Prediction method for dynamic recrystallization law of crystal grains in forging process of heat-resistant steel forgings

Abstract

The embodiment of the invention relates to a method for predicting the dynamic recrystallization rule of crystal grains in the forging process of a heat-resistant steel forge piece, and belongs to the technical field of finite element simulation forging processes. The prediction method comprises the following steps: S1, performing a thermal compression test on a heat-resistant steel sample at different deformation temperatures and different strain rates to obtain stress-strain data and grain size data of the heat-resistant steel sample at different deformation temperatures and different strain rates; s2, fitting according to the stress-strain data of the heat-resistant steel sample to obtain a dynamic recrystallization model of the heat-resistant steel material; s3, establishing a material file of the heat-resistant steel material according to the dynamic recrystallization model; s4, a finite element simulation model of the heat-resistant steel forge piece is established according to the actual heat-resistant steel forge piece, the material file is input into the finite element simulation model, simulation parameters are set according to the actual forging process of the heat-resistant steel forge piece, and the forging forming process is simulated; and S5, extracting a dynamic recrystallization integral number cloud picture and a grain size cloud picture of the section of the heat-resistant steel forge piece.

Description

Technical Field

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

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

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

[0004] With the advancement of computer technology and numerical simulation methods, the prediction methods of dynamic recrystallization have also developed rapidly. The prediction methods of dynamic recrystallization mainly include various means such as prediction methods based on physical models and experimental research methods. The prediction method based on the Sellars-Tegart model and the Avrami equation mainly predicts the dynamic recrystallization process by establishing the kinetic curve of recrystallization through mathematical models; the experimental research method mainly obtains key data such as the material's rheological stress curve and microstructure evolution image by conducting hot deformation tests on the material under different conditions, thereby establishing a mathematical model of dynamic recrystallization. The above methods cannot intuitively reflect the dynamic recrystallization law of each position under the actual forging conditions of the forging, and cannot directly provide a reliable reference for the forging process formulation of large forgings. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method for predicting the dynamic recrystallization law of grains in the forging process of heat-resistant steel forgings, so as to solve the problem that the existing method cannot intuitively reflect the dynamic recrystallization law of each position under the actual forging conditions of the forgings.

[0006] An embodiment of the present invention provides a method for predicting the dynamic recrystallization law of grains in a forging process of a heat-resistant steel forging, comprising the following steps:

[0007] S1, hot compression test is performed on the heat-resistant steel sample at different deformation temperatures and different strain rates to obtain stress-strain data of the heat-resistant steel sample at different deformation temperatures and different strain rates and grain size data after hot compression test;

[0008] S2, according to the stress-strain data of the heat-resistant steel sample, the dynamic recrystallization model of the heat-resistant steel material is fitted;

[0009] S3, establishing a material file of the heat-resistant steel material according to a dynamic recrystallization model of the heat-resistant steel material;

[0010] S4, establishing a finite element simulation model of the heat-resistant steel forging according to the actual heat-resistant steel forging, inputting the material file into the finite element simulation model, and setting simulation parameters according to the actual forging process of the heat-resistant steel forging to simulate the forging forming process of the heat-resistant steel forging;

[0011] S5, extracting dynamic recrystallization grain simulation data of the heat-resistant steel forging, the dynamic recrystallization grain simulation data including a dynamic recrystallization volume fraction cloud map and a grain size cloud map of a cross section of the heat-resistant steel forging;

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

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

[0014] The critical strain model formula is: Among them, ε c is the critical strain value; Acrit is set to 1; d 0 is the initial grain size; Z is the Zener-Hollomon parameter; is the activation energy, E p1 、E p2 、E p3 、E p4 are all constants determined based on stress-strain data;

[0015] The strain model formula when dynamic recrystallization occurs 50% is:

[0016]

[0017] in, Q is the strain value when dynamic recrystallization occurs 50%; Td is the activation energy; T d1 , T d2 , T d3 , T d4 are all constants determined based on stress-strain data;

[0018] The dynamic recrystallization kinetic equation is: Among them, X D is the volume fraction of dynamic recrystallization, ε is the actual strain value, X d1 , X d2 , X d3 , X d4 is a constant determined from stress-strain data;

[0019] The dynamic recrystallization grain size model formula is: Among them, D d is the dynamic recrystallization grain size; Q Dd is the activation energy; D d1 , D d2 , D d3 , D d4 is a constant determined from the grain size data.

[0020] Further, step S4 includes:

[0021] S41, establishing a geometric model of the heat-resistant steel forging and the auxiliary tool according to the actual size of the heat-resistant steel forging and the auxiliary tool, and defining the forging material according to the material file established in step S3;

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

[0023] S43, according to the actual forging process of the heat-resistant steel forging, setting the relative position of the forging and the auxiliary tool, selecting a forging press and setting press parameters;

[0024] S44, determining boundary conditions of the finite element mesh model during the forging forming 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; and the mesh re-division condition is set as: triggering mesh re-division according to deformation.

[0027] Furthermore, in step S44, the boundary conditions include: initial temperature of the forging and the auxiliary tool, heat exchange conditions and friction conditions, and 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 according to the contact type between the forging and the auxiliary tool and the pressure exerted by the auxiliary tool on the forging.

[0029] Furthermore, the heat exchange conditions include adiabatic conditions, heat exchange conditions under a pressure of 250MPa, medium interaction conditions between forgings and rigid dies, strong interaction conditions between forgings and rigid dies, and weak interaction conditions between forgings and rigid dies; among them, if the heat-resistant steel forgings are forged under the conditions of a press of 3000t to 8000t, medium interaction conditions between forgings and rigid dies are selected; if the heat-resistant steel forgings are forged under the conditions of a press of 9000t to 15000t, strong interaction conditions between forgings and rigid dies are selected.

[0030] Further, 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, decomposing the heat-resistant steel to be analyzed into a plurality of heat-resistant steel samples for hot compression tests;

[0033] S12, performing a single-pass hot compression test on the heat-resistant steel sample at different deformation temperatures and different deformation rates, and performing a quenching treatment on the heat-resistant steel sample after the deformation amount of the heat-resistant steel sample reaches a preset value;

[0034] S13, cutting the heat-resistant steel sample after quenching treatment in half along its own axis direction, preparing a metallographic sample along the cut surface, and obtaining a metallographic image of the heat-resistant steel sample;

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

[0036] S15, obtaining the test data of the hot compression test in step S12 and converting it into real stress-strain data of the heat-resistant steel.

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

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

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

[0040] 1. The prediction method of the present invention establishes a dynamic recrystallization model of heat-resistant steel through hot compression test data under different deformation conditions, and establishes a material file containing the dynamic recrystallization model, which is imported into the finite element simulation model of the heat-resistant steel forging to simulate the forging deformation process, thereby obtaining a dynamic recrystallization volume fraction cloud map and a grain size cloud map of the cross section of the heat-resistant steel forging, which can accurately and intuitively reflect the dynamic recrystallization law of each position in the forging process of the heat-resistant steel forging. The prediction results can provide a reference for optimizing the forging process of the heat-resistant steel to improve product quality and production efficiency.

[0041] 2. The present invention aims at Forge software and establishes a dynamic recrystallization model suitable for Forge software according to the hot compression test data under different deformation conditions to ensure the smooth progress of the simulation process and the accuracy of the simulation results.

[0042] 3. Compared with the traditional test method, the prediction method of the present invention can reduce the number and cycle of tests, 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 the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0045] Figure 1 Schematic diagram of the flow of a method for predicting dynamic recrystallization rules according to some embodiments of the present invention.

[0046] Figure 2 Schematic diagram of the plastic part of the true stress-strain curve of the heat-resistant steel material according to some embodiments of the present invention.

[0047] Figure 3 Schematic diagram of the thermal compression process of the thermal compression test of the sample in Example 1.

[0048] Figure 4 These are metallographic images of the sample in Example 1 after hot compression tests at different deformation temperatures.

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

[0050] Figure 6 This is the true stress-strain curve of 9Cr3W3Co heat-resistant steel under different deformation conditions in Example 1.

[0051] Figure 7 is the relationship curve between strain rate and flow stress of 9Cr3W3Co heat-resistant steel in Example 1, wherein (a) is Curve (b) is curve.

[0052] Figure 8 (a) The 9Cr3W3Co heat-resistant steel of Example 1 at different deformation temperatures (a) The relationship between ln[sinh(ασ)] and T at different strain rates -1 relationship curve.

[0053] Fig. 9 This is a relationship curve between the work hardening rate θ and the true stress σ of the 9Cr3W3Co series heat-resistant steel of Example 1 at different heating temperatures.

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

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

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

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

[0058] Fig.14 These are measured metallographic images of different parts of the actual forging of Example 1 after forging, where (a) is the core of the forging, (b) is the R / 2 position of the forging, and (c) is the surface of the forging. DETAILED DESCRIPTION

[0059] In order to make the purpose, 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 only used to explain the present invention and are not intended to limit the present invention. For the sake of clarity and conciseness, not all features of the actual implementation are described in the specification.

[0060] The embodiment of the present invention provides a method for predicting the dynamic recrystallization law of grains in the forging process of heat-resistant steel forgings, such as Figure 1 As shown, the prediction method of the embodiment of the present invention includes the following steps:

[0061] S1, hot compression test is performed on the heat-resistant steel sample at different deformation temperatures and different strain rates to obtain stress-strain data of the heat-resistant steel sample at different deformation temperatures and different strain rates and grain size data after hot compression test;

[0062] S2, according to the stress-strain data of the heat-resistant steel sample, the dynamic recrystallization model of the heat-resistant steel is fitted;

[0063] S3, establishing the material file of heat-resistant steel forgings according to the dynamic recrystallization model of heat-resistant steel;

[0064] S4, establishing a finite element simulation model of the heat-resistant steel forging according to the actual heat-resistant steel forging, inputting the material file into the finite element simulation model, and setting simulation parameters according to the actual forging process of the heat-resistant steel forging to simulate the forging forming process of the heat-resistant steel forging;

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

[0066] The prediction method of the present invention establishes a dynamic recrystallization model of heat-resistant steel through hot compression test data under different deformation conditions, and establishes a material file containing the dynamic recrystallization model, which is imported into a finite element simulation model of a heat-resistant steel forging to simulate the forging deformation process, thereby obtaining a dynamic recrystallization volume fraction cloud map and a grain size cloud map of the cross section of the heat-resistant steel forging, which can accurately and intuitively reflect the dynamic recrystallization law of each position in the forging process of the heat-resistant steel forging, and the prediction result can provide a reference for optimizing the forging process of the heat-resistant steel to improve product quality and production efficiency.

[0067] Compared with traditional test methods, the prediction method of the present invention can reduce the number and cycle of tests, 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.

[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 dynamic recrystallization occurs 50%, and dynamic recrystallization grain size model;

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

[0070] The critical strain model formula is: Among them, ε c is the critical strain value; Acrit is set to 1; d 0 is the initial grain size; Z is the Zener-Hollomon parameter; is the activation energy, E p1 、E p2 、E p3 、E p4 are all constants determined based on stress-strain data;

[0071] The strain model formula when dynamic recrystallization occurs 50% is:

[0072]

[0073] in, Q is the strain value when dynamic recrystallization occurs 50%; Td is the activation energy; T d1 , T d2 , T d3 , T d4 are all constants determined based on stress-strain data;

[0074] The dynamic recrystallization kinetic equation is: Among them, X D is the volume fraction of dynamic recrystallization, ε is the actual strain value, X d1 , X d2 , X d3 , X d4 is a constant determined from stress-strain data;

[0075] The dynamic recrystallization grain size model formula is: Among them, D d is the dynamic recrystallization grain size; Q Dd is the activation energy; D d1 , D d2 , D d3 , D d4 is a constant determined from the grain size data.

[0076] In an embodiment of the present invention, Forge software is used to simulate the forging deformation process of heat-resistant steel forgings. 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 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, their initial grain size and Zener-Hollomon parameters have little effect on the dynamic recrystallization of the forging deformation process compared to the deformation temperature and deformation rate. Therefore, the present invention does not consider the effect of the initial grain size and Zener-Hollomon parameters on the dynamic recrystallization model, and the coefficient E in the above dynamic recrystallization model is p2 、E p4 , T d2 , T d4 It is set to 0; and there is no need to conduct hot compression tests under different initial grain sizes, which reduces the number of tests and improves the prediction efficiency.

[0078] In some embodiments, step S1 includes:

[0079] S11, decomposing the heat-resistant steel to be analyzed into a plurality of heat-resistant steel samples for hot compression tests;

[0080] S12, performing a single-pass hot compression test on the heat-resistant steel sample at different deformation temperatures and different deformation rates, and performing a quenching treatment on the heat-resistant steel sample after the deformation amount of the heat-resistant steel sample reaches a preset value;

[0081] S13, cutting the heat-resistant steel sample after quenching treatment in half along its own axis direction, preparing a metallographic sample along the cut surface, and obtaining a metallographic image of the heat-resistant steel sample;

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

[0083] S15, obtaining the test data of the hot compression test in step S12 and converting it into real stress-strain data of the heat-resistant steel.

[0084] Specifically, a Gleeble thermal simulation tester may be used to perform a thermal compression test. In step S11, a plurality of Gleeble thermal compression specimens need to be prepared.

[0085] In step S13, the heat-resistant steel sample cut in half is ground, polished and corroded 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 counted based on the Image-Pro Plus software. For example, two intersecting straight lines can be drawn on the metallographic diagram, and at least 50 intercept points (the intercept point is the intersection of the straight line and the grain boundary) can be intercepted on the two straight lines, the number of intercept points obtained by the grid is measured and the grain size is determined, and the average value of the grain size obtained by the two straight lines is the grain size of the corresponding field of view of the metallographic diagram; the grain size of multiple fields of view of the same heat-resistant steel sample is calculated by this method and the average value 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 real 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, in step S2, the process of determining the thermal deformation activation energy is as follows:

[0089] Establish the constitutive equation of heat-resistant steel material, the material rheological stress σ and strain rate The relationship between and temperature T can be expressed as:

[0090]

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

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

[0093] In the formula, A1, A2, n 1 , β are constants, among which

[0094] Taking the logarithm of both sides of equations (2) and (3) we can obtain:

[0095]

[0096] From equations (4) and (5), we can see that when the temperature is constant, n 1 and β are and The slope of the relationship curve can be obtained by extracting the peak stress of the heat-resistant steel from the stress-strain data obtained by the hot compression test. and Plot the points and perform linear regression analysis on the two to find the average slope, and we can get n 1 , β, and α.

[0097] On this basis, assuming that the thermal deformation activation energy Q is independent of the temperature T, taking the logarithm of both sides of equation (1) yields:

[0098]

[0099] Substitute the α value into equation (6) and plot the Relationship curve and ln[sinh(ασ)]-T at different strain rates -1 The relationship curve is obtained by linear regression fitting of the two to obtain the average value of the slope, and the values ​​of n and Q / nR can be obtained, thereby the value of the thermal deformation activation energy Q can be obtained.

[0100] In some embodiments, the critical strain model is determined in step S2 as follows:

[0101] The critical strain is expressed by the θ-σ curve (work hardening rate ). The work hardening rate at different deformation temperatures is calculated by fitting the stress-strain curve with differential polynomial regression, and then the relationship curve between the work hardening rate θ and the true stress σ at different deformation temperatures (θ-σ curve) is obtained. Among them, the intersection of the θ-σ curve and the horizontal axis (that is, the point where the work hardening rate θ is zero) is the peak stress, the inflection point in the curve (that is, the point where the absolute slope of the curve is the smallest) is the critical value for the start of dynamic recrystallization, and 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.

[0102] The relationship between the critical strain of the material and parameters such as initial grain size, strain rate, Z parameter and temperature is expressed by the following equation:

[0103]

[0104] The initial grain size d is not considered. 0 The influence of E and Z parameters on the critical strain p2 、E p4 Set to 0.

[0105] Take the logarithm of both sides of the above equation, and substitute different deformation temperatures, strain rates and corresponding critical strain values ​​to obtain E P1 、E P3 and Then the critical strain model formula of heat-resistant steel is obtained.

[0106] In some embodiments, the strain model determination process when dynamic recrystallization occurs 50% in step S2 is as follows:

[0107] The strain at which dynamic recrystallization occurs at 50% is the strain corresponding to the maximum softening rate. Its value can be determined by the relationship curve between the work hardening rate θ and the true strain ε. The strain corresponding to the work hardening rate θ equals 0 is the peak strain ε. p and the steady-state strain ε s , and the strain corresponding to the minimum value of the work hardening rate θ is the strain ε when the maximum softening rate is reached 0.5 .

[0108] The relationship between the strain at which 50% of dynamic recrystallization of heat-resistant steel occurs and the initial grain size, strain rate, Z parameter, and temperature is expressed by the following equation:

[0109]

[0110] Where T d1 , T d2 , T d3 , T d4 are all constants, regardless of the initial grain size and the effect of Z parameter on the strain at 50% of dynamic recrystallization. The influence of T d2 , T d4 Set to 0.

[0111] Take the logarithm of both sides of the above equation and substitute different deformation temperatures, strain rates and corresponding critical strain values ​​to obtain T d1 , T d3 and Q Td , and obtain the strain model when dynamic recrystallization of heat-resistant steel material occurs 50%.

[0112] In some embodiments, the process of determining the dynamic recrystallization kinetic equation in step S2 is as follows:

[0113] The dynamic recrystallization volume fraction is given by Figure 2 The relationship between the dynamic recrystallization volume fraction and the thermorheological stress parameters is determined by the true stress-strain curve shown in the figure:

[0114]

[0115] Where, X D is the volume percentage of dynamic recrystallization; σ WH is the extension of the stress of the work hardening part, which can be obtained by ε<ε c The stress-strain data of the stage are obtained by extrapolating the mathematical model of the dynamic recovery rheological curve; σ is the instantaneous stress; σ s is the saturation stress; σ ss is the steady-state stress; σ and σ ss It can be determined based on the true stress-strain curve.

[0116]

[0117] In the formula, σ 0 is the yield stress, ε is the actual strain, and Ω is the dynamic recovery softening amount.

[0118] In some embodiments, the yield stress σ 0 The method for obtaining is: linearly fit the elastic part of the true stress-strain curve, whose slope corresponds to the elastic modulus G, draw a straight line y = G (ε-0.002) + intercept, and the stress at the intersection of this straight line and the true stress-strain curve is the yield stress σ 0 ; Saturation stress σ s It can be based on the peak stress σ p Determine, specific, σ s =σ p *1.1.

[0119] In determining the yield stress σ 0 , saturation stress σ s After that, Ω and σ can be determined according to equation (8) or (9): WH Specifically, the stress-strain value of any point in the plastic part of the true stress-strain curve where the strain value is less than the critical strain value is substituted into equation (8) or (9), and the Ω value and σ can be obtained. WH value.

[0120] Then, according to the relationship between the dynamic recrystallization fraction and the thermorheological stress parameter (7), the volume fraction of dynamic recrystallization can be determined.

[0121] Furthermore, in determining the dynamic recrystallization volume fraction data, critical strain ε c After that, according to X D , ε c and the actual strain ε, to determine the dynamic recrystallization kinetic equation:

[0122]

[0123] Where, X D is the volume fraction of dynamic recrystallization, X d1 , X d2 , X d3 , X d4 As a constant, take the logarithm of both sides of the above formula, and transform the strain ε and critical strain ε under different deformation conditions into c and the corresponding dynamic recrystallization volume fraction X D Substituting in, we can determine X d1 , X d2 , X d3 , X d4 The value of is then used to determine the dynamic recrystallization kinetic equation.

[0124] 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 dynamic recrystallization grain size model formula is as follows:

[0125]

[0126] Among them, D d1 , D d2 , D d3 , D d4 is a constant. The present invention does not consider the influence of the initial grain size and the Z parameter on the dynamic recrystallization grain size. d2 , D d4 Set to 0.

[0127] Take the logarithm of the two sides and substitute the grain size under different deformation conditions measured in step S14 to determine D d1 , D d3 and Q Dd , and then determine the dynamic recrystallization grain size model.

[0128] After the dynamic recrystallization model is determined, step S3 may be performed to establish a heat-resistant steel material file suitable for Forge software according to the dynamic recrystallization model determined by fitting, so as to ensure the accuracy of the simulation results.

[0129] Among them, the material file includes dynamic recrystallization models such as thermal deformation activation energy, critical strain model, dynamic recrystallization kinetic equation, dynamic recrystallization grain size model, etc. of heat-resistant steel materials determined by the above method based on hot compression test data.

[0130] In addition, the material file also includes the thermal property data of the heat-resistant steel material, including thermal conductivity, thermal expansion coefficient, Young's modulus, Poisson's ratio, plastic modulus, etc. In some embodiments, the thermal property data of the heat-resistant steel material can be measured by experiment or calculated based on JMatPro software.

[0131] In some embodiments, step S4 includes:

[0132] S41, establishing a geometric model of the heat-resistant steel forging and the auxiliary tool according to the actual size of the heat-resistant steel forging and the auxiliary tool, and defining the forging material according to the material file established in step S3;

[0133] S42, meshing the geometric model and setting mesh re-division conditions and parameters to obtain a finite element mesh model;

[0134] S43, according to the actual forging process of the heat-resistant steel forging, setting the relative position of the forging and the auxiliary tool, selecting a forging press and setting press parameters;

[0135] S44, determining boundary conditions of the finite element mesh model during the forging forming simulation process;

[0136] S45, simulates the forging process of heat-resistant steel forgings.

[0137] The present invention can simulate the actual forging process of the heat-resistant steel forging by establishing a finite element model according to the actual heat-resistant steel forging and performing simulation calculation of the forging forming process according to the actual forging process, so as to facilitate the prediction of the dynamic recrystallization law of the actual forging process of the heat-resistant steel forging.

[0138] In step S41, the geometric model of the heat-resistant steel forging and the auxiliary tool can be a two-dimensional model or a three-dimensional model. The geometric model is imported into the Forge software, and then the material file created in step S3 is also imported into the Forge software to define the forging material.

[0139] Among them, the auxiliary tools are molds or tools used in the forging process, including but not limited to one or more of flat anvils, V-anvils, wide flat anvils, wide platforms, horse bars, upsetting plates, punches, and leak plates.

[0140] In step S42, when meshing the geometric model, the Forge software can identify the size of the geometric body and give a recommended mesh size, including: coarse mesh, medium mesh, fine mesh, fine mesh, etc. In some embodiments, the mesh size can be selected according to the mesh size recommended by the Forge software; in addition, the experimenter can also set the mesh size by himself. Among them, the mesh model of the forging can be set according to the fine mesh or fine mesh, and the mesh model of the rigid auxiliary tool can select a coarse mesh to speed up the calculation as much as possible.

[0141] In some embodiments, in step S42, after the forging is meshed, it is necessary to check the surface shape factor (2D) or volume shape factor (3D) of the forging mesh model 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 mesh with better quality is obtained, which is conducive to improving the accuracy of the simulation calculation results.

[0142] In some embodiments, when setting the mesh re-division conditions and parameters in step S42, the mesh re-division conditions can be set as: triggering mesh re-division according to deformation; setting parameters includes: setting the volume size factor to 1.1 or 2. The value of the volume size factor will affect the size of the mesh inside the forging, and the volume size factor indicates the multiple 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, the present invention can adjust the density and shape of the mesh during the forging deformation process to adapt to the dynamic changes of the forging and improve the calculation accuracy.

[0143] In some embodiments, in step S43, press parameters are set according to the actual forging process of the heat-resistant steel forging to be simulated, wherein the forging process includes but is not limited to: one or more of upsetting, drawing, extrusion, punching, and expanding.

[0144] 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 procedures, the press parameters corresponding to each forging procedure are set according to the actual forging procedure.

[0145] In some embodiments, the boundary conditions in step S44 include: initial temperature of the forging and the auxiliary tool, heat exchange conditions and friction conditions, and heat exchange conditions between the forging and the external environment.

[0146] The initial temperature of the forging and the auxiliary tool can be set according to the initial temperature during actual forging. The heat exchange conditions between the forging and the auxiliary tool are determined according to the type of action between the forging and the auxiliary tool and the pressure applied by the auxiliary tool to the forging.

[0147] Furthermore, the heat exchange conditions include adiabatic conditions, heat exchange conditions under a pressure of 250 MPa, medium interaction conditions between forgings and rigid molds, strong interaction conditions between forgings and rigid molds, and weak interaction conditions between forgings and rigid molds. When using Forge software for simulation calculations, you can choose from the above heat exchange conditions. The greater the applied pressure, the greater the heat transfer coefficient corresponding to the selected heat exchange condition.

[0148] Specifically, the selection rules are as follows:

[0149] (1) When the influence of the auxiliary tool temperature on the temperature change of the forging is not of concern, the adiabatic condition can be selected. For example, when simulating the compression process of a hot compression specimen, the specimen is heated to a certain temperature by a resistor for a short time and kept warm. The resistance heating state is also maintained during the compression process. At this time, the specimen can be simplified to an adiabatic model.

[0150] (2) The heat exchange condition under 250MPa pressure state (ALphaT Pressure 250MPa) is suitable for the process state where the forging is under short-term high contact pressure.

[0151] (3) If the heat-resistant steel forging is forged under the conditions of a 3000t to 8000t press, the medium interaction condition between the forging and the rigid die (Medium Interaction with Steel Dies) is selected, and the corresponding heat transfer coefficient is 10000W / m 2· K. For example, if the forging is forged under the action conditions of a 4000t oil press or a 6000t hydraulic press, medium interaction conditions can be selected.

[0152] (4) If the heat-resistant steel forging is forged under the conditions of a 9000t to 15000t press, the strong interaction condition between the forging and the rigid die (Strong Interaction with Steel Dies) is selected, and the corresponding heat transfer coefficient is 20000W / m 2 · K. For example, if the forging is forged under the action of a 10,000t or 15,000t hydraulic press, strong interaction conditions can be selected.

[0153] (5) If the interaction between the heat-resistant steel forging and the auxiliary tool is small, or even no pressure is applied to the forging, for example, the heat-resistant steel forging is forged under the condition of a press less than or equal to 2000t, or there is no force between the forging and the auxiliary tool, the weak interaction condition between the forging and the rigid die (Weak Interaction with Steel Dies) can be selected, and the corresponding heat transfer coefficient is 2000W / m 2 ·K.

[0154] Furthermore, when the working environment during forging is air, the heat exchange condition between the forging and the external environment can be selected as air heat exchange condition (Heat Transfer with Air), and the corresponding heat transfer coefficient is 10W / m 2 ·K.

[0155] In some embodiments, the friction condition can be selected based on experience according to the contact type between the forging and the auxiliary tool, or the friction condition recommended by the Forge software can be selected. The 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, Viscoplastic 0.5.

[0156] Among them, bilateral sliding contact means that the nodes of the forging and the auxiliary tool can slide on the contact surface between the forging and the auxiliary tool, but cannot leave the contact surface.

[0157] Bilateral Sticking Contact means that the nodes of the forging and the auxiliary tool are bonded to each other on the contact surface.

[0158] Friction with No Lubrication is applicable to friction without lubrication. When Friction with No Lubrication is selected, the friction coefficient of the Coulomb model is 0.4 and the friction coefficient of the Tresca model is 0.8.

[0159] 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.

[0160] When High Friction Conditions is selected, the friction coefficient of the Coulomb model is 0.3 and the friction coefficient of the Tresca model is 0.6.

[0161] 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.

[0162] 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.

[0163] There is no contact friction coefficient for No Contact Condition and Sliding Contact; the friction coefficient for Tresca High is 0.45; the friction coefficient for Tresca Very High is 0.8; the friction coefficient for Viscoplastic friction model is 0.3; and the friction coefficient for Viscoplastic 0.5 is 0.5.

[0164] In addition, the boundary conditions also include: manipulator motion conditions. In step S44, the manipulator motion conditions can be set according to the motion state of the real manipulator during actual forging. It should be noted that the manipulator is used to control the motion of the forging during the forging process, for example, to control the rotation of the forging.

[0165] After completing the settings of meshing, boundary conditions, forging process, and press parameters, the model can be saved and submitted for calculation to simulate the actual forging process of heat-resistant steel forgings.

[0166] In some embodiments, in 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 a cross section of the heat-resistant steel forging after the forging deformation is completed. According to the dynamic recrystallization volume fraction cloud map and the grain size cloud map of the cross section of the forging after the forging deformation, the dynamic recrystallization law of the heat-resistant steel forging during the forging deformation process can be predicted.

[0167] Furthermore, the 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 also 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, so as to explore the change law of dynamic recrystallization during the forging process and provide a reference for designing the forging process.

[0168] In some embodiments, Figure 1 As shown, the prediction method also includes:

[0169] S6, obtaining the actual measured grain size data of the heat-resistant steel forgings after forging, comparing the dynamic recrystallization grain simulation data with the measured grain size data to verify the accuracy of the dynamic recrystallization grain simulation data, and thus ensure the accuracy of the dynamic recrystallization law prediction.

[0170] The following is a further description of the method for predicting the dynamic recrystallization law of grains during the forging process of heat-resistant steel forgings of the present invention through specific embodiments.

[0171] Example 1

[0172] This embodiment predicts the dynamic recrystallization law of grains during the forging process of 9Cr3W3Co series heat-resistant steel forgings. The chemical composition range of the main elements of the heat-resistant steel forgings is specifically shown in Table 1.

[0173] Table 1 Chemical composition range of 9Cr3W3Co series heat-resistant steel (ωt, %)

[0174] C Si Mn Cr Co V W 0.06-0.09 ≤0.30 0.45-0.55 8.50-9.5 2.50-3.50 0.15-0.25 2.5-3.5

[0175] The prediction method includes the following steps:

[0176] S1, perform a hot compression test on the above heat-resistant steel alloy to obtain its true stress-strain data and grain size data after the hot compression test:

[0177] S11, decomposing the heat-resistant steel alloy and preparing 20 samples for Gleeble test;

[0178] S12, the sample prepared in step S11 is Figure 3 The hot compression process shown in the figure is a single-pass hot compression test. Specifically, the sample is heated to 1200℃ at a speed of 10℃ / s and then kept at this temperature for 5 minutes; then cooled to the required deformation temperature of each sample at a speed of 3℃ / s, which is 1000℃, 1050℃, 1100℃, 1150℃, and 1200℃ in sequence, and kept at each deformation temperature for 30s; then hot compression deformation is performed by 60% at the required deformation rate, where the deformation rate is 0.01s -1 , 0.1s -1 , 1.0s -1 , 10.0s-1 When the deformation of the sample reaches 60% of the preset value, the sample is immediately quenched using water cooling.

[0179] S13, the quenched sample is cut in half along its own axis direction, and the cut sample is ground, polished, and etched along the cut surface to prepare a metallographic sample, and a metallographic image with clear grain boundaries of the metallographic sample is obtained, such as Figure 4 shown.

[0180] S14, using Image-Pro Plus software, counting the grain sizes under different deformation conditions in each metallographic image obtained in step S13. Specifically, for a certain field of view of a metallographic image of a certain sample, such as Figure 5 As shown, two intersecting straight lines are drawn on the metallographic image, the number of intercept points on the straight lines is measured and the grain size is determined, and the average value of the grain sizes obtained by the two straight lines is taken as the grain size of the corresponding field of view of the metallographic image.

[0181] S15, obtaining the test data of the thermal compression test in step S12, and converting it into real stress-strain data, such as Figure 6 shown.

[0182] S2, determining the dynamic recrystallization model of 9Cr3W3Co series heat-resistant steel under the above deformation conditions according to the real stress-strain data obtained in step S1.

[0183] (1) Thermal deformation activation energy:

[0184] Material flow stress σ and strain rate The relationship between and temperature T can be expressed as:

[0185]

[0186] At low stress levels, equation (1) can be expressed as:

[0187] At high stress levels, equation (1) can be expressed as:

[0188] In the formula, is the strain rate; A is the structure factor; α is the stress level parameter; σ is the true stress value; n is the stress-strain exponent; Q is the thermal deformation activation energy; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature; A1, A2, n 1 , β are constants, among which

[0189] Taking the logarithm of both sides of equations (2) and (3) we can obtain:

[0190]

[0191] From equations (4) and (5), we can see that when the temperature is constant, n 1 and β are and The slope of the relationship curve. From this, we can extract Figure 6 The peak stress of 9Cr3W3Co steel in the real stress-strain data shown is and Plot points, such as Figure 7 As shown, linear regression analysis is performed on the two to find the average slope, and n 1 =5.22952,β=0.06632,

[0192] On this basis, assuming that the thermal deformation activation energy Q is independent of the temperature T, taking the logarithm of both sides of equation (1) yields:

[0193]

[0194] Substitute the α value into equation (6) and plot the Relationship curve and ln[sinh(ασ)]-T at different strain rates -1 Relationship curve, such as Figure 8 As shown, the linear regression fitting is performed on the two to obtain the average slope, and n = 3.85146 is obtained. Therefore, the thermal deformation activation energy Q = 496.414 KJ / mol can be obtained.

[0195] (2) Critical strain model:

[0196] The relationship curve between the work hardening rate θ and the true stress σ at different heating temperatures is calculated by fitting the stress-strain curve with differential polynomial regression, as shown in Fig. 9 As shown. According to the θ-σ curve, the critical strain value is determined: 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 the smallest) is the critical value for the start of dynamic recrystallization. Fig. 9 It can be seen that the work hardening rate θ decreases sharply with the increase of deformation in the initial stage of deformation until it decreases to the 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 drops to 0, and the horizontal axis corresponds to the peak stress value.

[0197] The relationship between the critical strain of the material and parameters such as initial grain size, strain rate, Z parameter and temperature is expressed by the following equation:

[0198]

[0199] In the formula, ε c is the critical strain value; Acrit is set to 1; d 0 is the initial grain size, and the effect of the initial grain size on the critical strain is not considered here; Z is the Zener-Hollomon parameter, and its effect on the critical strain is not considered here; is the activation energy, E p1 、E p2 、E p3 、E p4 are constants, E p2 、E p4 Set to 0.

[0200] Taking the logarithm of both sides of the above equation and substituting different deformation temperatures, strain rates and corresponding critical strain values, the critical strain model formula of heat-resistant steel can be obtained:

[0201]

[0202] (3) Strain model when dynamic recrystallization occurs at 50%:

[0203] Similar to the method of determining the critical strain, the relationship curve between the work hardening rate θ and the true strain ε at different heating temperatures is calculated by fitting the stress-strain curve with differential polynomial regression. In the θ-ε relationship curve, the strain corresponding to the work hardening rate θ equals 0 is the peak strain ε p and the steady-state strain ε s , the corresponding strain when the work hardening rate θ is the minimum is the strain ε when the dynamic recrystallization occurs 50% when the maximum softening rate is reached 0.5 .

[0204] The strain at which dynamic recrystallization of the material occurs 50% The relationship between initial grain size, strain rate, Z parameter and temperature is expressed by the following equation:

[0205]

[0206] Where, T d1 , T d2 , T d3 , T d4 are constants, Q Td is the activation energy. The initial grain size and Zener-Hollomon parameter are not considered to affect the strain at which dynamic recrystallization occurs by 50%. The influence of d2 , T d4 Set to 0.

[0207] By taking the logarithm of both sides of the above equation and substituting different deformation temperatures, deformation rates and corresponding critical strain values, the strain model when the dynamic recrystallization of the material occurs at 50% can be obtained:

[0208]

[0209] (4) Dynamic recrystallization kinetic equation:

[0210] The relationship between the dynamic recrystallization volume fraction and the thermorheological stress parameters is:

[0211]

[0212] Where, X D is the volume percentage of dynamic recrystallization, σ WH is the extension of the stress of the work hardening part, which can be obtained by ε<ε c The stress-strain data of the stage are obtained by extrapolating the mathematical model of the dynamic recovery rheological curve, where σ is the instantaneous stress and σ s is the saturation stress, σ ss is the steady-state stress, σ and σ ss Determined based on the true stress-strain curve.

[0213] σ WH =[σ s 2 +(σ 0 2 -σ s 2 ) -Ωε ] 0.5 (8);

[0214]

[0215] By linearly fitting the elastic part of the true stress-strain curve, the slope corresponds to the elastic modulus G, and a straight line y = G (ε-0.002) + intercept is drawn. The stress at the intersection of the straight line and the true stress-strain curve is the yield stress σ 0 ; Saturation stress σ s =σ p *1.1.

[0216] In determining the yield stress σ 0 , saturation stress σ s Then, substitute the stress-strain value of 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 Then, according to the relationship between the dynamic recrystallization fraction and the thermo-rheological stress parameter (7), the volume fraction of dynamic recrystallization is determined.

[0217] The dynamic recrystallization kinetic equation is:

[0218]

[0219] Where, X D is the volume fraction of dynamic recrystallization, X d1 , X d2 , X d3 , X d4 As a constant, take the logarithm of both sides of the above formula, and transform the strain ε and critical strain ε under different deformation conditions into c And the corresponding dynamic recrystallization volume fraction are substituted into the equation, and the dynamic recrystallization kinetic equation can be determined as:

[0220]

[0221] (5) Dynamic recrystallization grain size model:

[0222] The dynamic recrystallization grain size model formula is as follows:

[0223]

[0224] Among them, D d1 , D d2 , D d3 , D d4 Assuming D d2 , D d4 Set to 0.

[0225] By taking the logarithm of the two sides and substituting the grain sizes under different deformation conditions measured in step S14, the dynamic recrystallization grain size model can be obtained as follows:

[0226]

[0227] S3, establishing a material file of 9Cr3W3Co series heat-resistant steel according to the dynamic recrystallization model fitted in step S2.

[0228] S4, simulation of forging deformation process of 9Cr3W3Co series heat-resistant steel forgings:

[0229] S41, establish the initial geometric model of forgings and auxiliary tools, such as Fig.10 The initial geometric model and the material file created in step S3 are imported into the Forge software.

[0230] S42, select the fine grid size in the Forge software, divide the initial geometric model into grids, and obtain the initial grid model, such as Fig.11As shown in the figure, set the mesh to trigger the re-division condition as the forging deforms, and set the volume size factor to 2, that is, the mesh elements inside the forging will be refined twice.

[0231] S43, set the relative position of the forging and the auxiliary tool according to the actual forging process, select a suitable forging press and set the press parameters according to the actual forging process:

[0232] The forging process simulated in this embodiment includes two steps of upsetting and drawing. The forging forming process is completed in two steps of one fire and one time, and a 4000t press is selected.

[0233] 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 rate is set to 10mm / s, and the maximum pressure is 4000t. When the maximum pressure of the press is reached, the pressure is maintained and continued to press down until the stroke requirement is met.

[0234] The second process is the wide flat anvil stretching process. The model after the upsetting process is directly exported as the initial model of the wide flat anvil stretching process. The auxiliary tools are the upper wide flat anvil and the lower platform. The press stroke is set to a multi-pass pressing stroke, with a total stroke of about 320mm, a pressing rate of 20mm / s, and a maximum pressure of 4000t. The movement of the auxiliary tools and the billet is defined in the multi-pass file; specifically, in order to ensure that the billet is deformed evenly when the auxiliary tool is pressed down, and its shape change does not cause excessive displacement of the center of mass, affecting the final forging shape, the movement of each pass is defined as: each time the upper wide flat anvil is pressed down, the billet rotates 37.5° around the axis corresponding to its center of mass, and the pass ends after ten presses; each time a pass is completed, the upper and lower auxiliary tools move 30mm toward the billet respectively, and the movement of each pass is continued until the final stroke of the upper wide flat anvil meets the setting requirements.

[0235] S44, determine the boundary conditions of the forging simulation process:

[0236] The settings are made according to the actual forging conditions of the forgings, where the initial temperatures of the forgings and the auxiliary tools are 1200℃ and 250℃ respectively; the heat exchange condition between the forgings and the auxiliary tools selects the medium interaction "Medium Interaction with Steel Dies", and the friction condition between the two selects the friction under water and graphite lubrication conditions "Friction with Water and Graphite Lubrication"; the ambient temperature is set to 50℃, and the heat exchange condition between the forgings and the environment selects the air heat exchange condition "Heat Transfer with Air"; and according to the motion state of the real manipulator, the motion of the manipulator is set to be able to rotate around the axis but not move in the axial direction.

[0237] Save the model and submit the calculation.

[0238] S5, obtain the dynamic recrystallization volume fraction cloud map and average grain size cloud map of the forging section after forging, such as Fig.12 and Fig.13 As shown, the dynamic recrystallization law of the forging during the entire forging process is explored.

[0239] like Fig.12 As shown in Figure 2, the volume fraction of dynamic recrystallization is larger in the forging core, at R / 2, and near the surface. Fig.13 As shown, the dynamically recrystallized grain size near the center of the forging and at R / 2 is smaller, and the grain size on the surface is the largest.

[0240] S6, obtain the grain size of the measured forgings and verify the accuracy of the simulation law.

[0241] like Fig.14 As shown, the grains at R / 2 of the actual forging dissection are the finest, the grains in the center are coarser, and the grains on the surface are the coarsest. Among them, the coarser grains in the center are mainly affected by the temperature rise in the center during the deformation process. 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.

[0242] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting the dynamic recrystallization law of grains in the forging process of heat-resistant steel forgings, characterized in that: The following steps are involved: S1, performing a hot compression test on a heat-resistant steel sample at different deformation temperatures and different strain rates to obtain stress-strain data of the heat-resistant steel sample at different deformation temperatures and different strain rates and grain size data after the hot compression test; 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 a dynamic recrystallization model of the heat-resistant steel material; S4, establishing a finite element simulation model of the heat-resistant steel forging according to the actual heat-resistant steel forging, inputting the material file into the finite element simulation model, and setting simulation parameters according to the actual forging process of the heat-resistant steel forging to simulate the forging forming process of the heat-resistant steel forging; S5, extracting dynamic recrystallization grain simulation data of the heat-resistant steel forging, wherein the dynamic recrystallization grain simulation data includes a dynamic recrystallization volume fraction cloud map and a grain size cloud map of a cross section 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, critical strain model, dynamic recrystallization kinetic equation, strain model when dynamic recrystallization occurs 50%, and dynamic recrystallization grain size model; The solution formula for the thermal deformation activation energy is: in, is the strain rate; A is the structure factor; α is the stress level parameter; σ is the true stress value; n is the stress-strain exponent; Q is the thermal deformation activation energy; R is the gas constant, R = 8.314 J / (mol·K); T is the absolute temperature; The critical strain model formula is: Among them, ε c is the critical strain value; Acrit is set to 1; d0 is the initial grain size; Z is the Zener-Hollomon parameter; is the activation energy, E p1 、E p2 、E p3 、E p4 are all constants determined according to the stress-strain data; The strain model formula when the dynamic recrystallization occurs at 50% is: in, Q is the strain value when dynamic recrystallization occurs 50%; Td is the activation energy; T d1 , T d2 , T d3 , T d4 are all constants determined according to the stress-strain data; The dynamic recrystallization kinetic equation is: Among them, X D is the volume fraction of dynamic recrystallization, ε is the actual strain value, X d1 , X d2 , X d3 , X d4 is a constant determined according to the stress-strain data; The dynamic recrystallization grain size model formula is: Among them, D d is the dynamic recrystallization grain size; Q Dd is the activation energy; D d1 , D d2 , D d3 , D d4 is a constant determined according to the grain size data.

3. The method according to claim 1, characterized in that Step S4 includes: S41, establishing a geometric model of the heat-resistant steel forging and the auxiliary tool according to the actual sizes of the heat-resistant steel forging and the auxiliary tool, and defining the forging material according to the material file established in step S3; S42, meshing the geometric model, and setting mesh re-meshing conditions and parameters to obtain a finite element mesh model; S43, according to the actual forging process of the heat-resistant steel forging, setting the relative position of the forging and the auxiliary tool, selecting a forging press and setting press parameters; S44, determining the boundary conditions of the finite element mesh model during the forging forming simulation process; S45, performing simulation calculation on the forging forming 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 according to deformation.

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

6. The method according to claim 5, characterized in that The heat exchange condition between the forging and the auxiliary tool is determined according to 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, medium interaction conditions between forgings and rigid dies, strong interaction conditions between forgings and rigid dies, and weak interaction conditions between forgings and rigid dies; Wherein, if the heat-resistant steel forging is forged under the action condition of a press of 3000t to 8000t, a medium interaction condition between the forging and the rigid die is selected; If the heat-resistant steel forging is forged under the condition of a press with a force of 9000t to 15000t, a strong interaction condition between the forging and the rigid die is selected.

8. The method according to claim 1, characterized in that Step S1 includes: S11, decomposing the heat-resistant steel to be analyzed into a plurality of heat-resistant steel samples for hot compression tests; S12, performing a single-pass hot compression test on the heat-resistant steel sample at different deformation temperatures and different deformation rates, and performing a quenching treatment on the heat-resistant steel sample after the deformation amount of the heat-resistant steel sample reaches a preset value; S13, cutting the heat-resistant steel sample after quenching treatment in half along its own axial direction, preparing a metallographic sample along the cut surface, and obtaining a metallographic image of the heat-resistant steel sample; S14, collecting data on the grain size of the heat-resistant steel sample under different deformation conditions; S15, obtaining the test data of the hot compression test in step S12 and converting it into real stress-strain data of the heat-resistant steel.

9. The method according to any one of claims 1 to 8, characterized in that: In step S5, extracting the dynamic recrystallization grain simulation data includes: extracting a dynamic recrystallization volume fraction cloud map and a grain size cloud map of a cross section of the heat-resistant steel forging during and after the forging deformation.

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

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