Alloy high-temperature stretching and creep deformation parameter identification and prediction method and device based on physical mechanism and storage medium

Abstract

The invention discloses an alloy high-temperature tensile and creep deformation parameter identification and prediction method and device based on a physical mechanism and a storage medium, and relates to the field of alloy high-temperature tensile and creep plasticity constitutive parameter identification and prediction.The alloy high-temperature tensile and creep deformation parameter identification and prediction method comprises the steps that a sample is subjected to a tensile test and a creep test to obtain experimental data; the nominal yield stress and the steady-state creep rate are obtained through analysis; measuring and analyzing the sample by using a backscattered electron diffraction analysis technology to obtain data, establishing a finite element model according to the data, and performing tensile finite element simulation and creep finite element simulation to obtain simulation data; constructing a crystal plastic constitutive model, and obtaining primary target parameters of an improved plastic flow rule through a least square method; a true stress strain value and a creep strain time value which are obtained by comparing experimental data and simulation data are compared to obtain matched secondary target parameters, and then high-temperature mechanical response, deformation behaviors and failure behaviors of the sample in different states are predicted.

Description

Technical Field

[0001] The present invention relates to the field of alloy high temperature tensile and creep plastic constitutive parameter identification and prediction, in particular to a physical mechanism-based alloy high temperature tensile and creep deformation parameter identification, prediction method, device and storage medium. Background Art

[0002] The tensile and creep deformation properties of alloy materials under high temperature environments are crucial for equipment design and performance evaluation in many industrial fields. Especially in the fields of aerospace, energy, automobile manufacturing and nuclear industry, alloy materials need to withstand the combined effects of high temperature, high pressure and complex stresses. Their high-temperature tensile and creep properties directly affect the reliability, safety and service life of the equipment. In alloy materials, high-temperature tensile and creep deformation often involve the interaction of multiple mechanisms, including dislocation slip, grain boundary diffusion, precipitation phase evolution, etc. The interaction of these mechanisms makes the creep deformation process complex and difficult to predict.

[0003] Accurate calibration of alloy material parameters is the cornerstone for in-depth understanding and prediction of the deformation characteristics of alloy materials, and is crucial for evaluating their mechanical properties, optimizing material design, and formulating efficient processing strategies. As an advanced theoretical framework that describes the macroscopic mechanical response of materials from the perspective of microstructure, the crystal plasticity constitutive model significantly incorporates the dynamic evolution mechanism of material microscopic defects compared to the traditional macroscopic phenomenological constitutive model, thus giving the model a deeper physical background and theoretical support. This feature has won it widespread attention and recognition in the field of materials science and mechanics research. The construction of the crystal plasticity constitutive model relies on a detailed set of material parameter systems, a total of 11 key parameters, covering 3 elastic constants (such as elastic modulus, Poisson's ratio, etc., which define the response of the material in the elastic deformation stage) and 8 plastic parameters (these parameters involve the activation of the slip system, hardening behavior, and other aspects, and together characterize the complex mechanism of the material in the plastic deformation process). Accurate calibration of parameters is a prerequisite for the effectiveness of the model and a bridge connecting theory and experiment, micro and macro.

[0004] The commonly used method is to obtain these alloy material parameters by comparing the finite element simulation results with the stress-strain curves obtained by experimental tests. This method can usually achieve good results under single temperature and single strain rate conditions, and can effectively verify the predictive ability of the model. However, faced with more complex actual working conditions, especially mechanical problems involving multiple temperature ranges and multiple strain rate combinations, the traditional single data set comparison calibration method seems to be powerless. In this case, relying solely on data fitting makes it difficult to capture the differences in the response of the material under different conditions, and it is impossible to effectively explore the parameter space to obtain the optimal solution. Therefore, for the calibration of material parameters under complex working conditions, more advanced and physically based strategies are needed.

[0005] Therefore, there is an urgent need for a physical mechanism-based alloy high-temperature tensile and creep deformation parameter identification, prediction method, device and storage medium for parameter identification and prediction. Summary of the invention

[0006] To solve the above problems, the main purpose of the present invention is to realize systematic parameter identification and prediction based on physical mechanisms for high-temperature tensile and creep deformation of alloys. The obtained parameters are applicable to multiple temperatures and rates, and at the same time solve the problem of computing power consumption caused by parameter search.

[0007] The present invention provides a method for identifying alloy high temperature tensile and creep deformation parameters based on physical mechanism, the method comprising the following steps:

[0008] S1. Perform tensile test and creep test on the sample to obtain experimental data and draw tensile stress-strain curve and creep strain-time curve, and analyze the experimental data to obtain nominal yield stress and steady-state creep rate under different states;

[0009] S2. Using backscattered electron diffraction analysis technology EBSD to measure and analyze the sample to obtain EBSD data, and establishing a finite element model based on the EBSD data, and performing tensile simulation tests and creep simulation experiments to obtain simulation data;

[0010] S3. Construct a crystal plasticity constitutive model, obtain the first-order target parameters of the improved plastic flow law by the least square method according to the nominal yield stress and the steady-state creep rate, and bring the first-order target parameters into the crystal plasticity constitutive model for simulation;

[0011] S4, by comparing the true stress-strain values ​​and creep strain time values ​​obtained by comparing the experimental data with the simulation data, the matching secondary target parameters are obtained;

[0012] S5. Based on the primary target parameters and secondary target parameters, predict the high-temperature mechanical response, deformation behavior and failure behavior of samples under different conditions.

[0013] Preferably, the sample is subjected to a tensile test and a creep test to obtain experimental data, and the specific contents of the nominal yield stress and steady-state creep rate under different states obtained by analyzing the experimental data are as follows:

[0014] A universal mechanical testing machine is used to conduct tensile tests. The displacement of the gauge point is measured by an optical extensometer. The tensile machine is used to output the loads on both ends of the component in real time during the loading process.

[0015] Repeat each set of experiments several times, and take the average curve of the experiments as the load-displacement curve;

[0016] Process the load displacement curve obtained through the experiment to obtain the material stress-strain curve;

[0017] According to the knowledge of material mechanics, the tensile curve of alloys is divided into elastic, yield, strengthening, necking and fracture stages;

[0018] Draw a straight line parallel to the elastic segment through the 0.2% strain. The intersection with the yield or strengthening segment is the nominal yield point, and the corresponding ordinate is the nominal yield stress of the material.

[0019] Preferably, the specific contents of establishing the finite element model according to the EBSD data are:

[0020] Pre-treat the sample and place the treated sample in an EBSD analysis device;

[0021] Run the EBSD analysis equipment to collect backscattered electrons and compare them with the diffraction pattern to analyze the crystal orientation information of each pixel;

[0022] The collected data is processed using software to generate the material's crystallographic orientation map, phase distribution map and grain boundary information to obtain EBSD data;

[0023] Based on the acquired EBSD data, identify and extract the different phases, grains and their orientation information in the material as the basis for constructing RVE;

[0024] According to the microstructural characteristics of the material, the geometric model of RVE is constructed using scripting language;

[0025] According to the crystal orientation information measured by EBSD, the corresponding material properties and constitutive relations are assigned to each grain in the RVE;

[0026] The constructed RVE model was imported into the finite element analysis software, and the boundary conditions, loading methods and solution parameters were set to perform numerical simulation analysis.

[0027] Preferably, the specific contents of constructing the crystal plasticity constitutive model are:

[0028] The key equations include the plastic flow law, which defines the crystal slip rate The decomposed shear stress τ of the αth slip system α , material slip resistance The governing equations of

[0029] For the high temperature deformation behavior of alloys, the slip resistance depends on the dislocation density and M 23 Volume fraction and size of C6 carbides or precipitates;

[0030] Through the forest dislocation (S 0d ) and M 23 C6 precipitate (S 0pThe initial sliding resistance S0 is the sum of the two main strengthening mechanisms caused by the existence of );

[0031] The initial slip resistance is the same for all slip systems;

[0032] Introducing equivalent accumulated plastic strain To describe the plastic deformation of the deformation process;

[0033] The plastic flow law equation is extended to obtain the solution expression of the critical shear stress τ0 with clear physical meaning. The solution expression of τ0 can obtain the shear stress of the alloy material when the absolute temperature is 0K.

[0034] Substitute the nominal yield stress and shear modulus of the alloy material at different temperatures to obtain the specific value of τ0;

[0035] The crystal plastic flow method was modified using the Taylor homogenization method.

[0036] Preferably, the specific content of the primary target parameter of the improved plastic flow law obtained by the least squares method is:

[0037] The relative difference Δ between the predicted and experimentally obtained yield stress values ​​is defined by:

[0038]

[0039] Where N is the total number of data points, is the experimental loading rate of the ith data point, σ 0.2％s,i represents the yield stress of the ith data point;

[0040] By limiting the search range of parameters in the crystal plasticity constitutive model, the least squares search is performed within the range to obtain the primary target parameters.

[0041] Preferably, by comparing the true stress-strain values ​​and creep strain time values ​​obtained by comparing the experimental data with the simulation data, the specific contents of the matched secondary target parameters are obtained as follows:

[0042] By comparing the true stress-strain experimental data of uniaxial tension with the true stress-strain values ​​obtained by numerical simulation of representative random distribution RVE generated by EBSD information, the matching h s With S sat value;

[0043] Specifically include: Substituting different h s With S satThe finite element simulation is performed on the microstructure RVE numerical simulation to obtain the true stress-strain value, which is compared with the uniaxial tension true stress-strain experimental data. When the true stress-strain value and the uniaxial tension true stress-strain experimental data reach the standard consistency, the calibration is successful, and the saturated slip resistance S in the current limited crystal plasticity constitutive model is determined. sat , material hardening parameter h s is the secondary target parameter.

[0044] Preferably, based on the primary target parameters and the secondary target parameters, the specific contents of predicting the high temperature mechanical response, deformation and failure behavior of the samples under different states include:

[0045] Submit the inp file and umat file in the finite element model to give the structure a material constitutive structure based on crystal plasticity, thereby completing the calculation and prediction of the high-temperature tensile and creep deformation of the alloy.

[0046] The present application also mentions a method and device for identifying and predicting high-temperature tensile and creep deformation parameters of alloys based on physical mechanisms, characterized in that it includes: a processor; and a memory, wherein a computer executable program is stored in the memory, and when the computer executable program is executed by the processor, the identification and prediction of high-temperature tensile and creep deformation parameters of alloys described in any one of claims S1-S5 are performed.

[0047] A storage medium having a program stored thereon, characterized in that when the program is executed by a processor, the identification and prediction of alloy high-temperature tensile and creep deformation parameters based on physical mechanisms as described in any one of claims S1-S5 are realized.

[0048] In summary, the present invention is a physical mechanism-based alloy high temperature tensile and creep deformation parameter identification, prediction method, device and storage medium. Compared with traditional technologies, the present invention aims at the problem that the existing alloy high temperature tensile and creep deformation constitutive calibration method is uncertain and consumes calibration computing power. It is solved by calibrating the decomposed shear stress at an absolute temperature of 0K based on physical meaning, calibrating some parameters using the homogenized macroscopic constitutive model, and calibrating the remaining parameters using the RVE model simulation based on the microscopic EBSD results. It realizes the crystal plastic parameter calibration based on physical meaning and fully considers multi-scale; at the same time, the finite element process problem that occurs when the alloy high temperature tensile and creep deformation are simulated using the calibrated parameter results is solved. By determining the steps of finite element simulation, the calculation and prediction of alloy high temperature tensile and creep deformation are realized. The invention is suitable for the identification and prediction of alloy high temperature tensile and creep deformation parameters based on physical mechanism. By using this method, the calibration parameter results can be made to conform to the physical mechanism and the accuracy and efficiency of alloy high temperature tensile and creep deformation parameter identification can be improved.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for identifying and predicting alloy high temperature tensile deformation parameters based on physical mechanism in the first embodiment of the present invention;

[0051] Figure 2 is a representative volume element generated by using the microscopic information of P91 steel obtained by EBSD in Examples 1 and 2 of the present invention, wherein Figure 2 (a) is the microstructure morphology obtained by EISD measurement. Figure 2 (b) shows the spatial distribution of the second Euler angle in the polycrystalline finite element model;

[0052] Figure 3 : is a comparison of the true strain and true stress relationship diagram of the P91 material at different temperatures in Example 1 of the present invention, wherein Figure 3 a is a comparison of the true strain and true stress relationship of the P91 material at 400°C in Example 1 of the present invention, where Figure 3 b is a comparison of the true strain and true stress relationship diagram of the P91 material at 500°C in the first embodiment of the invention, where Figure 3 Figure c is a comparison of the true strain and true stress relationship diagram of the P91 material at 600°C in Example 1 of the present invention;

[0053] Figure 4 is the calibrated creep strain curve in the second embodiment of the present invention;

[0054] Figure 5 is the creep strain curve under different stress levels in the second embodiment of the present invention;

[0055] Figure 6 is the predicted steady-state creep rate and stress curve in the second embodiment of the present invention;

[0056] Figure 7 is the error band of the measured value and the predicted value in the second embodiment of the present invention;

[0057] Figure 8 It is the architecture of a computer device in Embodiment 3 of the present invention;

[0058] Fig. 9 This is a schematic diagram of the overall process of a method for identifying and predicting alloy high temperature tensile deformation parameters based on physical mechanism of the present invention. DETAILED DESCRIPTION

[0059] The technical solution of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​described in these embodiments do not limit the scope of the present application.

[0060] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0061] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0062] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0063] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0064] Embodiment 1

[0065] The material corresponding to the present invention is P91 martensitic heat-resistant steel, and its chemical composition is shown in Table 1. The material has a body-centered cubic (BCC) crystal structure, and the microstructure of the martensitic heat-resistant steel consists of pre-austenite grains (PAG), martensitic blocks (Block), martensitic laths (lath) and carbide strengthening phases. It can be understood that the material of the embodiment of the present invention can also be an alloy containing other components, and the present invention is not limited to this.

[0066] Table 1 Chemical composition of martensitic heat-resistant steel P91

[0067] Element Name C Cr Mo Mn Si Ni Al V Nb N content(%) 0.086 8.89 0.87 0.39 0.33 0.30 0.003 0.022 0.096 0.003

[0068] Figure 1 The present invention provides a flow chart of a method for identifying high temperature tensile parameters of martensitic heat-resistant steel provided in the first embodiment of the present invention. EBSD is an electron backscatter diffraction technique (Electron Back Scatter Diffraction, EBSD) that uses a diffracted electron beam to identify the crystallographic orientation of a sample. The full name of the RVE model is Representative Volume Element, i.e., "representative volume element". The method includes the following steps:

[0069] S1. Perform tensile test and creep test on the sample to obtain experimental data and draw tensile stress-strain curve and creep strain-time curve, and analyze the experimental data to obtain nominal yield stress and steady-state creep rate under different states;

[0070] Preferably, the sample is subjected to a tensile test and a creep test to obtain experimental data, and the specific contents of the nominal yield stress and steady-state creep rate under different states obtained by analyzing the experimental data are as follows:

[0071] A universal mechanical testing machine is used to conduct tensile tests. The displacement of the gauge point is measured by an optical extensometer. The tensile machine is used to output the loads on both ends of the component in real time during the loading process.

[0072] Repeat each set of experiments several times, and take the average curve of the experiments as the load-displacement curve;

[0073] Process the load displacement curve obtained through the experiment to obtain the material stress-strain curve;

[0074] According to the knowledge of material mechanics, the tensile curve of alloys is divided into elastic, yield, strengthening, necking and fracture stages;

[0075] Draw a straight line parallel to the elastic segment through the 0.2% strain. The intersection with the yield or strengthening segment is the nominal yield point, and the corresponding ordinate is the nominal yield stress of the material.

[0076] The technical solution of the embodiment of the present invention is: obtain the tensile and creep curves of the alloy material through tensile tests and creep tests, that is, use the German Zwick 100KN universal mechanical testing machine to perform tensile tests, measure the displacement of the gauge point through an optical extensometer, and use the Zwick tensile machine to output the loads on both ends of the component during the loading process in real time. Repeat each set of experiments 3-5 times, and take the average curve of multiple experiments as the load displacement curve.

[0077] The load displacement curve obtained through the experiment is processed to obtain the material stress-strain curve. According to the knowledge of material mechanics, the alloy tensile curve is divided into elastic, yield, strengthening, necking and fracture stages. Draw a straight line parallel to the elastic segment through the 0.2% strain, and the intersection with the yield or strengthening segment is the nominal yield point, and the corresponding ordinate is the nominal yield stress of the material.

[0078] S2. Using backscattered electron diffraction analysis technology EBSD to measure and analyze the sample to obtain EBSD data, and establishing a finite element model based on the EBSD data, and performing tensile simulation tests and creep simulation experiments to obtain simulation data;

[0079] Preferably, the specific contents of establishing the finite element model according to the EBSD data are:

[0080] Pre-treat the sample and place the treated sample in an EBSD analysis device;

[0081] Run the EBSD analysis equipment to collect backscattered electrons and compare them with the diffraction pattern to analyze the crystal orientation information of each pixel;

[0082] The collected data is processed using software to generate the material's crystallographic orientation map, phase distribution map and grain boundary information to obtain EBSD data;

[0083] Based on the acquired EBSD data, the different phases, grains and their orientation information in the material are identified and extracted as the basis for constructing the RVE, the representative volume element of the RVE;

[0084] According to the microstructural characteristics of the material, the geometric model of RVE is constructed using scripting language;

[0085] According to the crystal orientation information measured by EBSD, the corresponding material properties and constitutive relations are assigned to each grain in the RVE;

[0086] The constructed RVE model was imported into the finite element analysis software, and the boundary conditions, loading methods and solution parameters were set to perform numerical simulation analysis.

[0087] The technical solution of the embodiment of the present invention is: using backscattered electron diffraction analysis technology (EBSD) to measure and obtain the microstructure of the material. First, the sample is pretreated, including cutting, grinding, and polishing until the surface is smooth and scratch-free to ensure the accuracy of the EBSD analysis; then, the treated sample is placed in a vacuum chamber of the EBSD analysis equipment, and the sample position and electron beam direction are adjusted to ensure that the analysis area is clear and identifiable; then, the EBSD data acquisition system is started, and the sample surface is bombarded with a high-energy electron beam to collect backscattered electrons and compare them with the diffraction pattern, thereby resolving the crystal orientation information of each pixel point; finally, the collected data is processed using professional software MTEX ​​to generate a crystallographic orientation map, phase distribution map, grain boundary information, etc. of the material, thereby comprehensively revealing the microstructural characteristics of the material and its crystallographic orientation.

[0088] Finite element model information is established through representative volume elements (RVE). The specific process includes: first, based on the obtained EBSD data, the different phases, grains and their orientation information in the material are identified and extracted as the basis for building RVE; then, according to the microstructural characteristics of the material, such as grain size, shape, distribution and grain boundary properties, the geometric model of RVE is constructed using a scripting language; then, according to the crystal orientation information measured by EBSD, the corresponding material properties and constitutive relations are assigned to each grain in RVE to ensure that the finite element model can accurately reflect the actual physical behavior of the material; finally, the constructed RVE model is imported into the finite element analysis software, the boundary conditions, loading methods and solution parameters are set, and numerical simulation analysis is performed to predict and evaluate the mechanical properties, deformation and failure behavior of the material under specific working conditions.

[0089] S3. Construct a crystal plasticity constitutive model, obtain the first-order target parameters of the improved plastic flow law by the least square method according to the nominal yield stress and the steady-state creep rate, and bring the first-order target parameters into the crystal plasticity constitutive model for simulation;

[0090] Preferably, the specific contents of constructing the crystal plasticity constitutive model are:

[0091] The key equations include the plastic flow law, which defines the crystal slip rate The decomposed shear stress τ of the αth slip system α , material sliding resistance S α The governing equations of

[0092] For the high temperature deformation behavior of alloys, the slip resistance depends on the dislocation density and M 23 Volume fraction and size of C6 carbides or precipitates;

[0093] Through the forest dislocation (S 0d ) and M 23 C6 precipitate (S 0p The initial sliding resistance S0 is the sum of the two main strengthening mechanisms caused by the existence of );

[0094] The initial slip resistance is the same for all slip systems;

[0095] Introducing equivalent accumulated plastic strain To describe the plastic deformation of the deformation process;

[0096] The plastic flow law equation is extended to obtain the solution expression of the critical shear stress τ0 with clear physical meaning. The solution expression of τ0 can obtain the shear stress of the alloy material when the absolute temperature is 0K.

[0097] Substitute the nominal yield stress and shear modulus of the alloy material at different temperatures to obtain the specific value of τ0;

[0098] The crystal plastic flow method was modified using the Taylor homogenization method.

[0099] Preferably, the specific content of the primary target parameter of the improved plastic flow law obtained by the least squares method is:

[0100] The relative difference Δ between the predicted and experimentally obtained yield stress values ​​is defined by:

[0101]

[0102] Where N is the total number of data points, is the experimental loading rate of the ith data point, σ 0.2％s,i represents the yield stress of the ith data point;

[0103] By limiting the search range of parameters in the crystal plasticity constitutive model, the least squares search is performed within the range to obtain the primary target parameters.

[0104] A further technical solution of the present invention is to use a crystal plastic constitutive model to describe the plastic slip of a non-porous alloy material, including key equations such as the plastic flow law, and the specific process includes:

[0105] The crystal slip rate used in the plastic flow law of the present invention is

[0106]

[0107] In the formula is the material parameter, F0 is the Helmholtz free energy, k is the Boltzmann constant, T is the absolute temperature, τ α is the decomposed shear stress of the αth slip system, S α is the material sliding resistance, τ0 is the critical shear stress, p and q are material parameters, K.

[0108] The decomposed shear stress τ of the αth slip system α It can be expressed as:

[0109]

[0110] Where F e is the elastic deformation gradient, T * is the elastic deformation gradient E e Conjugate Kirchhoff stress, m α is the slip direction unit vector of the αth slip system, n α is the unit normal vector of the slip surface.

[0111] Material sliding resistance Sα Rate of change It can be expressed as:

[0112]

[0113] In the formula is the dislocation interaction function, S0 is the initial slip resistance, S sat is the saturated slip resistance.

[0114] For the high temperature deformation behavior of the alloy, it is assumed that the slip resistance depends on the dislocation density and M 23 The volume fraction and size of C6 carbides or precipitates, the initial sliding resistance S0 can be determined by the forest dislocation (S 0d ) and M 23 C6 precipitate (S 0p ) is expressed in the form of the addition of two main strengthening mechanisms caused by the existence of . In addition, it is assumed that the initial slip resistance of all sliding systems is the same.

[0115]

[0116] Where α p is the precipitation phase strength parameter, μ is the shear modulus of the slip plane along the slip direction, b is the modulus of the Burgers vector, and d is M 23 The average size of the C6 precipitate is 131 nm, and f is M 23 The volume fraction of C6 precipitate phase is taken as 0.019, ρ0 is the initial dislocation density of the slip system, α ρ is the dislocation strength parameter.

[0117] For the slip resistance in the saturated state, in other words: when the hardening mechanism is balanced by the dynamic recovery process, an expression similar to the above can be derived. Slip saturation requires that the saturated dislocation density be defined as:

[0118]

[0119] Where Y S is the saturated mean free path of the dislocation, l a is the dislocation annihilation distance.

[0120] Based on the dislocation-precipitate interaction theory, the mean free path of saturated dislocations can be expressed as:

[0121]

[0122] Where l is the average sediment spacing and K is a dimensionless constant. l can be given by:

[0123]

[0124] In the formula, β p is a proportionality parameter used to explain the statistical variation of sediments. Combining the above three equations yields the following Expression, which has a clear correlation with the sediment size d.

[0125]

[0126] Then, for the saturated dislocation density term (S Sd ) and M 23 C6 precipitation strengthening related items (S Sp ) is expressed as:

[0127]

[0128]

[0129] The dislocation interaction function can be expressed by the material hardening parameter h s It means that its expression is:

[0130]

[0131] Where h s is the material hardening parameter, δ αβ is the Kronecker variable, ω1 and ω2 are interaction coefficients, and the present invention takes ω1=ω2=1.

[0132] In order to better analyze the finite element results of hole evolution, this paper introduces the equivalent cumulative plastic strain To describe the plastic deformation of the deformation process, the corresponding expression is as follows:

[0133]

[0134] Where t is the instantaneous time, τ is the time integral variable, and D p is the plastic deformation rate.

[0135] A further technical solution of the embodiment of the present invention is: expanding the plastic flow law equation to obtain a solution expression of the critical shear stress τ0 with a clear physical meaning, and the solution can obtain the shear stress of the alloy material when the absolute temperature is 0K. The specific process includes:

[0136] According to the crystal plasticity constitutive theory, the critical shear stress τ0 has a clear physical meaning, that is, the shear stress corresponding to the material when the absolute temperature is 0K. Rewriting the flow law formula, we can get the following two equations:

[0137]

[0138]

[0139] Where, T C It refers to the critical temperature at which the material can slip and dislocation without external force load, only through thermal activation.

[0140] Substituting T = 0K and T = T C We can get:

[0141]

[0142] Where τ0 is the critical shear stress of the material at an absolute temperature of 0K, τ α 0K is the decomposed shear stress of the αth slip system when the material is at an absolute temperature of 0K, G0 is the shear modulus of the material when the absolute temperature is 0K, σ 0K The material is at an absolute temperature of T C The nominal yield stress at The material is at an absolute temperature of T C The shear modulus at is the nominal yield stress of the material at an absolute temperature of 0K, and M is the Taylor factor.

[0143] A further technical solution of the present invention is: substituting the nominal yield stress and shear modulus of the alloy material at different temperatures to obtain a specific value of τ0, and the specific process includes:

[0144] make have

[0145] In addition, the shear modulus of P91 material at different temperatures can be obtained by exponential interpolation of the shear modulus at the existing temperature. Figure 3 As shown, P91 martensitic heat-resistant steel is a metal used in thermal power plants. It not only has high oxidation resistance and high-temperature steam corrosion resistance, but also has good impact toughness and high and stable long-lasting plasticity and thermal strength. Figure 3 a is a comparison of the true strain and true stress relationship of the P91 material at 400°C in Example 1 of the present invention, where Figure 3 b is a comparison of the true strain and true stress relationship diagram of the P91 material at 500°C in the first embodiment of the invention, where Figure 3 c is a comparison of the true strain and true stress relationship of the P91 material at 600°C in Example 1 of the present invention, according to the ratio G0 / G T The relationship between the scaled P91 yield stress and temperature can be fitted into a curve, the abscissa of which is the absolute temperature value and the ordinate is the ratio G0 / G T The scaled P91 yield stress value, the ordinate of the intersection of the curve and the Y axis is σY,0K The value of when the material is at an absolute temperature of T C When no force is required, the material will undergo plastic flow. Substituting in the value, we can get the specific value of τ0.

[0146] A further technical solution of the present invention is: using the Taylor homogenization method to modify the crystal plastic flow law so that it is applicable to the macroscopic constitutive model. The specific process includes: the plastic strain rate at the macroscopic level depends on the slip rate at the block level. According to Taylor homogenization, the crystal plastic flow law can be converted into:

[0147]

[0148] Where M is the Taylor factor, which is usually 3. is the macroscopic strain rate, taking the constant loading rate in the tensile test, σ e Equivalent stress.

[0149] A further technical solution of the embodiment of the present invention is: according to the nominal yield stress corresponding to different temperatures and strain rates obtained by the tensile test, the optimal parameters corresponding to the macroscopic plastic flow law are found by the least squares method. The specific process includes: the relative difference Δ between the predicted and experimental yield stress values ​​is defined by the following formula:

[0150]

[0151] Where N is the total number of data points, is the experimental loading rate of the ith data point, σ 0.2％s,i Represents the yield stress of the ith data point. By limiting the search range of parameters in the crystal plasticity constitutive model, the least squares search is performed within the range to obtain a set of most suitable parameters, which will be suitable for P91 material tensile simulation and related analysis in a large temperature range.

[0152] S4, by comparing the true stress-strain values ​​and creep strain time values ​​obtained by comparing the experimental data with the simulation data, the matching secondary target parameters are obtained;

[0153] Preferably, by comparing the true stress-strain values ​​and creep strain time values ​​obtained by comparing the experimental data with the simulation data, the specific contents of the matched secondary target parameters are obtained as follows:

[0154] By comparing the true stress-strain experimental data of uniaxial tension with the true stress-strain values ​​obtained by numerical simulation of representative random distribution RVE generated by EBSD information, the matching h s With S sat Value, S sat is the saturated slip resistance, h sare the material hardening parameters. These two parameters can be understood as secondary target parameters that need to be calibrated. The specific calibration method is to modify different h s With S sat The simulation is performed to make the simulation data consistent with the experimental data.

[0155] Specifically include: Substituting different h s With S sat The finite element simulation is performed on the microstructure RVE numerical simulation to obtain the true stress-strain value, which is compared with the uniaxial tension true stress-strain experimental data. When the true stress-strain value and the uniaxial tension true stress-strain experimental data reach the standard consistency, the calibration is successful, and the saturated slip resistance S in the current limited crystal plasticity constitutive model is determined. sat , material hardening parameter h s is the secondary target parameter.

[0156] A further technical solution of the embodiment of the present invention is: by comparing the true stress-strain experimental data of uniaxial tension with the true stress-strain values ​​obtained by numerical simulation of representative random distribution RVE generated by using EBSD information, a matching h s With S sat The specific process includes: Substituting different h s With S sat The finite element simulation is performed on the values ​​to obtain the true stress-strain values ​​obtained by the RVE numerical simulation of the microstructure, which are compared with the true stress-strain experimental data of uniaxial tension. When the numerical simulation and the experimental comparison show good consistency, the calibration is judged to be successful.

[0157] S5. Based on the primary and secondary target parameters, predict the high-temperature mechanical response, deformation and failure behavior of samples under different conditions.

[0158] Preferably, based on the primary target parameters and the secondary target parameters, the specific contents of predicting the high temperature mechanical response, deformation behavior and failure mechanism of the samples under different states include:

[0159] Submit the inp file and umat file in the finite element model to give the structure a material constitutive structure based on crystal plasticity, thereby completing the calculation and prediction of the high-temperature tensile and creep deformation of the alloy.

[0160] Embodiment 2

[0161] A representative volume element model is constructed based on the measured EBSD images. The representative volume element model is as follows: Figure 2 As shown, Eulerangle is the Euler angle, which is used to describe the three angles of the direction of a rigid body in 3-dimensional Euclidean space. Three parameters are needed to describe such a direction. Figure 2 (b) shows the spatial distribution of the second Euler angle of the polycrystalline sample.

[0162] In order to study the creep deformation behavior of martensitic steel at the bulk scale, a finite element model of the representative volume element (RVE) of its microstructure needs to be established. To this end, the RVE is digitally reconstructed from the electron backscatter diffraction (EBSD) image with a step size of 0.25 μm to obtain fine resolution. The size of the scan area is 180×135 μm 2 . The finite element models are built using a regular mesh with the same mesh element size as the EBSD step size. The RVE size is chosen to ensure that a size-independent solution is obtained. The finite element models typically contain about 30,000 linear (8-node) 3D elements with only one element in the out-of-plane direction. Periodic boundary conditions are imposed on the boundaries to achieve a realistic representation of the material bulk. In the finite element model of the polycrystalline RVE, the constitutive behavior of each single crystal block is described by a rate-dependent crystal plasticity constitutive model.

[0163] Model parameter calibration process:

[0164] The present invention describes a process for determining the flow rules and evolution equation parameters for a single grain within a martensite block region. First, except for the parameters and, the values ​​of the remaining parameters are taken from the literature or from measurements in this experimental study. The initial M 23 The size of C6 precipitate is 131nm. The scaling parameters of the relative strengthening contribution of precipitate and dislocation are defined at the same time. Studies have shown that the relative contribution of precipitate and dislocation forest to the overall strength is equivalent, so it is assumed here. The initial dislocation density of martensitic steel is set to 4.2×10^8mm -2 .

[0165] The five flow rule parameters in the equation are calibrated with numerical predictions from experimental data and polycrystalline RVE, as described below. Since the process of calibrating a single crystal model of the microstructure of martensitic steel using polycrystalline RVE is very time consuming, a Taylor model was developed for pre-calibration. Finally, in order to establish a scale conversion between the Taylor model and the microstructure RVE, an additional secondary calibration of one of the parameters in the flow rule is required. Subsequently, a least squares optimization of the five flow rule parameters is performed to minimize the difference between the Taylor model prediction and the experimental data. The experimental data used for calibration is derived from the present invention and related literature in the form of and stress.

[0166] Figure 4The comparison between the measured creep strain and time data and the RVE prediction using the calibrated single crystal plasticity model is shown. The model is used to describe the behavior of each single crystal block at 600°C and 130MPa stress. It can be seen that the predicted creep strain and time curves are in good agreement with the measured results. The horizontal axis time (h) is the creep time, and the vertical axis Creepstrain is the creep strain, 130MPa: The corresponding loading conditions of the experimental and simulated data in the figure are to control the stress value to be constant at 130MPa, Experiment is the creep strain-time data obtained by the experiment, and Model is the creep strain-time data obtained by establishing the finite element simulation.

[0167] Prediction of creep response under different temperature stress conditions:

[0168] Figure 5 The comparison between the RVE prediction results of the creep strain-time curves at different stress levels at 600°C and the published experimental data is shown. Kimura et al. (2012)-70MPa, Orlova et al. (1998)-110MPa, Basirat et al. (2012)-150MPa: refers to the creep strain-time data obtained by Kimura, Orlova, Basirat et al. through experiments, and the corresponding loading conditions are to control the stress values ​​to be 70, 110, and 150MPa; Model-70MPa, Model-110MPa, and Model-150MPa refer to the established finite element simulation data, and the corresponding loading conditions are to control the stress values ​​to be 70, 110, and 150MPa.

[0169] To further validate the predictive ability of the current model, the effect of a wide stress range on the minimum creep strain rate at 600 °C was investigated. Figure 6 The comparison between the minimum creep strain rate and stress predicted by RVE at 600°C is shown, and compared with the published P91 experimental data. Among them, the horizontal axis Stress (MPa) is stress, the vertical axis is the minimum creep rate, d = 70nm: the precipitate size d = 70nm, Basirat et al. (2012) Shrestha et al. (2012) Kimura et al. (2009) Sklenicka et al. (2003) refers to the minimum creep rate data obtained by Basirat, Shrestha, Kimura et al. through experiments; Model d = 70nm: refers to the minimum creep rate data obtained by finite element simulation when the precipitate size d = 70nm is given. It should be noted that due to Figure 5 The data in do not provide the size of the precipitated phase, and a typical initial value of d = 70 nm was used in the numerical prediction.

[0170] It is well known that even for the same grade of P91 steel, its creep behavior may vary significantly due to differences in heat treatment conditions and inherent microstructure. The prediction results under the T0 condition with an initial carbide size of 131 nm measured by the present invention are also given. It can be seen that the predicted trend is consistent with the experimental results. Figure 7 The error band diagram of the measured and predicted values ​​is shown in the figure. The upper and lower limits of the cyan area are selected to include all the data and minimize the scatter. Among them, the horizontal axis is the minimum creep rate obtained by experimental measurement, and the vertical axis is the minimum creep rate predicted by finite element method. Basirat et al. (2012) Shrestha et al. (2012) Kimura et al. (2009) Sklenicka et al. (2003): Legend, the horizontal axis of the corresponding data point is the minimum creep rate data obtained by Basirat, Shrestha, Kimura et al. through experiments, and the vertical axis is the minimum creep rate data predicted under the same conditions.

[0171] like Figure 8 As shown, the present application also mentions a method and device for identifying and predicting high-temperature tensile and creep deformation parameters of alloys based on physical mechanisms, characterized in that it includes: a processor; and a memory, wherein a computer executable program is stored in the memory, and when the computer executable program is executed by the processor, the identification and prediction of high-temperature tensile and creep deformation parameters of alloys described in any one of claims S1-S5 are performed.

[0172] A storage medium having a program stored thereon, characterized in that when the program is executed by a processor, the identification and prediction of alloy high-temperature tensile and creep deformation parameters based on physical mechanisms as described in any one of claims S1-S5 are realized.

[0173] The Calibration Center is used to calibrate the parameters of mechanical models. The core function of the center is to store and manage model parameters used for material behavior prediction and automate processing based on predefined calibration processes. First, the center will store the pre-calibrated parameters of the Taylor model. These initial parameters are used as the starting point for calibration and are set based on theoretical derivation or historical experimental data. Then, the center will receive and import actual experimental data related to high-temperature tensile and creep experiments on alloys, which provide a basis for the calibration process.

[0174] The system processes the imported data according to a specific calibration algorithm. The algorithm combines the experimental results of the material with the predicted results of the model, and adjusts the model parameters through iterative optimization to make it more accurately reflect the actual behavior of the material. The stress-strain curve in the experiment is fitted with the results of the model calculation using the least squares method or other optimization algorithms, and then the initial parameters of the model are corrected. This process is refined according to each set of experimental conditions, such as temperature, stress level, time, etc.

[0175] After calibration, the optimized parameters will be automatically stored in the hard disk as a permanent record. These calibrated parameter files will use standardized naming and format to facilitate subsequent calls and retrieval. In addition, the storage system also supports version control and backup of parameters to ensure transparency and data security during each calibration process. Through this parameter calibration center, researchers can efficiently and accurately manage and optimize model parameters to improve the accuracy and reliability of material predictions.

[0176] Using the calibration parameters stored above, the saved high-temperature tensile or creep models can be extracted from the computer, the parameters can be substituted, and the mechanical response prediction under different working conditions can be further carried out. This process first retrieves the optimization parameters previously stored in the hard disk and inputs them into the corresponding material model. These models, such as those based on the Taylor model or other crystal plasticity models, can describe the deformation behavior and stress-strain relationship of materials at high temperatures. By substituting the calibrated parameters into the model, it can be ensured that the model simulates the mechanical behavior of the material more accurately.

[0177] The system will predict the mechanical response in various scenarios according to the set working conditions. This includes simulating the performance of materials in high temperature environments in different temperature ranges, such as 500°C, 600°C, etc. At the same time, different stress levels and strain rates can be set to simulate the behavior of materials under various loading conditions. For example, under high-temperature creep conditions, the system can predict the strain accumulation and material life of materials under long-term continuous loads. For high-temperature tensile tests, the model can predict the yield strength, plastic deformation, and fracture behavior of the material.

[0178] These prediction results can not only help engineers optimize the selection and use of materials, but also guide actual engineering production.

[0179] Through the parameter calibration process based on physical significance, the optimal plastic parameters of alloy materials at high temperatures can be accurately determined. These parameters not only profoundly affect the basic mechanical properties of the material, but also bring many advantages and significant benefits to solving material mechanics problems under complex working conditions. It can improve prediction accuracy and comprehensiveness, optimize processing technology, enhance equipment reliability, extend service life, accelerate the development of new materials, and reduce R&D costs.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism, characterized in that: The following steps are involved: S1. Perform tensile test and creep test on the sample to obtain experimental data and draw tensile stress-strain curve and creep strain-time curve. Analyze the experimental data and curve to obtain nominal yield stress and steady-state creep rate under different conditions. S2. Using backscattered electron diffraction analysis technology EBSD to measure and analyze the sample to obtain EBSD data, and establishing a finite element model based on the EBSD data, and performing tensile simulation tests and creep simulation experiments to obtain simulation data; S3. Construct a crystal plasticity constitutive model, obtain the first-order target parameters of the improved plastic flow law by the least square method according to the nominal yield stress and the steady-state creep rate, and bring the first-order target parameters into the crystal plasticity constitutive model for simulation; S4, by comparing the true stress-strain values ​​and creep strain time values ​​obtained by comparing the experimental data with the simulation data, the matching secondary target parameters are obtained; S5. Based on the primary and secondary target parameters, predict the high-temperature mechanical response, deformation and failure behavior of samples under different conditions.

2. The method for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism according to claim 1, characterized in that: The tensile test and creep test were carried out on the samples to obtain experimental data. The specific contents of the nominal yield stress and steady-state creep rate under different states were obtained by analyzing the experimental data: A universal mechanical testing machine is used to conduct tensile tests. The displacement of the gauge point is measured by an optical extensometer. The tensile machine is used to output the loads on both ends of the component in real time during the loading process. Repeat each set of experiments several times, and take the average curve of the experiments as the load-displacement curve; Process the load displacement curve obtained through the experiment to obtain the material stress-strain curve; According to the knowledge of material mechanics, the tensile curve of alloys is divided into elastic, yield, strengthening, necking and fracture stages; Draw a straight line parallel to the elastic segment through the 0.2% strain. The intersection with the yield or strengthening segment is the nominal yield point, and the corresponding ordinate is the nominal yield stress of the material.

3. The physical mechanism-based high temperature tensile and creep deformation parameter identification of alloys according to claim 2, characterized in that: The specific contents of establishing the finite element model based on EBSD data are as follows: Pre-treat the sample and place the treated sample in an EBSD analysis device; Run the EBSD analysis equipment to collect backscattered electrons and compare them with the diffraction pattern to analyze the crystal orientation information of each pixel; The collected data is processed using software to generate the material's crystallographic orientation map, phase distribution map and grain boundary information to obtain EBSD data; Based on the acquired EBSD data, identify and extract the different phases, grains and their orientation information in the material as the basis for constructing RVE; According to the microstructural characteristics of the material, the geometric model of RVE is constructed using scripting language; According to the crystal orientation information measured by EBSD, the corresponding material properties and constitutive relations are assigned to each grain in the RVE; The constructed RVE model was imported into the finite element analysis software, and the boundary conditions, loading methods and solution parameters were set to perform numerical simulation analysis.

4. The method for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism according to claim 3 is characterized in that: The specific contents of constructing the crystal plasticity constitutive model are as follows: The key equations include the plastic flow law, which defines the crystal slip rate The decomposed shear stress τ of the αth slip system α , material sliding resistance S α The governing equations of For the high temperature deformation behavior of alloys, the slip resistance depends on the dislocation density and M 23 Volume fraction and size of C6 carbides or precipitates; Through the forest dislocation S 0d and M 23 C6 Precipitate S 0p The initial sliding resistance S0 is formed by the addition of two main strengthening mechanisms caused by the existence of; The initial slip resistance is the same for all slip systems; Introducing equivalent accumulated plastic strain To describe the plastic deformation of the deformation process; The plastic flow law equation is extended to obtain the solution expression of the critical shear stress τ0 with clear physical meaning. The solution expression of τ0 can obtain the shear stress of the alloy material when the absolute temperature is 0K. Substitute the nominal yield stress and shear modulus of the alloy material at different temperatures to obtain the specific value of τ0; The crystal plastic flow method was modified using the Taylor homogenization method.

5. The method for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism according to claim 4, characterized in that: The specific contents of the primary target parameters of the improved plastic flow law obtained by the least squares method are: The relative difference Δ between the predicted and experimentally obtained yield stress values ​​is defined by: Where N is the total number of data points, is the experimental loading rate of the ith data point, σ 0.2％s,i represents the yield stress of the ith data point; By limiting the search range of parameters in the crystal plasticity constitutive model, the least squares search is performed within the range to obtain the primary target parameters.

6. The method for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism according to claim 5, characterized in that: By comparing the true stress-strain values ​​and creep strain time values ​​obtained from the experimental data and simulation data, the specific contents of the matched secondary target parameters are as follows: By comparing the true stress-strain experimental data of uniaxial tension with the true stress-strain values ​​obtained by numerical simulation of representative random distribution RVE generated by EBSD information, the matching h s With S sat value; Specifically include: Substituting different h s With S sat The finite element simulation is performed on the microstructure RVE numerical simulation to obtain the true stress-strain value, which is compared with the uniaxial tension true stress-strain experimental data. When the true stress-strain value and the uniaxial tension true stress-strain experimental data reach the standard consistency, the calibration is successful, and the saturated slip resistance S in the current limited crystal plasticity constitutive model is determined. sat , material hardening parameter h s is the secondary target parameter.

7. The method for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism according to claim 6, characterized in that: Based on the primary and secondary target parameters, the specific contents of predicting the high temperature mechanical response, deformation behavior and failure mechanism of samples under different states include: Submit the inp file and umat file in the finite element model to give the structure a material constitutive structure based on crystal plasticity, thereby completing the calculation and prediction of the high-temperature tensile and creep deformation of the alloy.

8. A method and device for identifying and predicting alloy high temperature tensile and creep deformation parameters based on physical mechanism, characterized in that: include: processor; and a memory, wherein a computer executable program is stored in the memory, and when the computer executable program is executed by the processor, the method for identifying and predicting high-temperature tensile and creep deformation parameters of the alloy as described in any one of claims S1-7 is executed.

9. A storage medium having a program stored thereon, characterized in that: When the program is executed by the processor, it implements the alloy high-temperature tensile and creep deformation parameter identification and prediction method based on physical mechanism as described in any one of claims S1-7.

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