislocation density of the material at the current time step;

[0017] S5: judge whether the nucleation probability P is greater than the dislocation density ρ c When the nucleation probability P is greater than the dislocation density ρ c , dynamic recrystallization process is performed; when the nucleation probability P is less than the dislocation density ρ c , return to S3;

[0018] S6: update grain parameters and recrystallized grain growth;

[0019] S7: Calculate the current step number in each time step, check whether all grains have passed the judgment of S5, when all grains have passed the judgment of S5 and recrystallization nucleation occurs, enter S8; when there is a grain that has not passed the judgment of S5, the time step number is increased by one and returns to S4;

[0020] S8: Update the dislocation density state of all grains to obtain the visual microstructure evolution simulation result.

[0021] Further, S1 specifically comprises the following steps:

[0022] S11: Determine a three-dimensional simulation region, select the geometric shape and size of the three-dimensional simulation region, and discretize the three-dimensional simulation region into a grid composed of cells;

[0023] S12: Randomly select N cells in the three-dimensional simulation region as initial crystal nuclei, and assign different grain numbers and grain orientation variables to the selected cells;

[0024] S13: Calculate all cells one by one;

[0025] S14: Repeat S13 until all cells in the three-dimensional simulation region are assigned a grain number and the cell state variable is assigned an initial value.

[0026] Further, S2 is specifically: a three-dimensional Gaussian thermal source model is used for simulation and finite element simulation to obtain the temperature, stress and strain field parameters of the material in the processing process.

[0027] Further, the three-dimensional Gaussian thermal source model is obtained by substituting the vibration model of the electrode wire and the rewritten form of the current analytical solution into the Gaussian function of the heat flux density.

[0028] Further, in S3, the change model of the dislocation density inside the material is represented as:

[0029]

[0030] Where, ρ j is the dislocation density of the cell at time step j; k1 is the work hardening coefficient; k2 is the dynamic softening coefficient; Δε is the strain value increased by each time step.

[0031] Further, in S4, the dislocation density and strain calculation formula of the material is represented as:

[0032]

[0033] Where, K is a constant related to the material, which is 10; μ is the shear modulus; b is the Burgers vector; γ ij is the grain boundary energy between grains; M is the grain boundary mobility.

[0034] Compared with the prior art, the beneficial effects of the present application mainly include:

[0035] 1. Surface microstructure optimization: By introducing the combined effect of ultrasonic and magnetic fields, the surface microstructure of materials such as shape memory alloys is significantly improved, the grain size is reduced, and the uniformity of the grains is improved. The evolution of the surface modification layer is more controllable, which can effectively reduce the surface roughness and defects during processing, and improve the comprehensive mechanical properties of the material.

[0036] 2. Advantage of multi-physical field coupling: The technical solution of the present application combines the effects of thermal-mechanical coupling, ultrasonic and magnetic fields, etc., which can comprehensively consider the influence of different factors on the microstructure of the material, and break the limitation of single factor in the prior art, so as to realize more efficient processing optimization under complex process conditions.

[0037] 3. Fill in the technical gap: At present, there is no research on the simulation of the evolution of the surface modification layer in the slow wire electrochemical spark wire cutting process. The present application fills this technical gap and provides a new processing process optimization scheme, which provides a new idea and method for the precision machining of shape memory alloys and other high-performance materials. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is the general flowchart of the ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting microstructure simulation method of the present application;

[0039] Figure 2 (a) and (b) are respectively the von Neumann neighbor type diagram and the growth rule diagram of the ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting microstructure simulation method of the present application;

[0040] Figure 3 is the finite element simulation result diagram of the nickel-titanium alloy processing process of the ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting microstructure simulation method of the present application, wherein (a), (b), (c) and (d) are respectively the schematic diagrams of the temperature, stress, strain and temperature, stress, strain and strain rate curves with time under the ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting processing;

[0041] Figure 4 , (a) is the evolution simulation and experimental result diagram of the ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting microstructure simulation method of the present application, and (b) is the comparison diagram between the average grain size prediction value and the measured value of the microstructure simulation method for cutting process provided by the embodiment of the present application. DETAILED DESCRIPTION

[0042] A method for simulating microstructure of ultrasonic magnetic field assisted slow wire electro-discharge wire cutting will be described in more detail below with reference to the accompanying schematic drawings, in which a preferred embodiment of the present application is illustrated. It should be understood that those skilled in the art can modify the present application described herein while still achieving the advantageous effects of the present application, therefore, the following description should be understood as a broad knowledge for those skilled in the art, and not as a limitation of the present application.

[0043] A method for simulating microstructure of ultrasonic magnetic field assisted slow wire electro-discharge wire cutting, as shown in the figure, the method comprises the following steps: Figure 1

[0044] Step 1: Start.

[0045] Step 2: Generate initial grains of parent phase.

[0046] Step 2.1, determine the three-dimensional simulation region, and discretize the simulation region into a grid composed of cells: first, select the geometric shape and size of the simulation region. In this embodiment, the simulation region is a three-dimensional cubic region. Discretize the region into a number of cells, each cell representing a small region in the material, the size of which is set according to the scale of the specific material. Each cell will be assigned a grain number, grain orientation and other information to describe the microstructure of the grain.

[0047] Step 2.2, randomly select N cells in the three-dimensional simulation region as initial crystal nucleus, and assign different grain numbers and grain orientations to the selected cells, and set the grain boundary migration distance variable to 1 cell length: randomly select N cells in the simulation region as crystal nucleus, and assign a unique grain number and grain orientation variable to each crystal nucleus. In addition, the grain boundary migration distance variable of these cells is initialized to 1 cell length, which is used to control the starting condition of grain growth.

[0048] Step 2.3, calculate all cells one by one.

[0049] Step 2.4, repeat step 2.3 until all cells in the simulation region are assigned a grain number and the cell state variable is assigned an initial value.

[0050] Step 3: Read loading conditions from finite element processing.

[0051] Read the loading conditions obtained from the finite element analysis. These loading conditions include temperature, stress and strain data, which are obtained by simulating the mechanical response and heat conduction process of the material during the ultrasonic magnetic field assisted slow wire electro-discharge machining process. Accurate reading of loading conditions is a prerequisite for ensuring the accuracy of subsequent simulation.

[0052] Step 4: Update dislocation density state. ​

[0053] The evolution of dislocation density has a significant impact on grain growth, dynamic recrystallization, and mechanical properties of materials during processing. In this step, the dislocation density state of the material at the current time step is updated. The update of dislocation density is related to the degree of deformation, temperature field, and stress field of the material during processing. With each loading, the dislocation density changes, affecting the recrystallization process of the grains.

[0054] Step 5: Increase the time step number by one.

[0055] The calculation of each simulation is based on time steps. The step determines the accuracy of the simulation and the efficiency of the calculation. In this step, the time step number is increased, entering the next calculation phase. Each time step represents a small time period of the material during processing, and all calculations are completed within this small time period.

[0056] Step 6: Update the strain ε and dislocation density ρ at the current time step c .

[0057] At each time step, the strain ε and dislocation density ρ at the current time step are updated according to the loading conditions c . The strain ε reflects the degree of deformation of the material during processing, while the dislocation density ρ c represents the number of dislocations in the material. Strain and dislocation density are key factors affecting recrystallization and grain evolution, so they must be accurately calculated and updated.

[0058] Step 7: Determine whether the nucleation probability P is greater than the dislocation density ρ c . If the nucleation probability P is greater than the dislocation density ρ c , dynamic recrystallization nucleation occurs, otherwise return to step 4 to update the dislocation density state.

[0059] If the result of the judgment is that the nucleation probability P is greater than the dislocation density ρ c , the dynamic recrystallization nucleation process is executed. At this time, a new grain will start to grow at this location, and the growth of the grain will affect the evolution of the surrounding grains, gradually changing the microstructure of the material. If the nucleation probability P is less than the dislocation density ρ c , it indicates that dynamic recrystallization does not occur at this location in the current time step, and the system will return to step 4 to continue updating the dislocation density state.

[0060] Step 8: Update the grain parameters.

[0061] After dynamic recrystallization nucleation occurs, the relevant parameters of the newly generated grain are updated, including grain number, grain size, grain orientation, etc. The evolution of the grain is closely related to the time step, and the growth speed of the grain and the migration of the grain boundary are affected by factors such as dislocation density and strain.

[0062] Step 9: Recrystallization grain growth.

[0063] As the time step increases, the nucleated grains begin to grow. The grain growth process is carried out by updating the growth rate of the grains. Under the assistance of ultrasonic and magnetic fields, the growth rate of the grains is affected by the external fields. During the growth of the grains, the grain boundary migration and dislocation redistribution will further affect the morphology and size of the grains.

[0064] Step 10: Calculate the current step number and determine whether all grains have been completed by step 7.

[0065] In each time step, the current step number is calculated and all grains are checked to see if they have been completed by step 7. If all grains have completed the nucleation judgment and dynamic recrystallization nucleation has occurred, proceed to the next step; if there are grains that have not completed the judgment, continue to update the subsequent time steps.

[0066] Step 11: If all grains have been completed, output the simulation results, otherwise increase the time step and return to step 5.

[0067] If all grains have completed the judgment and dynamic recrystallization nucleation has occurred, output the simulation results, including the grain distribution, grain boundary position, grain size and other microstructure characteristics at each time step. If there are still grains that have not completed the dynamic recrystallization process, increase the time step, return to step 5 for time step update, and continue the simulation until all grains have been completed.

[0068] Step 12: End.

[0069] In order to improve the efficiency and accuracy of the simulation, the simplified conditions and model initialization are first performed; the model required for ultrasonic and magnetic field assisted slow wire electrochemical spark wire cutting is established according to the simplified conditions, mainly including Gaussian heat source, heat flow coupling and magnetic fluid coupling model; the ultrasonic and magnetic field assisted slow wire electrochemical spark wire cutting molten pool model is established, the formation of the molten zone and surface modification layer under the action of ultrasonic and magnetic field assisted slow wire electrochemical spark wire cutting is simulated, and the influence of ultrasonic, magnetic field and electrolysis on the surface modification layer of the workpiece is obtained; the temperature field, thermal stress and thermal strain under different process parameters are coupled with the recrystallization model; the nucleation, dislocation and grain growth cellular automata model of recrystallization evolution is established; the cellular automata model is simulated and calculated, and the microstructure evolution simulation results of ultrasonic and magnetic field assisted slow wire electrochemical spark wire cutting are obtained.

[0070] In summary, first, the temperature, stress and other data of the modification layer under the assistance of multiple fields are established according to the finite element method, and then the cellular automata model is constructed using these data, so that the microstructure evolution process of the surface modification layer of the workpiece can be simulated more accurately.

[0071] It should be noted that the application scenarios of the above microstructure simulation method include but are not limited to numerical simulation of microstructure evolution of shape memory alloy.

[0072] The specific embodiment of the present application is described below with the nickel-titanium alloy as the research object and in combination with the drawings.

[0073] According to Figure 1 , the following steps are implemented:

[0074] Step two, generating initial grains of parent phase, including the following sub-steps:

[0075] A 300*300*300 cubic grid representing the microstructure of the material is constructed using a three-dimensional model, and each grid represents a microcell of the material with a size of 1 μm. The grain orientation is randomly distributed between 1 and 181 to simulate the crystal orientation heterogeneity of the real material. The periodic boundary condition is used to simulate the infinite space. During the simulation, the time is discretized into time steps (CAS), and each CAS represents a comprehensive update of the state of all cells in the model;

[0076] Figure 2 The schematic diagram of (a) Von Neumann neighbor type and (b) growth rule of the microstructure simulation method for ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting of the present application is shown in FIGS. Figure 3 In order to more accurately predict the crystal growth behavior, the model considers the influence of the state of adjacent cells on the transition of the core cell. This is achieved by setting different priority growth rules, and the curvature-driven rule (a) has the highest priority, while the curvature-driven rule (c) has the lowest priority. If any four cells in rule (a) share the same state N, the state of the core cell will be converted to N;

[0077] The following calculations are performed for each cell one by one; and until all cells in the simulation area are assigned a grain number and the initial value of the cell state variable is assigned.

[0078] Step three, reading loading conditions from finite element of machining process

[0079] Figure 3 The finite element simulation result diagram of nickel-titanium alloy for the microstructure simulation method for ultrasonic magnetic field assisted slow wire electrochemical spark wire cutting of the present application is shown in FIGS.

[0080] A three-dimensional Gaussian heat source model is used for simulation, and the temperature, stress and strain fields of the workpiece during the machining process are obtained. The Gaussian function of the heat flux density is:

[0081]

[0082] where Q(r,t) is the heat flux density at the discharge channel r from the axis at time t, U is the processing voltage, I is the processing current, Ip is the peak current, and Ton is the pulse width.

[0083] US will cause the lateral vibration of the wire electrode. Assuming that the current flow direction is the positive direction of the x-axis and the electrode wire vibration direction is the y-axis direction. The discharge point position distribution is set as a random function S(t), and the vibration model is

[0084] y = A us sin(2pft)-S(t) (2)

[0085] where Aus is the amplitude, f is the frequency, and t is the vibration time.

[0086] The electric field strength under the coupling effect of US-MF-electrolysis can be expressed as

[0087]

[0088] Under the action of MF, the electron trajectory will be deflected. According to Newton's second law, the velocity vector v and the acceleration vector a are both decomposed into x and y direction scalars, and the displacement equation of the electron is obtained:

[0089]

[0090] where m is the mass of the electron, q is the charge of the electron, and B is the magnetic field strength. The length of the electron trajectory L is

[0091]

[0092] Under the coupling effect of US-MF-electrolysis, the number of electrons and the internal electron current density increase, ultimately leading to an increase in discharge current. Therefore, the current analytical solution can be rewritten as

[0093]

[0094] where i0 is the initial current density, a is the number of ion collisions per unit time, q' is the number of electrons generated after ion collision, and S is the cross-sectional area. Substituting equations (2) and (6) into equation (1), the heat source model of US-MF-assisted WEDCM-LS can be obtained.

[0095] Comsol software is used for finite element simulation. A 1 / 2 symmetric entity with a size of 200 pm x 70 pm x 50 pm is constructed, and Ip is set to 8, Ton is set to 12, and U is set to 50 V. The O point is 20 pm away from the processing surface. Pulsed discharge is performed on the workpiece surface, and the dynamic temperature and strain field changes of the O point are obtained, as shown inFigure 3 The maximum stress and strain of the simulation region center are σ = 1610 Mpa and ε = 0.407, respectively, and the maximum temperature value T max = 1250.5℃, the dynamic recrystallization (DRX) starting temperature T s = 800℃, and the corresponding strain ε and strain rate are 0.054 and 0.026 s -1 , respectively, indicating the beginning of grain refinement. The termination temperature T f = 600, and the corresponding strain is 0.061, indicating the completion of DRX.

[0096] Step four, update the dislocation density state

[0097] The model of the change of the dislocation density inside the material is

[0098]

[0099] wherein ρ j represents the dislocation density of the cell at the j time step; k1 is the work hardening coefficient; k2 is the dynamic softening coefficient; and Δε represents the strain value increased by each CAS.

[0100] Step five, increase the time step number by one

[0101] Step six, update the strain ε and the dislocation density ρ c

[0102] The increase of the dislocation density will trigger the DRX grain nucleation at the GB until the critical dislocation density is reached. The formula for calculating the critical dislocation density is:

[0103]

[0104] wherein K is a constant related to the material and is 10; μ is the shear modulus; b is the Burgers vector; and γ ij is the grain boundary energy between grains.

[0105] Step seven, determine whether the nucleation probability ρ c is greater than the dislocation density ρ c . If yes, proceed with the dynamic recrystallization nucleation, otherwise return to step 4 to update the dislocation density state

[0106] Step eight, update the grain parameters; including the grain number, the grain size, the grain orientation, etc.

[0107] Step nine, recrystallized grain growth; during the growth of the grain, the grain boundary migration and the redistribution of dislocations will further affect the morphology and size of the grain.

[0108] Step 10: Calculate the current number of steps and determine whether all grains have been completed as determined in step 7.

[0109] Step 11: If all grains are judged to be complete, output the simulation results; otherwise, increase the time step number and return to step 5.

[0110] Figure 4 (a) is a schematic diagram of the evolution simulation and experimental results of the ultrasonic magnetic field assisted slow-moving electrolytic electric spark wire cutting microstructure simulation method of the present invention; the visualization of the microstructure evolution process of a specified area during the ultrasonic magnetic field assisted slow-moving electrolytic electric spark wire cutting process is achieved; the above method is used to simulate the surface area during the ultrasonic magnetic field assisted slow-moving electrolytic electric spark wire cutting process, Figure 4 (b) is a comparison diagram between the average grain size prediction value and the measured value of a microstructure simulation method for cutting process provided by an embodiment of the present invention, as shown in FIG. Figure 4 As shown in (b), by comparing the microstructure prediction results with the experimental results, it can be seen that the present invention can more accurately simulate the microstructure evolution behavior of the titanium alloy cutting process.

[0111] The above description is merely a preferred embodiment of the present invention and does not limit the present invention in any way. Any person skilled in the art who, without departing from the scope of the present invention, makes any equivalent substitution, modification, or other changes to the technical solution and technical content disclosed in the present invention shall be deemed to be within the scope of the present invention and still fall within the scope of protection of the present invention.

Claims

1. A method for simulating microstructure of wire-cut electric discharge cutting assisted by ultrasonic magnetic field, characterized in that: During the wire-cut electrical discharge machining process, ultrasonic waves and an external magnetic field are introduced to improve the surface microstructure of the material during cutting. By establishing a thermomechanical coupling model and combining finite element analysis with a cellular automaton model, the distribution of temperature, stress, and strain fields during the machining process is accurately simulated. At the same time, the dynamic evolution of grains in the metamorphic layer on the material cutting surface is simulated by cellular automata, and visual simulation results of the microstructure evolution are obtained.

2. The ultrasonic magnetic field assisted slow-moving wire electric discharge cutting microstructure simulation method according to claim 1 is characterized in that: The following steps are involved: S1: Generate initial grains of parent phase; S2: Finite element analysis was used to simulate the mechanical response and heat conduction process of the material during ultrasonic magnetic field assisted machining to obtain the loading conditions; S3: Update the dislocation density state of the material in the current time step; S4: Update the strain and dislocation density of the material at the current time step; S5: Determine whether the nucleation probability P is greater than the dislocation density ρ c , when the nucleation probability P is greater than the dislocation density ρ c When the nucleation probability P is less than the dislocation density ρ c When , it returns to S3; S6: Update grain parameters and recrystallize grain growth; S7: Calculate the current step number in each time step and check whether all grains have passed the judgment of S5. When all grains have passed the nucleation judgment and recrystallization nucleation has occurred, enter S8; when there are grains that have not passed the judgment of S5, increase the time step number by one and return to S4; S8: Update the dislocation density state of all grains to obtain visualized microstructure evolution simulation results.

3. The ultrasonic magnetic field assisted slow-moving wire electric discharge cutting microstructure simulation method according to claim 2, characterized in that: The S1 specifically includes the following steps: S11: determining a three-dimensional simulation region, selecting a geometric shape and size of the three-dimensional simulation region, and discretizing the three-dimensional simulation region into a grid composed of cells; S12: Randomly select N cells in the three-dimensional simulation area as initial crystal nuclei, and assign different grain numbers and grain orientation variables to the selected cells; S13: Calculate all cells one by one; S14: Repeat S13 until all cells in the three-dimensional simulation area are assigned grain numbers and initial values ​​are assigned to the cell state variables.

4. The ultrasonic magnetic field assisted slow-moving wire electric discharge cutting microstructure simulation method according to claim 2, characterized in that: The S2 specifically includes: using a three-dimensional Gaussian thermoelectric model to simulate and perform finite element simulation to obtain the temperature, stress and strain field parameters of the material during the processing.

5. The ultrasonic magnetic field assisted slow-moving wire electric discharge cutting microstructure simulation method according to claim 4, characterized in that: The three-dimensional Gaussian thermogenic model is obtained by substituting the vibration model of the electrode wire and the rewritten formula of the current analytical solution into the Gaussian function of the heat flux density.

6. The ultrasonic magnetic field assisted slow-moving wire electric discharge cutting microstructure simulation method according to claim 2, characterized in that: In S3, the variation model of the dislocation density inside the material is expressed as: Among them, ρ j is the dislocation density of the cell at time step j; k1 is the work hardening coefficient; k2 is the dynamic softening coefficient; Δε is the strain value increased in each time step.

7. The ultrasonic magnetic field assisted wire-cut electric discharge cutting microstructure simulation method according to claim 2, characterized in that: In S4, the dislocation density and strain calculation formula of the material are expressed as: Where K is a constant related to the material, which is set to 10; μ is the shear modulus; b is the Burgers vector; and γ is ij The grain boundary energy between grains; M is the grain boundary mobility.

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