CN-120030831-B - Prediction method for dynamic recrystallization rule of crystal grains in forging process of heat-resistant steel forging

CN120030831BCN 120030831 BCN120030831 BCN 120030831BCN-120030831-B

Abstract

The embodiment of the invention relates to a prediction method of a dynamic recrystallization rule of crystal grains in a forging process of a heat-resistant steel forging, belonging to the technical field of finite element simulation forging processes. The prediction method comprises the steps of S1, carrying out thermal compression tests 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 to obtain a dynamic recrystallization model of a 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, setting simulation parameters according to an actual forging process of the heat-resistant steel forging, and carrying out simulation of a forging forming process, and S5, extracting a dynamic recrystallization volume fraction cloud image and a grain size cloud image of a section of the heat-resistant steel forging.

Inventors

LI HAN
ZHANG XUEJIAO
LI XIAO
YANG KANG
HUO JIE
ZHU LIN

Assignees

天津重型装备工程研究有限公司
中国第一重型机械股份公司

Dates

Publication Date: 20260508
Application Date: 20250114

Claims (9)

1. The method for predicting the dynamic recrystallization rule of the crystal grains in the forging process of the heat-resistant steel forging is characterized by comprising the following steps of: S1, performing thermal compression tests 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 thermal compression tests; S2, fitting to obtain a dynamic recrystallization model of the heat-resistant steel material according to the stress-strain data of the heat-resistant steel sample, wherein the dynamic recrystallization model comprises a critical strain model, a dynamic recrystallization dynamics equation, a strain model when dynamic recrystallization occurs by 50%, and a dynamic recrystallization grain size model; The critical strain model formula is as follows Wherein, the method comprises the steps of, Is a critical strain value; setting 1;d 0 as the initial grain size, wherein Z is the Zener-Hollomon parameter; For activation energy, E p1 、E p2 、E p3 、E p4 is a constant determined according to the stress-strain data, and E p2 、E p4 is set to 0 regardless of the influence of the initial grain size d 0 and Z parameters on critical strain; the strain model formula at 50% of dynamic recrystallization is as follows: ; Wherein, the The strain value is 50% of the dynamic recrystallization, Q Td is the activation energy, T d1 、T d2 、T d3 、T d4 is a constant determined according to the stress-strain data, and the strain of 50% of the dynamic recrystallization is not considered by the initial grain size and Z parameter Setting T d2 、T d4 to 0; The dynamic recrystallization kinetic equation is that Wherein X D is dynamic recrystallization volume fraction, In order to be a value of the actual strain, 、、、 The relation between the dynamic recrystallization volume fraction and the heat flow variable stress parameter is as follows: ; wherein X D is a dynamic recrystallization volume percentage, sigma WH is the extension of stress of a work hardening part, and can be obtained by extending stress strain data of epsilon < epsilon c stage according to a dynamic reversion rheological curve mathematical model, sigma is instantaneous stress, sigma s is saturated stress, sigma ss is steady-state stress, sigma and sigma ss are determined according to a real stress strain curve, ; ; Wherein, sigma 0 is the yield stress, Omega is dynamic recovery softening quantity; the dynamic recrystallization grain size model formula is Wherein, the method comprises the steps of, Q Dd is the activation energy; 、、、 To determine the constant based on the grain size data, the effect of the initial grain size and Z parameters on the dynamic recrystallized grain size is not taken into account 、 Set to 0; Wherein, the R is a gas constant, R= 8.314J/(mol.K), T is absolute temperature; the hot compression test under different initial grain sizes is not needed, the test times are reduced, and the prediction efficiency is improved; s3, establishing a material file of the heat-resistant steel material suitable for Forge software according to the dynamic recrystallization model of the heat-resistant steel material; 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, setting simulation parameters according to an actual forging process of the heat-resistant steel forging, and simulating a forging forming process of the heat-resistant steel forging by adopting force software; And S5, extracting dynamic recrystallization grain simulation data of the heat-resistant steel forging, wherein the dynamic recrystallization grain simulation data comprise a forging section dynamic recrystallization volume fraction cloud picture and a grain size cloud picture of the heat-resistant steel forging in the forging deformation process and after the forging deformation is finished.
2. The method according to claim 1, wherein in step S2, the dynamic recrystallization model comprises thermal deformation activation energy; The solving formula of the thermal deformation activation energy is that Wherein, the method comprises the steps of, The strain rate is shown as a strain rate, A is a structural factor, alpha is a stress level parameter, sigma is a true stress value, n is a stress strain index, Q is thermal deformation activation energy, R is a gas constant, R= 8.314J/(mol.K), and T is absolute temperature.
3. The method according to claim 1, wherein step S4 comprises: 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 forging materials according to the material file established in the step S3; S42, meshing the geometric model, setting mesh repartitioning conditions and parameters, and obtaining a finite element mesh model; s43, setting the relative positions of the forging and the auxiliary tool according to the actual forging process of the heat-resistant steel forging, selecting a forging press and setting press parameters; s44, determining boundary conditions of the finite element mesh model in the forging forming simulation process; S45, performing simulation calculation on the forging forming process of the heat-resistant steel forging.
4. A method according to claim 3, wherein in step S42, after meshing the forging, the minimum value of the surface shape factor or the volume shape factor of the mesh model of the forging is greater than 0.4; And setting the grid repartitioning condition to trigger grid repartitioning according to deformation.
5. The method according to claim 3, wherein in step S44, the boundary conditions include initial temperature, heat exchange conditions, friction conditions of the forging and the auxiliary tool, and heat exchange conditions of the forging and the external environment.
6. The method of claim 5, wherein the heat exchange conditions of the forging and the accessory are determined based on the type of contact between the forging and the accessory and the pressure exerted by the accessory to which the forging is subjected.
7. The method of claim 6, wherein the heat exchange conditions include adiabatic conditions, heat exchange conditions at a pressure of 250MPa, medium-interaction conditions of the forging and the rigid mold, strong-interaction conditions of the forging and the rigid mold, and weak-interaction conditions of the forging and the rigid mold; if the heat-resistant steel forging is forged under the action condition of a 3000 t-8000 t press, selecting the interaction condition of the forging and a rigid die; And selecting a strong interaction condition of the forging and the rigid die if the heat-resistant steel forging is forged under the action condition of the 9000-15000 t press.
8. The method according to claim 1, wherein step S1 comprises: s11, decomposing heat-resistant steel to be analyzed to prepare a plurality of heat-resistant steel samples for thermal compression tests; S12, carrying out single-pass thermal compression tests on the heat-resistant steel sample at different deformation temperatures and different deformation rates, and quenching the heat-resistant steel sample after the deformation of the heat-resistant steel sample reaches a preset value; S13, cutting the quenched heat-resistant steel sample in half along the axis direction of the heat-resistant steel sample, preparing a metallographic sample along a section, and obtaining a gold phase diagram of the heat-resistant steel sample; s14, counting the grain size data of the heat-resistant steel sample under different deformation conditions; And S15, acquiring test data of the thermal compression test in the step S12, and converting the test data into real stress strain data of the heat-resistant steel.
9. The method according to any one of claims 1-8, further comprising: S6, obtaining actual grain size actual measurement data of the heat-resistant steel forging after forging forming, and comparing the dynamic recrystallization grain simulation data with the grain size actual measurement data to verify the accuracy of the dynamic recrystallization grain simulation data.

Description

Prediction method for dynamic recrystallization rule of crystal grains in forging process of heat-resistant steel forging Technical Field The invention relates to the technical field of finite element simulation forging processes, in particular to a prediction method for a dynamic recrystallization rule of crystal grains in a forging process of a heat-resistant steel forging. Background The ultra-supercritical generator set has the advantages of high heat efficiency, low energy consumption and the like, and is widely studied in the thermal generator set technology. The high-temperature pressure-bearing member in the ultra-supercritical generator set is mainly made of heat-resistant steel, and the quality of the heat-resistant steel forging directly influences the performance of the high-temperature pressure-bearing member. However, the grain size of the heat-resistant steel material is difficult to control in the hot working process, which is always a difficult point in the forging trial-manufacturing process, and the quality of the forging is greatly dependent on the grain size, which is mainly dependent on the dynamic recrystallization process, so that the rule of dynamic recrystallization of the grain is accurately predicted and controlled, and the heat-resistant steel material has important significance in improving the comprehensive mechanical property of the forging. With the progress of computer technology and numerical simulation methods, prediction methods of dynamic recrystallization have also been rapidly developed. The prediction method of dynamic recrystallization mainly comprises a prediction method based on a physical model, an experimental research method and other means. The prediction method based on Sellars-Tegart model and Avrami equation is mainly characterized in that a dynamic recrystallization process is predicted by establishing a dynamic curve of recrystallization through a mathematical model, and key data such as a rheological stress curve, a microstructure evolution image and the like of a material are obtained by performing thermal deformation tests on the material under different conditions through an experimental research method, so that the mathematical model of dynamic recrystallization is established, the dynamic recrystallization rules of all positions of a forging under the actual forging condition cannot be intuitively reflected through the above methods, and reliable reference cannot be directly provided for the forging process formulation of a large-sized forging. Disclosure of Invention In view of the above analysis, the embodiment of the invention aims to provide a prediction method of a dynamic recrystallization rule of crystal grains in a forging process of a heat-resistant steel forging, which is used for solving the problem that the existing method cannot intuitively reflect the dynamic recrystallization rule of each position of the forging under the actual forging condition. The embodiment of the invention provides a prediction method of a dynamic recrystallization rule of crystal grains in a forging process of a heat-resistant steel forging, which comprises the following steps: S1, performing thermal compression tests 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 thermal compression tests; S2, fitting to obtain a dynamic recrystallization model of the heat-resistant steel material according to 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 a material file into the finite element simulation model, setting simulation parameters according to the actual forging process of the heat-resistant steel forging, and simulating 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 comprises a dynamic recrystallization volume fraction cloud picture and a grain size cloud picture of the section of the heat-resistant steel forging; 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 by 50%, dynamic recrystallization grain size model; the solution formula of the thermal deformation activation energy is as follows Wherein, the method comprises the steps of,A is a strain rate, A is a structural factor, alpha is a stress level pa