A method and system for predicting recrystallized grain size during cutting process

Abstract

The invention proposes a method and system for predicting recrystallized grain size in a cutting process, and relates to the field of cutting technology. The specific scheme comprises: constructing a dynamic recrystallization critical strain; correcting the Johnson-Cook constitutive equation by means of a constructed recrystallization softening effect function; constructing a recrystallization grain prediction model based on the corrected Johnson-Cook constitutive equation; predicting the final dynamic recrystallization grain size by means of the constructed recrystallization grain prediction model; based on the corrected Johnson-Cook constitutive equation and the recrystallization grain size prediction model, the invention predicts the dynamic recrystallization grain size under the synergistic effect of multi-scale deformation in the alloy cutting process; wherein the Johnson-Cook constitutive equation is corrected by adding a recrystallization softening effect function, the recrystallization grain size prediction model adopts a recrystallization kinetics correction equation JMAK, the recrystallization critical condition does not adopt a single recrystallization temperature, but is related to the strain rate, cutting temperature, etc., thereby improving the prediction accuracy of machining stress, strain rate, cutting temperature and surface grain size in the cutting process.

Description

Technical Field

[0001] The invention belongs to the field of cutting technology, and in particular relates to a method and system for predicting recrystallized grain size in a cutting process. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] During the cutting process, the material undergoes a severe plastic deformation process with high strain rate. The shear, extrusion and friction between the tool and the workpiece in the cutting deformation zone provide the driving force for rapid dynamic recrystallization nucleation, inducing the dynamic recrystallization behavior of the processed material, leading to changes in the microstructure of the material, and further causing changes in the macroscopic mechanical behavior of the material. The dynamic recrystallization behavior of the material under the above-mentioned complex macro and micro deformation plays a key role in determining the surface quality of the alloy processing. How to more effectively reveal the dynamic recrystallization mechanism under the synergistic effect of multi-scale deformation in the alloy cutting process has always been a key scientific issue that has attracted much attention.

[0004] The establishment of finite element simulation model plays a key role in revealing the recrystallization mechanism of cutting process. However, the existing methods have the following problems:

[0005] (1) The simulation model generally uses the empirical constitutive formula (Johnson-Cook formula) to describe the stress-strain response relationship of the material, treating strain, strain rate and cutting temperature as independent factors without considering the coupling relationship between the three.

[0006] (2) The original Johnson-Cook model does not consider the influence of recrystallization softening effect on stress changes during cutting, nor does it consider the synergistic effect of macro- and micro-deformation during material cutting, which greatly reduces the prediction accuracy and practicality of the Johnson-Cook formula.

[0007] (3) The critical condition for recrystallization is generally determined by the critical temperature. However, since the cutting process is a transient high strain rate deformation process, the critical temperature as a dynamic recrystallization condition is still controversial.

[0008] Therefore, in-depth and detailed research is still needed on the prediction model of the dynamic recrystallization mechanism of the cutting process. It is urgent to establish a recrystallized grain size prediction model based on the dynamic recrystallization behavior characteristics of the powder high-temperature alloy cutting process, couple the synergistic effects of macro and micro deformation, and provide basic theoretical and technical support for efficient and high-quality processing and manufacturing. Summary of the invention

[0009] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method and system for predicting the recrystallized grain size in a cutting process, which predicts the dynamic recrystallized grain size under the synergistic effect of multi-scale deformation in the alloy cutting process based on the modified Johnson-Cook constitutive equation and the recrystallized grain size prediction model; wherein the Johnson-Cook constitutive equation is modified by adding a function of the recrystallization softening effect The recrystallization grain size prediction model adopts the recrystallization kinetics correction equation JMAK. The critical condition of recrystallization does not only use the recrystallization temperature, but is related to the strain rate, cutting temperature, etc., which improves the prediction accuracy of machining stress, strain rate, cutting temperature and surface grain size in the cutting process.

[0010] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0011] A first aspect of the present invention provides a method for predicting recrystallized grain size during a cutting process;

[0012] A method for predicting recrystallized grain size during a cutting process, comprising:

[0013] Based on the strain rate and cutting temperature, the critical strain of dynamic recrystallization is constructed;

[0014] According to the dynamic recrystallization critical strain, the recrystallization softening effect function is constructed and added to the Johnson-Cook constitutive equation to obtain the modified Johnson-Cook constitutive equation.

[0015] Based on the modified Johnson-Cook constitutive equation, the strain, strain rate, cutting temperature, dynamic recrystallization volume fraction and dynamic recrystallization grain size under different cutting conditions are obtained, and a recrystallization grain prediction model is constructed;

[0016] The strain, strain rate, and cutting temperature under the cutting condition to be predicted are obtained, and the final dynamic recrystallization grain size is predicted through the constructed recrystallized grain prediction model.

[0017] Furthermore, the dynamic recrystallization critical strain is specifically:

[0018]

[0019] Among them, ε c is the critical strain of dynamic recrystallization, a1, d0, m1, Q1, c1 are material coefficients, T is the cutting temperature, R is the ideal gas constant, is the strain rate.

[0020] Furthermore, the recrystallization softening effect function is specifically:

[0021]

[0022]

[0023]

[0024] in, is the recrystallization softening effect function, ε c is the critical strain for dynamic recrystallization, is the strain rate, ε is the true strain; It is the criterion for judging whether recrystallization affects the constitutive relationship. To consider the recrystallization correction relationship, u0, u1, and u2 are recrystallization coefficients.

[0025] Furthermore, the modified Johnson-Cook constitutive equation is:

[0026]

[0027] Where σ is the true stress, A, B, C, n, m are material coefficients, T room is the reference temperature, T m is the melting point of the material, T is the cutting temperature, is the recrystallization softening effect function.

[0028] Furthermore, the coefficients in the modified Johnson-Cook constitutive equation are obtained through room temperature quasi-static compression tests and room temperature / high temperature SHPB dynamic impact tests.

[0029] Furthermore, the recrystallized grain prediction model is constructed as follows:

[0030] (1) Based on the modified Johnson-Cook constitutive equation, finite element simulation and experiments were performed on right-angle cutting to obtain the strain ε and strain rate under different cutting conditions. Cutting temperature T, dynamic recrystallization volume fraction X drex and the dynamically recrystallized grain size d drex ;

[0031] (2) The least squares method was used to construct a recrystallized grain prediction model through regression method.

[0032] Furthermore, the recrystallized grain prediction model is specifically:

[0033]

[0034] Among them, d drex and X drexis the dynamically recrystallized grain size and recrystallized volume fraction, d is the final predicted grain size, ε is the true strain, and ε 0.5 is the true strain when dynamic recrystallization reaches 50%, ε c is the real strain of dynamic recrystallization; a i 、h i 、m i 、n i , Q i , β d , K d , d0 are material coefficients, R is the ideal gas constant (8.314 J·mol -1 ·K -1 ).

[0035] A second aspect of the present invention provides a system for predicting recrystallized grain size during a cutting process.

[0036] A recrystallization grain size prediction system for a cutting process includes a critical strain building module, a constitutive equation building module, a prediction model building module and a grain size prediction module:

[0037] The critical strain building module is configured to: build the dynamic recrystallization critical strain based on the strain rate and cutting temperature;

[0038] The constitutive equation building module is configured to: construct a recrystallization softening effect function according to the dynamic recrystallization critical strain, and add it to the Johnson-Cook constitutive equation to obtain a modified Johnson-Cook constitutive equation;

[0039] The prediction model building module is configured to: obtain the strain, strain rate, cutting temperature, dynamic recrystallization volume fraction and dynamic recrystallization grain size under different cutting conditions based on the modified Johnson-Cook constitutive equation, and build a recrystallization grain prediction model;

[0040] The grain size prediction module is configured to: obtain the strain, strain rate, and cutting temperature under the cutting condition to be predicted, and predict the final dynamic recrystallization grain size through the constructed recrystallization grain prediction model.

[0041] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a method for predicting recrystallized grain size during a cutting process as described in the first aspect of the present invention.

[0042] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method for predicting the recrystallized grain size during a cutting process as described in the first aspect of the present invention are implemented.

[0043] One or more of the above technical solutions have the following beneficial effects:

[0044] The present invention predicts the dynamic recrystallization grain size under the synergistic effect of multi-scale deformation in the alloy cutting process based on the modified Johnson-Cook constitutive equation and the recrystallization grain size prediction model; wherein, the Johnson-Cook constitutive equation is modified by adding the function of the recrystallization softening effect The recrystallization grain size prediction model adopts the recrystallization kinetics correction equation JMAK. The critical condition of recrystallization does not only use the recrystallization temperature, but is related to the strain rate, cutting temperature, etc., which improves the prediction accuracy of machining stress, strain rate, cutting temperature and surface grain size in the cutting process.

[0045] The present invention aims at the coordination effect of macro and micro deformation in the right-angle cutting process, establishes the constitutive equation of dynamic mechanical properties of materials considering the recrystallization softening effect, adopts perfect recrystallization critical conditions and recrystallization grain prediction model, and predicts the recrystallized grains on the right-angle cutting surface through ABAQUS simulation software. Cutting optimization is performed by analyzing the influence of cutting parameters on the size and distribution state of recrystallized grains.

[0046] The present invention considers the synergistic effect of macro and micro deformation in the simulation process for the first time, improves the prediction accuracy of machining stress, strain rate, cutting temperature and surface grain size in the cutting process, and provides a new idea for the study of cutting surface integrity.

[0047] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0049] Figure 1 This is a flow chart of the method of the first embodiment.

[0050] Figure 2 Figure 2. Experimental method for determining the coefficient values ​​in the Johnson-Cook constitutive equation for the first embodiment.

[0051] Figure 3 It is a system structure diagram of the second embodiment.

[0052] Figure 4 A method for obtaining the grain size distribution law. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] Embodiment 1

[0055] This embodiment discloses a method for predicting recrystallized grain size during cutting;

[0056] like Figure 1 As shown, a method for predicting recrystallized grain size during cutting process comprises:

[0057] Step S1: construct the dynamic recrystallization critical strain based on the strain rate and cutting temperature.

[0058] The critical condition for recrystallization is generally determined by the critical temperature, that is, when the cutting temperature is greater than 0.4T m Dynamic recrystallization occurs around T m is the melting point of the material; however, since the cutting process is a transient high strain rate deformation process, the critical temperature as a dynamic recrystallization condition is still controversial. Therefore, this embodiment proposes a dynamic recrystallization critical strain ε c , that is, the true strain is greater than the dynamic recrystallization critical strain ε c When dynamic recrystallization occurs, the critical strain of dynamic recrystallization is ε c The formula is:

[0059]

[0060] Among them, a1, d0, m1, Q1, c1 are material coefficients, T is the cutting temperature, is the strain rate, R is the ideal gas constant, usually 8.314 J·mol -1 ·K -1 The material was hot extruded to obtain the critical strain test results for recrystallization grain refinement, and the parameters were corrected and determined using an iterative procedure.

[0061] Step S2: According to the dynamic recrystallization critical strain, a recrystallization softening effect function is constructed and added to the Johnson-Cook constitutive equation to obtain the modified Johnson-Cook constitutive equation, which is:

[0062] Step S201: Constructing a recrystallization softening effect function

[0063] In this embodiment, the recrystallization softening effect function To characterize the influence of dynamic recrystallization flow softening and strain softening on stress changes during cutting, the formula is:

[0064]

[0065] Among them, εc is the critical strain for dynamic recrystallization, is the strain rate, ε is the true strain; It is the criterion for judging whether recrystallization affects the constitutive relationship. To consider the recrystallization correction relationship, u0, u1, and u2 are recrystallization coefficients.

[0066] When ε≤ε c When dynamic recrystallization does not occur, When ε≥ε c When dynamic recrystallization occurs, and but but That is, when dynamic recrystallization occurs, When dynamic recrystallization does not occur,

[0067] Step S202: Modify the Johnson-Cook constitutive equation

[0068] By transforming the recrystallization softening effect function Add it to the Johnson-Cook constitutive equation and make corrections, specifically:

[0069]

[0070] Where σ is the true stress, A, B, C, n, m are material coefficients, T room is the reference temperature, T m is the melting point of the material and T is the cutting temperature.

[0071] From formula (3), we can see that when That is, when dynamic recrystallization does not occur, the original Johnson-Cook formula is still used. The original model does not consider the influence of the recrystallization softening effect on the stress change during the cutting process. Only when dynamic recrystallization occurs, the influence of the recrystallization softening effect on the stress change during the cutting process is considered, thereby improving the prediction accuracy and practicality of the Johnson-Cook formula.

[0072] Step S203: Determine the coefficients in the modified Johnson-Cook constitutive equation

[0073] After steps S201 and S202, the modified Johnson-Cook constitutive equation is obtained, and the complete formula is:

[0074]

[0075] In formula (4), in addition to strain ε and strain rate There are also some coefficients for cutting temperature T, the values ​​of which are obtained through experiments, such as Figure 2 As shown, specifically:

[0076] The yield strength A, strain hardening rate B, and n were obtained through room temperature quasi-static compression tests. The strain hardening coefficient c, material softening coefficient m, and recrystallization coefficient h were confirmed by linear fitting method using the least squares method through room temperature / high temperature SHPB dynamic impact tests. i , the FGH96 constitutive equation of cutting processing can be obtained.

[0077] The obtained FGH96 constitutive equation for cutting was embedded in Abaqus through a subroutine to carry out finite element simulation of right-angle cutting. The reliability of the FGH96 constitutive equation for cutting was verified by comparing it with the chip morphology and cutting force obtained from the experiment.

[0078] Step S3: Based on the finite element simulation of the modified Johnson-Cook constitutive equation, the strain, strain rate, and cutting temperature under different cutting conditions are obtained, and the dynamic recrystallization volume fraction and dynamic recrystallization grain size of the hot compression experiment are used to construct a recrystallization grain prediction model, specifically:

[0079] Step S301: Based on the modified Johnson-Cook constitutive equation, right-angle cutting finite element simulation and experiment are performed to obtain the grain size distribution law, that is, the strain ε and strain rate under different cutting conditions. Cutting temperature T, dynamic recrystallization volume fraction X drex and the dynamically recrystallized grain size d drex .

[0080] The grain size distribution law is used to characterize the relationship between grain size d and strain ε and strain rate Cutting temperature T, dynamic recrystallization volume fraction X drex and the dynamically recrystallized grain size d drex The relationship between.

[0081] Dynamic recrystallization volume fraction X drex is through different cutting temperature T and strain rate The dynamic recovery and dynamic recrystallization flow stress under the condition of dynamic recovery and dynamic recrystallization, and the dynamic recrystallization grain size d drex It is obtained when dynamic recrystallization occurs at different strain rates and cutting temperatures.

[0082] Methods for obtaining grain size distribution laws, such as Figure 4 As shown, specifically:

[0083] (1) Set simulation parameters and start finite element simulation;

[0084] The FGH96 constitutive equation for cutting obtained in step S2 is embedded into Abaqus through a subroutine, and the initial grain size d0 and the total cutting time t are set. f After that, the finite element simulation of right-angle cutting was started.

[0085] (2) Read the working condition parameters at the time and calculate the simulated grain size under the working condition;

[0086] Read the strain ε and strain rate at the material point at time t Cutting temperature T, and the strain ε and strain rate read The cutting temperature T is in the form of a user-defined variable, and the simulated grain size under the current cutting condition is displayed in the form of a cloud diagram in the Abaqus software post-processing module.

[0087] (3) performing a right-angle cutting experiment under the working condition read in step (2) to obtain an experimental grain size under the working condition;

[0088] Under the same cutting condition as the finite element simulation of right-angle cutting at time t, that is, the same strain ε and strain rate At the cutting temperature T, a right-angle cutting experiment was carried out to obtain a right-angle cutting cross-section sample, and then the sample was prepared and polished by a focused ion beam (FIB). The recrystallized grain morphology was observed by SEM and TEM, and the grain structure in the metallographic image was statistically analyzed by Image-Pro Plus software. The dynamic recrystallization grain size d was quantitatively determined by the intercept method. drex , take 5 to 8 pictures at different areas of the sample center for measurement, and then perform linear fitting on the stress-strain curve of the material to obtain the dynamic recrystallization volume fraction X drex , based on the dynamic recrystallization volume fraction X drex and the dynamically recrystallized grain size d drex , calculate the average value of the dynamically recrystallized grain size, which is the experimental grain size under the current cutting conditions.

[0089] (4) Determine the final grain size;

[0090] Compare the simulated grain size with the experimental grain size. If they are inconsistent, adjust the dynamic recrystallization grain size d drex , repeat the simulation and experiment, if they are consistent, then we can get the strain ε and strain rate Cutting temperature T, dynamic recrystallization grain size d drex The final grain size d.

[0091] (5) Obtaining grain size distribution law;

[0092] According to the same method, the working parameters at different times are read to obtain the grain size d of the deformed sample under different deformation conditions, and the relationship between the grain size d and the strain ε and strain rate is obtained. Cutting temperature T, dynamic recrystallization volume fraction X drex and the dynamically recrystallized grain size d drex The relationship between them is the grain size distribution law.

[0093] Step S302: using the least square method and regression method to construct a recrystallized grain prediction model.

[0094] The constructed recrystallized grain prediction model is:

[0095]

[0096] Among them, d drex and X drex is the dynamically recrystallized grain size and recrystallized volume fraction, d0 is the initial grain size, d is the grain size, ε is the true strain, and ε 0.5 is the true strain when dynamic recrystallization reaches 50%, ε c is the real strain of dynamic recrystallization; a i 、h i 、m i 、n i , Q i , β d , K d , d0 are material coefficients, R is the ideal gas constant (8.314 J·mol -1 ·K -1 ).

[0097] According to the grain size distribution law obtained in step S301, that is, the strain ε and strain rate under different cutting conditions Cutting temperature T, dynamic recrystallization volume fraction X drex , Dynamic recrystallization grain size d drex The least square method was used to fit the experimental data through regression method to establish the material constant a in the recrystallization grain prediction model. i 、h i 、m i 、n i , Q i , β d , K d The value of is obtained, thus obtaining the recrystallized grain prediction model.

[0098] Step S4: Obtain the strain, strain rate, and cutting temperature under the cutting condition to be predicted, and predict the final dynamic recrystallization grain size through the constructed recrystallized grain prediction model.

[0099] Under the cutting condition to be predicted, perform finite element simulation of right-angle cutting to obtain the strain ε and strain rate under the cutting condition. The cutting temperature T is input into the constructed recrystallized grain prediction model for calculation, and the value of the grain size d is the predicted value of the dynamic recrystallization grain size.

[0100] Embodiment 2

[0101] This embodiment discloses a system for predicting recrystallized grain size during cutting;

[0102] like Figure 3 As shown, a recrystallization grain size prediction system for a cutting process includes a critical strain building module, a constitutive equation building module, a prediction model building module and a grain size prediction module:

[0103] The critical strain building module is configured to: build the dynamic recrystallization critical strain based on the strain rate and cutting temperature;

[0104] The constitutive equation building module is configured to: construct a recrystallization softening effect function according to the dynamic recrystallization critical strain, and add it to the Johnson-Cook constitutive equation to obtain a modified Johnson-Cook constitutive equation;

[0105] The prediction model building module is configured to: obtain the strain, strain rate, cutting temperature, dynamic recrystallization volume fraction and dynamic recrystallization grain size under different cutting conditions based on the modified Johnson-Cook constitutive equation, and build a recrystallization grain prediction model;

[0106] The grain size prediction module is configured to: obtain the strain, strain rate, and cutting temperature under the cutting condition to be predicted, and predict the final dynamic recrystallization grain size through the constructed recrystallization grain prediction model.

[0107] Embodiment 3

[0108] The purpose of this embodiment is to provide a computer-readable storage medium.

[0109] A computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps in a method for predicting recrystallized grain size during a cutting process as described in the first embodiment of the present disclosure.

[0110] Embodiment 4

[0111] The purpose of this embodiment is to provide an electronic device.

[0112] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a method for predicting recrystallized grain size during a cutting process as described in the first embodiment of the present disclosure are implemented.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting recrystallized grain size during cutting, characterized in that: include: Based on the strain rate and cutting temperature, the critical strain of dynamic recrystallization is constructed; According to the dynamic recrystallization critical strain, the recrystallization softening effect function is constructed and added to the Johnson-Cook constitutive equation to obtain the modified Johnson-Cook constitutive equation. Based on the modified Johnson-Cook constitutive equation, the strain, strain rate, cutting temperature, dynamic recrystallization volume fraction and dynamic recrystallization grain size under different cutting conditions are obtained, and a recrystallization grain prediction model is constructed; Obtain the strain, strain rate, and cutting temperature under the cutting condition to be predicted, and predict the final dynamic recrystallization grain size through the constructed recrystallization grain prediction model; Wherein, the dynamic recrystallization critical strain is specifically: in, is the critical strain of dynamic recrystallization, a1, d0, m1, Q1, c1 are material coefficients, T is the cutting temperature, R is the ideal gas constant, is the strain rate; The recrystallization softening effect function is specifically: in, is the recrystallization softening effect function, is the critical strain for dynamic recrystallization, is the strain rate, For real strain; It is the criterion for judging whether recrystallization affects the constitutive relationship. To consider the recrystallization correction relationship, u0, u1, and u2 are recrystallization coefficients; The modified Johnson-Cook constitutive equation is: Where σ is the true stress, A, B, C, n, m are material coefficients, T room is the reference temperature, T m is the melting point of the material, T is the cutting temperature, is the recrystallization softening effect function.

2. A method for predicting recrystallized grain size during cutting as claimed in claim 1, characterized in that: The coefficients in the modified Johnson-Cook constitutive equation are obtained through room temperature quasi-static compression tests and room temperature / high temperature SHPB dynamic impact tests.

3. A method for predicting recrystallized grain size during cutting process according to claim 1, characterized in that: The construction of the recrystallized grain prediction model is specifically as follows: (1) Based on the modified Johnson-Cook constitutive equation, finite element simulation and experiments were performed on right-angle cutting to obtain the strain under different cutting conditions. , strain rate , cutting temperature T, dynamic recrystallization volume fraction X drex and the dynamically recrystallized grain size d drex ; (2) The least squares method was used to construct a recrystallized grain prediction model through regression method.

4. A method for predicting recrystallized grain size during cutting as claimed in claim 1, characterized in that: The recrystallized grain prediction model is specifically: Among them, d drex and X drex is the dynamically recrystallized grain size and recrystallized volume fraction, d0 is the initial grain size, d is the final predicted grain size, ε is the true strain, and ε 0.5 is the true strain when dynamic recrystallization reaches 50%, ε c is the real strain of dynamic recrystallization; a i 、h i 、m i 、n i , Q i , β d , K d , d0 are material coefficients, R is the ideal gas constant (8.314 J•mol -1 •K -1 ).

5. A system for predicting recrystallized grain size during cutting, characterized in that: It includes critical strain building module, constitutive equation building module, prediction model building module and grain size prediction module: The critical strain building module is configured to: build the dynamic recrystallization critical strain based on the strain rate and cutting temperature; The constitutive equation building module is configured to: construct a recrystallization softening effect function according to the dynamic recrystallization critical strain, and add it to the Johnson-Cook constitutive equation to obtain a modified Johnson-Cook constitutive equation; The prediction model building module is configured to: obtain the strain, strain rate, cutting temperature, dynamic recrystallization volume fraction and dynamic recrystallization grain size under different cutting conditions based on the modified Johnson-Cook constitutive equation, and build a recrystallization grain prediction model; The grain size prediction module is configured to: obtain the strain, strain rate, and cutting temperature under the cutting condition to be predicted, and predict the final dynamic recrystallization grain size through the constructed recrystallization grain prediction model; Wherein, the dynamic recrystallization critical strain is specifically: in, is the critical strain of dynamic recrystallization, a1, d0, m1, Q1, c1 are material coefficients, T is the cutting temperature, R is the ideal gas constant, is the strain rate; The recrystallization softening effect function is specifically: in, is the recrystallization softening effect function, is the critical strain for dynamic recrystallization, is the strain rate, For real strain; It is the criterion for judging whether recrystallization affects the constitutive relationship. To consider the recrystallization correction relationship, u0, u1, and u2 are recrystallization coefficients; The modified Johnson-Cook constitutive equation is: Where σ is the true stress, A, B, C, n, m are material coefficients, T room is the reference temperature, T m is the melting point of the material, T is the cutting temperature, is the recrystallization softening effect function.

6. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for predicting recrystallized grain size during a cutting process as described in any one of claims 1 to 4 are implemented.

7. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for predicting the recrystallized grain size in a cutting process as described in any one of claims 1 to 4 are implemented.

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