A method for predicting the dynamic recrystallization law of a heat-resistant steel forging process

Abstract

The embodiment of the application relates to a prediction method for a grain dynamic recrystallization law of a heat-resistant steel forging process, and belongs to the technical field of finite element simulation forging processes. The prediction method comprises the following steps: S1, performing hot compression tests on heat-resistant steel samples at different deformation temperatures and different strain rates to obtain stress-strain data and grain size data of the heat-resistant steel samples at different deformation temperatures and different strain rates; S2, fitting a dynamic recrystallization model of the heat-resistant steel material according to the stress-strain data of the heat-resistant steel sample; S3, establishing a material file of the heat-resistant steel material according to the dynamic recrystallization model; S4, establishing a finite element simulation model of the heat-resistant steel forging according to an actual heat-resistant steel forging, inputting the material file into the finite element simulation model, and setting simulation parameters according to an actual forging process of the heat-resistant steel forging to simulate the forging forming process; and S5, extracting a dynamic recrystallization volume fraction cloud chart and a grain size cloud chart of a cross section of the heat-resistant steel forging.

Description

Technical Field

[0001] This invention relates to the field of finite element simulation forging technology, and in particular to a method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings. Background Technology

[0002] Ultra-supercritical power generator sets have advantages such as high thermal efficiency and low energy consumption, and have been widely studied in thermal power generator set technology. In ultra-supercritical power generator sets, high-temperature pressure-bearing components are mainly made of heat-resistant steel, and the quality of the heat-resistant steel forgings directly affects the performance of these components.

[0003] However, the grain size of heat-resistant steel is difficult to control during hot working, which has always been a challenge in the forging trial production process. The quality of forgings largely depends on the grain size, which in turn depends on the dynamic recrystallization process. Therefore, accurately predicting and controlling the dynamic recrystallization law of grains is of great significance for improving the comprehensive mechanical properties of forgings.

[0004] With the advancement of computer technology and numerical simulation methods, prediction methods for dynamic recrystallization have also developed rapidly. These methods mainly include physical model-based prediction and experimental research methods. Prediction methods based on the Sellars-Tegart model and Avrami equations primarily predict the dynamic recrystallization process by establishing a kinetic curve of recrystallization using a mathematical model. Experimental research methods mainly obtain key data such as rheological stress curves and microstructure evolution images of materials through hot deformation tests under different conditions, thereby establishing a mathematical model of dynamic recrystallization. However, neither of these methods can intuitively reflect the dynamic recrystallization laws at various locations under actual forging conditions and cannot directly provide a reliable reference for the formulation of forging processes for large forgings. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to provide a method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings, in order to solve the problem that existing methods cannot intuitively reflect the dynamic recrystallization law of each position under actual forging conditions of forgings.

[0006] Embodiments of the present invention provide a method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings, comprising the following steps:

[0007] S1, hot compression tests were conducted on heat-resistant steel samples at different deformation temperatures and strain rates to obtain stress-strain data and grain size data of the heat-resistant steel samples at different deformation temperatures and strain rates and after the hot compression test.

[0008] S2, Based on the stress-strain data of the heat-resistant steel sample, a dynamic recrystallization model of the heat-resistant steel material is obtained by fitting.

[0009] S3, Based on the dynamic recrystallization model of heat-resistant steel, establish the material file for heat-resistant steel;

[0010] S4. Establish a finite element simulation model of the heat-resistant steel forging based on the actual heat-resistant steel forging, input the material file into the finite element simulation model, and set the simulation parameters according to the actual forging process of the heat-resistant steel forging to simulate the forging process of the heat-resistant steel forging.

[0011] S5, extract the dynamic recrystallization grain simulation data of the heat-resistant steel forging. The dynamic recrystallization grain simulation data includes the dynamic recrystallization volume fraction cloud map and grain size cloud map of the cross section of the heat-resistant steel forging.

[0012] Furthermore, in step S2, the dynamic recrystallization model includes the thermal deformation activation energy, the critical strain model, the dynamic recrystallization kinetic equation, the strain model when 50% of the dynamic recrystallization occurs, and the dynamic recrystallization grain size model.

[0013] The formula for solving the activation energy of hot deformation is as follows: ;in, σ is the strain rate; A is the structural factor; α is the stress level parameter; σ is the true stress value; n is the stress-strain index; Q is the activation energy of thermal deformation; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature;

[0014] The critical strain model formula is as follows ;in, This is the critical strain value; Set to 1; d0 is the initial grain size; Z is the Zener-Hollomon parameter; To activate energy, E p1 E p2 E p3 E p4 All of these are constants determined based on stress-strain data;

[0015] The strain model formula for when 50% of dynamic recrystallization has occurred is:

[0016] ;

[0017] in, Q represents the strain value at which 50% of the dynamic recrystallization has occurred. Td For activation energy; T d1 T d2 T d3 T d4 All of these are constants determined based on stress-strain data;

[0018] The dynamic recrystallization kinetic equation is: ;in, This refers to the volume fraction of dynamic recrystallization. This is the actual strain value. , , , These are constants determined based on stress-strain data;

[0019] The formula for the dynamic recrystallization grain size model is as follows: ;in, For dynamic recrystallization grain size; Q Dd To activate energy; , , , This is a constant determined based on grain size data.

[0020] Further, step S4 includes:

[0021] S41. Based on the actual dimensions of the heat-resistant steel forgings and accessories, establish the geometric model of the heat-resistant steel forgings and accessories, and define the forging material according to the material file established in step S3;

[0022] S42, mesh the geometric model and set the mesh re-meshing conditions and parameters to obtain the finite element mesh model;

[0023] S43, based on the actual forging process of heat-resistant steel forgings, set the relative positions of the forgings and auxiliary fixtures, select the forging press and set the press parameters;

[0024] S44, Determine the boundary conditions of the finite element mesh model during the forging simulation process;

[0025] S45 simulates the forging process of heat-resistant steel forgings.

[0026] Further, in step S42, after the forging is meshed, the minimum value of the surface shape factor or volume shape factor of the mesh model of the forging is greater than 0.4; the mesh re-meshing condition is set as: mesh re-meshing is triggered according to deformation.

[0027] Furthermore, in step S44, the boundary conditions include: the initial temperature of the forging and the auxiliary fixture, the heat exchange conditions and friction conditions, and the heat exchange conditions between the forging and the external environment.

[0028] Furthermore, the heat exchange conditions between the forging and the auxiliary tool are determined based on the contact type between the forging and the auxiliary tool and the pressure applied to the forging by the auxiliary tool.

[0029] Furthermore, the heat exchange conditions include adiabatic conditions, heat exchange conditions under a pressure of 250 MPa, moderate interaction conditions between the forging and the rigid die, strong interaction conditions between the forging and the rigid die, and weak interaction conditions between the forging and the rigid die. Among these, if the heat-resistant steel forging is forged under the action conditions of a press of 3000t to 8000t, the moderate interaction conditions between the forging and the rigid die are selected; if the heat-resistant steel forging is forged under the action conditions of a press of 9000t to 15000t, the strong interaction conditions between the forging and the rigid die are selected.

[0030] Furthermore, in step S43, the press parameters include: pressing direction, initial displacement, final displacement, pressing rate, maximum pressing force, stroke, and press travel.

[0031] Further, step S1 includes:

[0032] S11, the heat-resistant steel to be analyzed is decomposed and prepared into multiple heat-resistant steel samples for hot compression tests;

[0033] S12, the heat-resistant steel sample is subjected to a single-pass hot compression test at different deformation temperatures and different deformation rates. After the deformation of the heat-resistant steel sample reaches the preset value, the heat-resistant steel sample is quenched.

[0034] S13, the heat-resistant steel sample after quenching is cut in half along its own axis, metallographic sample is prepared along the cut surface, and metallographic image of heat-resistant steel sample is obtained.

[0035] S14, Statistical analysis of grain size data of heat-resistant steel samples under different deformation conditions;

[0036] S15, Obtain the test data from the hot compression test in step S12 and convert it into the actual stress-strain data of the heat-resistant steel.

[0037] Further, in step S5, dynamic recrystallization grain simulation data is extracted, including: extracting dynamic recrystallization volume fraction cloud map and grain size cloud map of the forging cross section during and after the forging deformation of the heat-resistant steel forging.

[0038] Furthermore, the prediction method also includes: S6, obtaining actual measured grain size data of heat-resistant steel forgings after forging, and comparing the simulated dynamic recrystallization grain data with the measured grain size data to verify the accuracy of the simulated dynamic recrystallization grain data.

[0039] This invention can achieve at least one of the following beneficial effects:

[0040] 1. The prediction method of this invention establishes a dynamic recrystallization model of heat-resistant steel based on hot compression test data under different deformation conditions, and creates a material file containing this dynamic recrystallization model. This material file is then imported into a finite element simulation model of the heat-resistant steel forging to simulate the forging deformation process. This yields a dynamic recrystallization volume fraction cloud map and a grain size cloud map of the cross-section of the heat-resistant steel forging. This method can accurately and intuitively reflect the dynamic recrystallization law at various locations during the forging process of the heat-resistant steel forging. The prediction results can provide a reference for optimizing the forging process of heat-resistant steel, thereby improving product quality and production efficiency.

[0041] 2. This invention is designed for Forge software. Based on hot compression test data under different deformation conditions, a dynamic recrystallization model suitable for Forge software is established to ensure the smooth progress of the simulation process and the accuracy of the simulation results.

[0042] 3. Compared with traditional experimental methods, the prediction method of this invention can reduce the number of experiments and the cycle, and quickly discover new phenomena and new laws of grain changes in the deformation process of heat-resistant steel, thereby shortening the research and development cycle of heat-resistant steel materials, greatly saving trial and error costs, and reducing resource consumption and environmental pollution.

[0043] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0044] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0045] Figure 1 This is a flowchart illustrating the dynamic recrystallization law prediction method according to some embodiments of the present invention.

[0046] Figure 2 This is a schematic diagram of the plastic portion of the actual stress-strain curve of heat-resistant steel material in some embodiments of the present invention.

[0047] Figure 3 A schematic diagram of the hot compression process for the sample of Example 1.

[0048] Figure 4 Metallographic images of the specimen from Example 1 after hot compression tests at different deformation temperatures.

[0049] Figure 5 This is a schematic diagram of the grain size of the heat-resistant steel sample after the hot compression test in Example 1.

[0050] Figure 6 The actual stress-strain curves of 9Cr3W3Co heat-resistant steel under different deformation conditions in Example 1 are shown.

[0051] Figure 7 The curves showing the relationship between strain rate and flow stress for the 9Cr3W3Co heat-resistant steel of Example 1 are shown, where (a) is ln -σ curve, (b) is ln -lnσ curve.

[0052] Figure 8 For example 1, the 9Cr3W3Co series heat-resistant steel has ln values ​​at different deformation temperatures (a) with ln[sin h ( ασ The relationship curve of ln[sin] at different strain rates, and (b) ln[sin] at different strain rates. h ( ασ )] and T -1 The relationship curve.

[0053] Figure 9 The curves showing the relationship between the work hardening rate θ and the actual stress σ at different heating temperatures for the 9Cr3W3Co heat-resistant steel of Example 1 are shown.

[0054] Figure 10 This is the initial geometric model of the forging and auxiliary parts of Example 1.

[0055] Figure 11 This is the initial mesh model of the forging and auxiliary parts in Example 1.

[0056] Figure 12 This is a dynamic recrystallization volume fraction cloud diagram of the forging cross section of Example 1.

[0057] Figure 13 This is a cloud diagram of the average grain size of the forging cross section in Example 1.

[0058] Figure 14 The images shown are measured metallographic images of different parts of the actual forging in Example 1 after forging. (a) is the core of the forging, (b) is at R / 2 of the forging, and (c) is the surface of the forging. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the present invention clearer, exemplary embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. For clarity and brevity, not all features of actual embodiments are described in the specification.

[0060] Embodiments of the present invention provide a method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings, such as... Figure 1 As shown, the prediction method of this invention includes the following steps:

[0061] S1, hot compression tests were conducted on heat-resistant steel samples at different deformation temperatures and strain rates to obtain stress-strain data and grain size data of the heat-resistant steel samples at different deformation temperatures and strain rates and after the hot compression test.

[0062] S2, Based on the stress-strain data of the heat-resistant steel sample, a dynamic recrystallization model of the heat-resistant steel is obtained by fitting.

[0063] S3. Based on the dynamic recrystallization model of heat-resistant steel, establish the material file for heat-resistant steel forgings;

[0064] S4. Establish a finite element simulation model of the heat-resistant steel forging based on the actual heat-resistant steel forging, input the material file into the finite element simulation model, and set the simulation parameters according to the actual forging process of the heat-resistant steel forging to simulate the forging process of the heat-resistant steel forging.

[0065] S5, extract the dynamic recrystallization grain simulation data of the heat-resistant steel forging. The dynamic recrystallization grain simulation data includes the dynamic recrystallization volume fraction cloud map and grain size cloud map of the cross section of the heat-resistant steel forging.

[0066] The prediction method of this invention establishes a dynamic recrystallization model of heat-resistant steel using hot compression test data under different deformation conditions, and creates a material file containing this dynamic recrystallization model. This material file is then imported into a finite element simulation model of the heat-resistant steel forging to simulate the forging deformation process. This yields a dynamic recrystallization volume fraction cloud map and a grain size cloud map of the cross-section of the heat-resistant steel forging. This method can accurately and intuitively reflect the dynamic recrystallization law at various locations during the forging process of the heat-resistant steel forging. The prediction results can provide a reference for optimizing the forging process of heat-resistant steel, thereby improving product quality and production efficiency.

[0067] Compared with traditional experimental methods, the prediction method of this invention can reduce the number of experiments and the cycle, and quickly discover new phenomena and new laws of grain changes during the deformation process of heat-resistant steel, thereby shortening the research and development cycle of heat-resistant steel materials, greatly saving trial and error costs, and reducing resource consumption and environmental pollution.

[0068] In some embodiments, in step S2, the dynamic recrystallization model includes thermal deformation activation energy, critical strain model, dynamic recrystallization kinetic equation, strain model when 50% of dynamic recrystallization occurs, and dynamic recrystallization grain size model.

[0069] The formula for solving the activation energy of hot deformation is as follows: ;in, σ is the strain rate; A is the structural factor; α is the stress level parameter; σ is the true stress value; n is the stress-strain index; Q is the activation energy of thermal deformation; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature;

[0070] The critical strain model formula is as follows ;in, This is the critical strain value; Set to 1; d0 is the initial grain size; Z is the Zener-Hollomon parameter; To activate energy, E p1 E p2 E p3 E p4 All of these are constants determined based on stress-strain data;

[0071] The strain model formula for when 50% of dynamic recrystallization has occurred is:

[0072] ;

[0073] in, Q represents the strain value at which 50% of the dynamic recrystallization has occurred. Td For activation energy; T d1 T d2 T d3 T d4 All of these are constants determined based on stress-strain data;

[0074] The dynamic recrystallization kinetic equation is: ; where X D This refers to the volume fraction of dynamic recrystallization. This is the actual strain value. , , , These are constants determined based on stress-strain data;

[0075] The formula for the dynamic recrystallization grain size model is as follows: ;in, For dynamic recrystallization grain size; Q Dd To activate energy; , , , This is a constant determined based on grain size data.

[0076] In an embodiment of the present invention, the forging deformation process of heat-resistant steel forgings is simulated using Forge software. For Forge software, the present invention establishes the above-mentioned dynamic recrystallization model suitable for Forge software based on hot compression test data under different deformation conditions, so as to ensure the smooth progress of the simulation process and the accuracy of the simulation results.

[0077] In some embodiments, for heat-resistant steel materials, the initial grain size and Zener-Hollomon parameters have a negligible impact on the dynamic recrystallization during forging deformation compared to deformation temperature and deformation rate. Therefore, this invention does not consider the influence of the initial grain size and Zener-Hollomon parameters on the dynamic recrystallization model, and uses the coefficient E in the above dynamic recrystallization model. p2 E p4 T d2 T d4 Setting it to 0 eliminates the need for hot compression tests at different initial grain sizes, reducing the number of tests and improving prediction efficiency.

[0078] In some embodiments, step S1 includes:

[0079] S11, the heat-resistant steel to be analyzed is decomposed and prepared into multiple heat-resistant steel samples for hot compression tests;

[0080] S12, the heat-resistant steel sample is subjected to a single-pass hot compression test at different deformation temperatures and different deformation rates. After the deformation of the heat-resistant steel sample reaches the preset value, the heat-resistant steel sample is quenched.

[0081] S13, the heat-resistant steel sample after quenching is cut in half along its own axis, metallographic sample is prepared along the cut surface, and metallographic image of heat-resistant steel sample is obtained.

[0082] S14, Statistical analysis of grain size data of heat-resistant steel samples under different deformation conditions;

[0083] S15, Obtain the test data from the hot compression test in step S12 and convert it into the actual stress-strain data of the heat-resistant steel.

[0084] Specifically, a Gleeble thermal simulation testing machine can be used to conduct a thermal compression test. In step S11, multiple Gleeble thermal compression specimens need to be prepared.

[0085] In step S13, the heat-resistant steel sample cut in half is ground, polished and etched along the cut surface to prepare a metallographic sample and obtain a metallographic image with clear grain boundaries.

[0086] In step S14, the grain size of the heat-resistant steel sample under different deformation conditions can be statistically determined using Image-Pro Plus software. For example, two intersecting straight lines can be drawn on the metallographic image, ensuring that at least 50 intercept points (intercept points are the intersections of the lines and grain boundaries) can be obtained from the two lines. The number of intercept points obtained from the grid is measured and the grain size is determined. The average of the grain sizes obtained from the two lines is the grain size of the corresponding field of view of the metallographic image. Using this method, the grain size of multiple fields of view of the same heat-resistant steel sample is calculated and the average is taken, which is the grain size under the deformation condition, ensuring the accurate acquisition of the grain size of the heat-resistant steel sample under each deformation condition.

[0087] Based on the actual stress-strain data of the heat-resistant steel material obtained in step S15, a dynamic recrystallization model of the heat-resistant steel can be fitted.

[0088] In some embodiments, the process of determining the thermal deformation activation energy in step S2 is as follows:

[0089] Establish the constitutive equation for heat-resistant steel materials, and the relationship between the material's rheological stress σ and strain rate. The relationship between temperature T and temperature can be expressed as:

[0090] (1);

[0091] Under low stress levels, equation (1) can be expressed as: (2);

[0092] Under high stress levels, equation (1) can be expressed as: (3);

[0093] In the formula, A1, A2, n1, and β are all constants. .

[0094] Taking the logarithm of both sides of equations (2) and (3) respectively, we get:

[0095] (4);

[0096] (5);

[0097] From equations (4) and (5), it can be seen that when the temperature is constant, n1 and β are respectively and The slope of the relationship curve. Therefore, the peak stress of the heat-resistant steel can be extracted from the stress-strain data obtained from the hot compression test, and then... and Plot the points and perform linear regression analysis on both to obtain the average slope, thus obtaining the values ​​of n1, β, and α.

[0098] Based on this, assuming that the activation energy Q of thermal deformation is independent of temperature T, taking the logarithm of both sides of equation (1) yields:

[0099] (6);

[0100] Substitute the value of α into equation (6) and plot the results at different deformation temperatures. Relationship curves and strain rates at different strain rates The relationship curve is obtained by performing linear regression fitting on the two curves to find the average slope, and the values ​​of n and Q / nR can be obtained. From this, the value of the thermal deformation activation energy Q can be obtained.

[0101] In some embodiments, the process of determining the critical strain model in step S2 is as follows:

[0102] Critical strain is obtained through the θ-σ curve (work hardening rate) The inflection point in the stress-strain curve is used to determine the work hardening rate. The work hardening rate at different deformation temperatures is calculated by fitting the stress-strain curve using differential polynomial regression, thus obtaining the relationship curve between the work hardening rate θ and the true stress σ at different deformation temperatures (θ-σ curve). The intersection of the θ-σ curve and the horizontal axis (i.e., the point where the work hardening rate θ is zero) represents the peak stress, and the inflection point in the curve (i.e., the point where the absolute slope of the curve is minimum) represents the critical value at which dynamic recrystallization begins. The horizontal axis corresponds to the critical stress value. Based on this method, the critical stress values ​​at different deformation temperatures and strain rates are obtained.

[0103] The relationship between the critical strain of a material and parameters such as initial grain size, strain rate, Z-parameter, and temperature is expressed by the following equation:

[0104]

[0105] Where the initial grain size is not considered The influence of the Z parameter on the critical strain, E p2 E p4 Set to 0.

[0106] Taking the logarithm of both sides of the above equation and substituting different deformation temperatures, strain rates, and corresponding critical strain values, we can obtain the result. , and Thus, the critical strain model formula for heat-resistant steel is obtained.

[0107] In some embodiments, the process for determining the strain model when 50% of the dynamic recrystallization occurs in step S2 is as follows:

[0108] The strain at which 50% dynamic recrystallization occurs is the strain corresponding to the maximum softening rate. Its value can be obtained by relating the work hardening rate θ to the true strain. The relationship curve between the two is determined, and the strain corresponding to the work hardening rate θ being equal to 0 is the peak strain ε. p and steady-state strain ε s The strain corresponding to the minimum work hardening rate θ is the strain ε at which the maximum softening rate is achieved. 0.5 .

[0109] The relationship between strain and parameters such as initial grain size, strain rate, Z-parameter, and temperature when 50% of the dynamic recrystallization of heat-resistant steel occurs is expressed by the following equation:

[0110]

[0111] In the formula T d1 T d2 T d3 T d4 All values ​​are constants, and the effect of initial grain size and Z-parameter on strain at 50% dynamic recrystallization is not considered. The impact will be T d2 T d4 Set to 0.

[0112] Taking the logarithm of both sides of the above equation and substituting different deformation temperatures, strain rates, and corresponding critical strain values, we can obtain the result. , and A strain model was obtained when 50% of the dynamic recrystallization of heat-resistant steel material occurred.

[0113] In some embodiments, the process of determining the dynamic recrystallization kinetic equation in step S2 is as follows:

[0114] The volume fraction of dynamic recrystallization is as follows Figure 2 The relationship between the dynamic recrystallization volume fraction and the thermorheological stress parameter is determined by the actual stress-strain curve shown.

[0115] (7);

[0116] In the formula, X D σ represents the volume percentage of dynamic recrystallization. WH To extend the stress of the work-hardened portion, it can be determined by ε < ε c The stress-strain data for each stage were obtained by extrapolation from the mathematical model of the dynamic recovery rheological curve; σ represents the instantaneous stress; σ s For saturation stress; σ ss For steady-state stress; σ and σ ss It can be determined based on the actual stress-strain curve. Among them,

[0117] (8);

[0118] (9);

[0119] In the formula, σ0 is the yield stress. Ω represents the actual strain; Ω represents the dynamic recovery softening amount.

[0120] In some embodiments, the yield stress σ0 is obtained by: linearly fitting the elastic portion of the true stress-strain curve, the slope of which corresponds to the elastic modulus G; plotting the straight line y = G(ε - 0.002) + intercept; the stress at the intersection of this line and the true stress-strain curve is the yield stress σ0; saturation stress σ s It can be based on the peak stress σ p Determine, specifically, σ s =σ p *1.1.

[0121] Determining the yield stress σ0 and saturation stress σ s Then, Ω and σ can be determined according to equation (8) or (9). WH Specifically, by substituting the stress-strain value at any point in the plastic portion of the true stress-strain curve where the strain value is less than the critical strain value into equation (8) or (9), the values ​​of Ω and σ can be obtained. WH value.

[0122] Then, the volume fraction of dynamic recrystallization can be determined according to the relationship between the dynamic recrystallization fraction and the thermorheological stress parameter (7).

[0123] Furthermore, in determining the dynamic recrystallization volume fraction data and critical strain... After that, it can be based on , and actual strain To determine the dynamic recrystallization kinetic equation:

[0124]

[0125] In the formula, This refers to the volume fraction of dynamic recrystallization. , , , As a constant, take the logarithm of both sides of the above equation, and consider the strain under different deformation conditions. Critical strain and the corresponding dynamic recrystallization volume fraction Substitute the values ​​to determine the result. , , , The value of is then used to determine the dynamic recrystallization kinetic equation.

[0126] In some embodiments, the dynamic recrystallization grain size model is determined based on the grain size of the heat-resistant steel sample under different deformation conditions (including deformation temperature and deformation rate). Specifically, the formula for the dynamic recrystallization grain size model is as follows:

[0127] ;

[0128] in, , , , Since the initial grain size and Z parameter are constants, this invention does not consider the influence of the initial grain size and Z parameter on the dynamic recrystallization grain size. , Set to 0.

[0129] Taking the logarithm of both sides above and substituting the grain size measured under different deformation conditions in step S14, the result can be determined. , and This leads to the determination of the dynamic recrystallization grain size model.

[0130] After determining the dynamic recrystallization model, step S3 can be performed to create a heat-resistant steel material file suitable for Forge software based on the fitted dynamic recrystallization model, in order to ensure the accuracy of the simulation results.

[0131] The material documents include dynamic recrystallization models such as the activation energy of hot deformation of heat-resistant steel, critical strain model, dynamic recrystallization kinetic equation, and dynamic recrystallization grain size model, which are determined by the above methods based on hot compression test data.

[0132] In addition, the material file includes thermal property data for the heat-resistant steel, including thermal conductivity, coefficient of thermal expansion, Young's modulus, Poisson's ratio, and plastic modulus. In some embodiments, the thermal property data for the heat-resistant steel can be determined experimentally or calculated using JMatPro software.

[0133] In some embodiments, step S4 includes:

[0134] S41. Based on the actual dimensions of the heat-resistant steel forgings and fixtures, establish the geometric model of the heat-resistant steel forgings and fixtures, and define the forging material according to the material file established in step S3.

[0135] S42, mesh the geometric model and set the mesh re-meshing conditions and parameters to obtain the finite element mesh model;

[0136] S43, based on the actual forging process of heat-resistant steel forgings, set the relative positions of the forgings and auxiliary fixtures, select the forging press and set the press parameters;

[0137] S44, Determine the boundary conditions of the finite element mesh model during the forging simulation process;

[0138] S45 simulates the forging process of heat-resistant steel forgings.

[0139] This invention establishes a finite element model based on actual heat-resistant steel forgings and performs simulation calculations of the forging process based on actual forging technology. This allows for the simulation of the actual forging process of heat-resistant steel forgings and facilitates the prediction of the dynamic recrystallization law of the actual forging process of heat-resistant steel forgings.

[0140] In step S41, the geometric model of the heat-resistant steel forging and fixtures can be a two-dimensional or three-dimensional model. The established geometric model is imported into Forge software, and then the material file established in step S3 is also imported into Forge software to define the forging material.

[0141] Among them, auxiliary tools are molds or tools used in the forging process, including but not limited to one or more of the following: flat anvil, V-anvil, wide flat anvil, wide platform, screed, upsetting plate, punch, and screed.

[0142] In step S42, when meshing the geometric model, the Forge software can identify the geometric dimensions and provide recommended mesh sizes, including coarse, medium, fine, and high-quality meshes. In some embodiments, the mesh size can be selected based on the mesh size recommended by the Forge software; alternatively, the experimenter can set the mesh size themselves. Specifically, the mesh model of the forging can be set to a fine or high-quality mesh, while the mesh model of the rigid fixture can be set to a coarse mesh to maximize computational speed.

[0143] In some embodiments, after the forging is meshed in step S42, the surface shape factor (2D) or volume shape factor (3D) of the forging mesh model needs to be checked to ensure that the minimum value of the surface shape factor or volume shape factor of the forging mesh model is greater than 0.4, so as to ensure that a better quality mesh is obtained, which is beneficial to improving the accuracy of the simulation calculation results.

[0144] In some embodiments, when setting the mesh re-division conditions and parameters in step S42, the mesh re-division condition can be set as: mesh re-division triggered by deformation; the parameters include: setting the volume size factor to 1.1 or 2. The volume size factor affects the size of the mesh inside the forging, and represents the factor by which the mesh elements inside the forging will be refined compared to the surface mesh elements. By setting the mesh re-division conditions and the volume size factor, this invention can adjust the mesh density and shape during the forging deformation process to adapt to the dynamic changes of the forging and improve calculation accuracy.

[0145] In some embodiments, in step S43, the press parameters are set according to the actual forging process of the heat-resistant steel forging to be simulated. The forging process includes, but is not limited to, one or more of the following: upsetting, drawing, extrusion, punching, and reaming.

[0146] Furthermore, the press parameters include: pressing direction, initial displacement, final displacement, pressing rate, maximum pressing force, stroke, and press stroke; wherein, the press stroke can be set to a single-pass pressing stroke or a multi-pass pressing stroke. If the forging process includes different forging steps, the press parameters corresponding to each forging step are set according to the actual forging steps.

[0147] In some embodiments, the boundary conditions in step S44 include: the initial temperature of the forging and the auxiliary fixture, the heat exchange conditions and friction conditions, and the heat exchange conditions between the forging and the external environment.

[0148] The initial temperatures of the forging and the auxiliary fixtures can be set based on the actual initial temperatures during forging. The heat exchange conditions between the forging and the auxiliary fixtures are determined based on the type of interaction between them and the pressure applied to the forging by the auxiliary fixtures.

[0149] Furthermore, the heat exchange conditions include adiabatic conditions, heat exchange conditions under a pressure of 250 MPa, moderate interaction conditions between the forging and the rigid die, strong interaction conditions between the forging and the rigid die, and weak interaction conditions between the forging and the rigid die. When performing simulation calculations using Forge software, the heat exchange conditions can be selected from the above. The greater the applied pressure, the greater the heat transfer coefficient corresponding to the selected heat exchange condition.

[0150] Specifically, the selection rules are as follows:

[0151] (1) When the influence of the auxiliary tool temperature on the temperature change of the forging is not a concern, an adiabatic condition can be selected. For example, when simulating the compression process of a hot compression sample, the sample is briefly heated to a certain temperature by resistance and held at that temperature. During the compression process, the resistance heating state is also maintained. In this case, the sample can be simplified into an adiabatic model.

[0152] (2) The heat exchange conditions under 250MPa pressure (ALphaT Pressure 250MPa) are applicable to the process conditions of forgings under short-term high contact pressure.

[0153] (3) If the heat-resistant steel forging is forged under the action of a press with a pressure of 3000t~8000t, and the medium interaction condition between the forging and the rigid die is selected, the corresponding heat transfer coefficient is 10000W / m 2•K. For example, when forgings are forged under the action of a 4000t hydraulic press or a 6000t water press, moderate interaction conditions can be selected.

[0154] (4) If the heat-resistant steel forging is forged under the action of a press with a pressure of 9000t~15000t, and the strong interaction condition between the forging and the rigid die is selected, the corresponding heat transfer coefficient is 20000W / m. 2 •K. For example, when forgings are forged under the action of a 10,000t or 15,000t hydraulic press, strong interaction conditions can be selected.

[0155] (5) If the interaction between the heat-resistant steel forging and the auxiliary tool is small, or even if no pressure is applied to the forging, for example, if the heat-resistant steel forging is forged under the action of a press with a pressure of less than or equal to 2000t, or if there is no force between the forging and the auxiliary tool, a weak interaction condition between the forging and the rigid die can be selected, with a corresponding heat transfer coefficient of 2000W / m 2 •K.

[0156] Furthermore, when the working environment during forging is air, the heat exchange conditions between the forging and the external environment can be selected as air heat exchange, with a corresponding heat transfer coefficient of 10 W / m. 2 •K.

[0157] In some embodiments, friction conditions can be selected empirically based on the contact type between the forging and the fixture, or friction conditions recommended by the Forge software can be selected. Friction conditions include: Bilateral Sliding Contact, Bilateral Sticking Contact, Friction with No Lubrication, Friction with Water and Graphite Lubrication, High Friction Conditions, High Friction with Oil Lubrication, Low Friction with Oil Lubrication, No Contact Condition, Sliding Contact, Tresca High, Tresca Very High, Viscoplastic Friction Model, and Viscoplastic Model 0.5.

[0158] Bilateral sliding contact means that the joint between the forging and the fixture can slide on the contact surface between the forging and the fixture, but cannot leave the contact surface.

[0159] Bilateral sticking contact refers to the bonding between the joints of the forging and the fixture at the contact surface.

[0160] Friction with no lubrication is applicable to friction under conditions without lubrication. When choosing friction with no lubrication, the friction coefficient of the Coulomb model is 0.4, and the friction coefficient of the Tresca model is 0.8.

[0161] When friction with water and graphite lubrication is selected, the friction coefficient of the Coulomb model is 0.15, and the friction coefficient of the Tresca model is 0.3.

[0162] When high friction conditions are selected, the friction coefficient of the Coulomb model is 0.3, and the friction coefficient of the Tresca model is 0.6.

[0163] When high friction with oil lubrication is selected, the friction coefficient of the Coulomb model is 0.1, and the friction coefficient of the Tresca model is 0.2.

[0164] When low friction with oil lubrication is selected, the friction coefficient of the Coulomb model is 0.075, and the friction coefficient of the Tresca model is 0.15.

[0165] The coefficients of friction for no-contact and sliding contact conditions are as follows: 0.45 for Tresca High, 0.8 for Tresca Very High, 0.3 for Viscoplastic, and 0.5 for Viscoplastic 0.5.

[0166] In addition, the boundary conditions also include: robot arm motion conditions. In step S44, the robot arm motion conditions can be set according to the actual motion state of the robot arm during forging. It should be noted that the robot arm is used to control the movement of the forging during the forging process, for example, controlling the rotation of the forging.

[0167] After completing the mesh generation, boundary conditions, forging process, and press parameters, the model can be saved and the calculation submitted to simulate the actual forging process of heat-resistant steel forgings.

[0168] In some embodiments, step S5, obtaining dynamic recrystallization grain simulation data of heat-resistant steel forging includes: obtaining a dynamic recrystallization volume fraction cloud map and a grain size cloud map of the forging cross section after the forging deformation of the heat-resistant steel forging. Based on the dynamic recrystallization volume fraction cloud map and grain size cloud map of the forging cross section after forging deformation, the dynamic recrystallization law of the heat-resistant steel forging during the forging deformation process can be predicted.

[0169] Furthermore, dynamic recrystallization volume fraction cloud map and grain size cloud map of the cross section of the heat-resistant steel forging during the forging deformation process can be extracted. For example, the dynamic recrystallization volume fraction cloud map and grain size cloud map of the cross section of the forging after each pressing during the forging process can be extracted, thereby exploring the dynamic recrystallization change law during the forging process and providing a reference for designing the forging process.

[0170] In some embodiments, such as Figure 1 As shown, the prediction method also includes:

[0171] S6. Obtain actual measured grain size data of heat-resistant steel forgings after forging, and compare the simulated grain size data of dynamic recrystallization with the measured grain size data to verify the accuracy of the simulated grain size data of dynamic recrystallization, thereby ensuring the accuracy of the prediction of dynamic recrystallization law.

[0172] The following specific embodiments further illustrate the method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings according to the present invention.

[0173] Example 1

[0174] This embodiment predicts the dynamic recrystallization law of grains during the forging process of 9Cr3W3Co heat-resistant steel forgings. The specific chemical composition range of the main elements of the heat-resistant steel forgings is shown in Table 1.

[0175] Table 1. Chemical composition range of 9Cr3W3Co series heat-resistant steel (ωt, %)

[0176]

[0177] The prediction method includes the following steps:

[0178] S1, Perform a hot compression test on the above-mentioned heat-resistant steel alloy to obtain its actual stress-strain data and grain size data after the hot compression test:

[0179] S11, decompose the above-mentioned heat-resistant steel alloy and prepare 20 Gleeble test specimens;

[0180] S12, the sample prepared in step S11 is processed according to... Figure 3 The hot compression process shown was used for a single-pass hot compression test. Specifically, the specimen was heated to 1200℃ at a rate of 10℃ / s and held at that temperature for 5 minutes; then cooled to the desired deformation temperatures of each specimen at a rate of 3℃ / s, namely 1000℃, 1050℃, 1100℃, 1150℃, and 1200℃, respectively, and held at each deformation temperature for 30 seconds; then, it was hot-compressed at the desired deformation rate of 60%, with each deformation rate being 0.01s. -1 0.1s -1 1.0s -1 10.0s-1 Once the deformation of the sample reaches 60% of the preset value, the sample is immediately quenched using water cooling.

[0181] S13, the quenched sample is cut in half along its own axis. The cut sample is then ground, polished, and etched along the cut surface to prepare a metallographic sample. A metallographic image with clear grain boundaries is obtained from the metallographic sample, such as... Figure 4 As shown.

[0182] S14, using Image-Pro Plus software, statistically analyze the grain size under different deformation conditions in the metallographic images obtained in step S13. Specifically, for a specific field of view of a metallographic image of a sample, such as... Figure 5 As shown, two intersecting straight lines are drawn on the metallographic image. The number of intercept points on the lines is measured and the grain size is determined. The average of the grain sizes obtained from the two lines is the grain size of the corresponding field of view of the metallographic image.

[0183] S15, Obtain the test data from the hot compression test in step S12 and convert it into actual stress-strain data, such as... Figure 6 As shown.

[0184] S2. Based on the actual stress-strain data obtained in step S1, determine the dynamic recrystallization model of 9Cr3W3Co heat-resistant steel under the above deformation conditions.

[0185] (1) Activation energy of thermal deformation:

[0186] Rheological stress σ and strain rate The relationship between temperature T and temperature can be expressed as:

[0187] (1);

[0188] Under low stress levels, equation (1) can be expressed as: (2);

[0189] Under high stress levels, equation (1) can be expressed as: (3);

[0190] In the formula, σ is the strain rate; A is the structural factor; α is the stress level parameter; σ is the true stress value; n is the stress-strain exponent; Q is the activation energy of thermal deformation; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature; A1, A2, n1, and β are all constants, where .

[0191] Taking the logarithm of both sides of equations (2) and (3) respectively, we get:

[0192] (4);

[0193] (5);

[0194] From equations (4) and (5), it can be seen that when the temperature is constant, n1 and β are respectively and The slope of the relationship curve. From this, we can extract, for example... Figure 6 The peak stress of 9Cr3W3Co steel in the actual stress-strain data shown is respectively for... and Plotting points, such as Figure 7 As shown, and by performing linear regression analysis on the two to obtain the average slope, we get n1 = 5.22952, β = 0.06632. .

[0195] Based on this, assuming that the activation energy Q of thermal deformation is independent of temperature T, taking the logarithm of both sides of equation (1) yields:

[0196] (6);

[0197] Substitute the value of α into equation (6) and plot the results at different deformation temperatures. Relationship curves and strain rates at different strain rates Relationship curves, such as Figure 8 As shown, by performing linear regression fitting on the two and calculating the average slope, we obtain n=3.85146. Therefore, the activation energy for thermal deformation can be calculated as Q = 496.414 kJ / mol.

[0198] (2) Critical strain model:

[0199] The relationship between the work hardening rate θ and the actual stress σ at different heating temperatures was calculated by fitting the stress-strain curve using differential polynomial regression, as shown in the figure. Figure 9 As shown. The critical strain value is determined based on the θ-σ curve: the intersection of the θ-σ curve and the horizontal axis (i.e., the point where the work hardening rate θ is zero) is the peak stress, and the inflection point in the curve (i.e., the point where the absolute slope of the curve is minimum) is the critical value at which dynamic recrystallization begins. Figure 9 It can be seen that in the early stage of deformation, the work hardening rate θ decreases sharply with the increase of deformation until it reaches a minimum value. This moment corresponds to the critical moment when dynamic recrystallization begins, and the horizontal axis corresponds to the critical stress value. As the deformation continues to increase, the work hardening rate θ value drops to 0, and the horizontal axis corresponds to the peak stress value.

[0200] The relationship between the critical strain of a material and parameters such as initial grain size, strain rate, Z-parameter, and temperature is expressed by the following equation:

[0201]

[0202] In the formula, This is the critical strain value; Set to 1; d0 is the initial grain size, and its influence on the critical strain is not considered here; Z is the Zener-Hollomon parameter, and its influence on the critical strain is not considered here. To activate energy, E p1 E p2 E p3 E p4 Both are constants, E p2 E p4 Set to 0.

[0203] Taking the logarithm of both sides of the above equation and substituting different deformation temperatures, strain rates, and corresponding critical strain values, we can obtain the critical strain model formula for heat-resistant steel as follows:

[0204] .

[0205] (3) Strain model when 50% of dynamic recrystallization occurs:

[0206] Similar to the method for determining the critical strain, the work hardening rate θ and the true strain at different heating temperatures are calculated by fitting the stress-strain curve using differential polynomial regression. The relationship curve at θ- In the relationship curve, the strain corresponding to the work hardening rate θ being equal to 0 is the peak strain ε. p and steady-state strain ε s The strain corresponding to the minimum work hardening rate θ is the strain ε at which 50% of the dynamic recrystallization occurs when the maximum softening rate is reached. 0.5 .

[0207] Strain when 50% of the material undergoes dynamic recrystallization The relationship between the initial grain size, strain rate, Z-parameter, and temperature is expressed by the following equation:

[0208]

[0209] In the formula, T d1 T d2 T d3 T d4 All are constants. Q Td As an activation energy, the effects of initial grain size and Zener-Hollomon parameters on strain at 50% dynamic recrystallization are not considered here. The influence of T d2 T d4 Set to 0.

[0210] Taking the logarithm of both sides of the above equation and substituting different deformation temperatures, deformation rates, and corresponding critical strain values, we can obtain the strain model when 50% of the material's dynamic recrystallization has occurred:

[0211] .

[0212] (4) Dynamic recrystallization kinetic equation:

[0213] The relationship between the dynamic recrystallization volume fraction and the thermorheological stress parameters is as follows:

[0214] (7);

[0215] In the formula, X D σ represents the volume percentage of dynamic recrystallization. WH To extend the stress of the work-hardened portion, it can be determined by ε < ε c The stress-strain data for each stage were obtained by extrapolation from the mathematical model of the dynamic recovery rheological curve, where σ is the instantaneous stress. s For saturation stress, σ ss For steady-state stresses, σ and σ ss Determined based on the actual stress-strain curve. Among them,

[0216] (8);

[0217] (9);

[0218] By linearly fitting the elastic portion of the true stress-strain curve, the slope of which corresponds to the elastic modulus G, a straight line y = G(ε - 0.002) + intercept is plotted. The stress at the intersection of this line and the true stress-strain curve is the yield stress σ0; the saturation stress σ s =σ p *1.1.

[0219] Determining the yield stress σ0 and saturation stress σ s Then, substitute the stress-strain value at any point in the plastic part of the true stress-strain curve where the strain value is less than the critical strain value into equation (8) or (9) to obtain the Ω value and σ. WH The value is then determined by the relationship between the dynamic recrystallization fraction and the thermorheological stress parameter (7).

[0220] The dynamic recrystallization kinetic equation is:

[0221]

[0222] In the formula, X D This refers to the volume fraction of dynamic recrystallization. , , , As a constant, take the logarithm of both sides of the above equation, and consider the strain under different deformation conditions. Critical strain Substituting the corresponding dynamic recrystallization volume fraction into the equation, the dynamic recrystallization kinetics equation can be determined as follows:

[0223] .

[0224] (5) Dynamic recrystallization grain size model:

[0225] The formula for the dynamic recrystallization grain size model is as follows:

[0226] ;

[0227] in, , , , As a constant, neglecting the influence of initial grain size and Z parameter on dynamic recrystallization grain size, , Set to 0.

[0228] Taking the logarithm of both sides above and substituting the grain sizes measured under different deformation conditions in step S14, the dynamic recrystallization grain size model can be obtained as follows:

[0229] .

[0230] S3. Based on the dynamic recrystallization model obtained in step S2, establish the material file for 9Cr3W3Co heat-resistant steel.

[0231] S4, Simulation of the forging deformation process of 9Cr3W3Co series heat-resistant steel forgings:

[0232] S41, Establish the initial geometric model of the forging and auxiliary tools, such as Figure 10 As shown. Import the initial geometric model and the material file created in step S3 into the Forge software.

[0233] S42, select the fine mesh size in Forge software to mesh the initial geometric model, obtaining the initial mesh model, such as... Figure 11 As shown, the mesh is re-meshed as the forging deforms, and the volumetric size factor is set to 2, meaning the mesh elements inside the forging will be refined by a factor of two.

[0234] S43, Set the relative positions of the forging and the auxiliary fixtures according to the actual forging process, select a suitable forging press, and set the press parameters according to the actual forging process:

[0235] The forging process to be simulated in this embodiment includes two steps: upsetting and drawing. The forging process is completed in two steps in one firing, and a 4000t press is selected.

[0236] The first process is the upsetting process. In this process, the press stroke is set to a single-pass pressing stroke, the stroke is set to 440mm, the pressing speed is 10mm / s, and the maximum pressure is 4000t. When the maximum pressure of the press is reached, the pressure is maintained and the press continues to press down until the stroke requirement is met.

[0237] The second process is the wide-anvil drawing process. The model after the upsetting process is directly exported as the initial model for the wide-anvil drawing process. The fixtures are an upper wide-anvil and a lower platform. The press stroke is set to a multi-pass pressing stroke, with a total stroke of approximately 320mm, a pressing rate of 20mm / s, and a maximum pressure of 4000t. The movement of the fixtures and the billet is defined in the multi-pass file. Specifically, to ensure uniform deformation of the billet during the pressing of the fixtures and to prevent excessive shift of the center of gravity due to shape changes that would affect the final forging shape, the movement of each pass is defined as follows: for each press of the upper wide-anvil, the billet rotates 37.5° around the axis corresponding to its center of gravity. After ten presses, the pass ends. After each pass, the upper and lower fixtures move 30mm towards the billet, and the movement of each pass continues until the final stroke of the upper wide-anvil meets the set requirements.

[0238] S44, Determine the boundary conditions for the forging simulation process:

[0239] The settings are based on the actual forging conditions of the forging. The initial temperatures of the forging and the auxiliary tooling are 1200℃ and 250℃, respectively. The heat exchange condition between the forging and the auxiliary tooling is set to "Medium Interaction with Steel Dies", and the friction condition between them is set to "Friction with Water and Graphite Lubrication". The ambient temperature is set to 50℃, and the heat exchange condition between the forging and the environment is set to "Heat Transfer with Air". The motion of the robot is set to be able to rotate around the axis, but not move along the axis, based on the actual motion state of the robot.

[0240] Save the model and submit the calculation.

[0241] S5, obtain the dynamic recrystallization volume fraction cloud map and average grain size cloud map of the forging cross section after forging, such as Figure 12 and Figure 13 As shown, this study investigates the dynamic recrystallization law of forgings throughout the forging process.

[0242] like Figure 12 As shown, the dynamic recrystallization volume fraction is larger in the core, at R / 2, and near the surface of the forging; for example... Figure 13 As shown, the dynamic recrystallized grain size is smaller in the near-center and R / 2 region of the forging, while the grain size is largest on the surface.

[0243] S6. Obtain the grain size of the measured forging to verify the accuracy of the simulation results.

[0244] like Figure 14 As shown, the grains are finest at R / 2 of the actual forged part, coarser in the core, and coarsest on the surface. The coarser grains in the core are mainly due to the temperature rise in the core during deformation. Therefore, the dynamic recrystallization law predicted by simulation is approximately consistent with the law obtained by actual measurement, indicating that the prediction method of this embodiment is reliable, and the dynamic recrystallization model established by the above method is reliable.

[0245] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings, characterized in that, Includes the following steps: S1, hot compression tests are conducted on heat-resistant steel samples at different deformation temperatures and different strain rates to obtain stress-strain data and grain size data of the heat-resistant steel samples at different deformation temperatures and different strain rates. S2, Based on the stress-strain data of the heat-resistant steel sample, a dynamic recrystallization model of the heat-resistant steel material is fitted; the dynamic recrystallization model includes a critical strain model, a dynamic recrystallization kinetic equation, a strain model when 50% of the dynamic recrystallization occurs, and a dynamic recrystallization grain size model. The critical strain model formula is as follows: ;in, This is the critical strain value; Set to 1; d0 is the initial grain size; Z is the Zener-Hollomon parameter; To activate energy, E p1 E p2 E p3 E p4 All are constants determined based on the stress-strain data, without considering the influence of the initial grain size d0 and Z parameters on the critical strain, and E p2 E p4 Set to 0; The strain model formula for when 50% of the dynamic recrystallization occurs is: ； in, Q represents the strain value at which 50% of the dynamic recrystallization has occurred. Td For activation energy; T d1 T d2 T d3 T d4 All are constants determined based on the stress-strain data, without considering the initial grain size and Z-parameter's effect on the strain at 50% dynamic recrystallization. The impact will be T d2 T d4 Set to 0; The dynamic recrystallization kinetic equation is: ; where X D This refers to the volume fraction of dynamic recrystallization. This is the actual strain value. , , , This is a constant determined based on the stress-strain data; the relationship between the dynamic recrystallization volume fraction and the thermorheological stress parameters is as follows: ； In the formula, X D σ represents the volume percentage of dynamic recrystallization. WH To extend the stress of the work-hardened portion, it can be determined by ε < ε c The stress-strain data for each stage were obtained by extrapolation from the mathematical model of the dynamic recovery rheological curve; σ represents the instantaneous stress; σ s For saturation stress; σ ss For steady-state stress; σ and σ ss Determined based on the actual stress-strain curve; where, ； ； In the formula, σ0 is the yield stress. Ω represents the actual strain; Ω represents the dynamic recovery softening amount. The formula for the dynamic recrystallization grain size model is as follows: ;in, For dynamic recrystallization grain size; Q Dd To activate energy; , , , The constants determined based on the grain size data do not consider the influence of the initial grain size and Z parameter on the dynamic recrystallization grain size. , Set to 0; in, is the strain rate; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature; There is no need to conduct hot compression tests under different initial grain sizes, which reduces the number of tests and improves prediction efficiency; S3, Based on the dynamic recrystallization model of the heat-resistant steel material, establish a material file for the heat-resistant steel material suitable for Forge software; S4. Establish a finite element simulation model of the heat-resistant steel forging based on the actual heat-resistant steel forging, input the material file into the finite element simulation model, set the simulation parameters according to the actual forging process of the heat-resistant steel forging, and use Forge software to simulate the forging process of the heat-resistant steel forging. S5, extract the dynamic recrystallization grain simulation data of the heat-resistant steel forging. The dynamic recrystallization grain simulation data includes the dynamic recrystallization volume fraction cloud map and grain size cloud map of the forging cross section during the forging deformation process and after the forging deformation of the heat-resistant steel forging.

2. The method according to claim 1, characterized in that, In step S2, the dynamic recrystallization model includes thermal deformation activation energy; The formula for solving the activation energy of the heat deformation is as follows: ;in, σ is the strain rate; A is the structural factor; α is the stress level parameter; σ is the true stress value; n is the stress-strain index; Q is the activation energy of thermal deformation; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature.

3. The method according to claim 1, characterized in that, Step S4 includes: S41, Based on the actual dimensions of the heat-resistant steel forging and the auxiliary tool, establish the geometric model of the heat-resistant steel forging and the auxiliary tool, and define the forging material according to the material file established in step S3; S42, mesh the geometric model and set the mesh re-meshing conditions and parameters to obtain the finite element mesh model; S43, Based on the actual forging process of the heat-resistant steel forging, set the relative position of the forging and the auxiliary tool, select the forging press and set the press parameters; S44, Determine the boundary conditions of the finite element mesh model during the forging simulation process; S45, simulate and calculate the forging process of the heat-resistant steel forging.

4. The method according to claim 3, characterized in that, In step S42, after the forging is meshed, the minimum value of the surface shape factor or volume shape factor of the mesh model of the forging is greater than 0.4; The mesh re-division condition is set as follows: mesh re-division is triggered based on deformation.

5. The method according to claim 3, characterized in that, In step S44, the boundary conditions include: the initial temperature of the forging and the auxiliary fixture, the heat exchange conditions and the friction conditions, and the heat exchange conditions between the forging and the external environment.

6. The method according to claim 5, characterized in that, The heat exchange conditions between the forging and the auxiliary tool are determined based on the contact type between the forging and the auxiliary tool and the pressure applied to the forging by the auxiliary tool.

7. The method according to claim 6, characterized in that, The heat exchange conditions include adiabatic conditions, heat exchange conditions under a pressure of 250 MPa, moderate interaction conditions between the forging and the rigid mold, strong interaction conditions between the forging and the rigid mold, and weak interaction conditions between the forging and the rigid mold. Wherein, if the heat-resistant steel forging is forged under the action conditions of a press with a pressure of 3000t~8000t, the interaction conditions between the forging and the rigid die are selected. If the heat-resistant steel forging is forged under the action conditions of a press with a pressure of 9000t~15000t, the condition of strong interaction between the forging and the rigid die is selected.

8. The method according to claim 1, characterized in that, Step S1 includes: S11, the heat-resistant steel to be analyzed is decomposed and prepared into multiple heat-resistant steel samples for hot compression tests; S12, the heat-resistant steel sample is subjected to a single-pass hot compression test at different deformation temperatures and different deformation rates. After the deformation of the heat-resistant steel sample reaches the preset value, the heat-resistant steel sample is quenched. S13, the heat-resistant steel sample after quenching is cut in half along its own axis, a metallographic sample is prepared along the cut surface, and a metallographic image of the heat-resistant steel sample is obtained. S14, Statistically analyze the grain size data of the heat-resistant steel samples under different deformation conditions; S15, obtain the test data of the hot compression test in step S12 and convert it into the actual stress and strain data of the heat-resistant steel.

9. The method according to any one of claims 1-8, characterized in that, Also includes: S6. Obtain the actual measured grain size data of the heat-resistant steel forging after forging, and compare the simulated dynamic recrystallization grain size data with the measured grain size data to verify the accuracy of the simulated dynamic recrystallization grain size data.

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