A physical mechanism-based method, device and storage medium for identifying and predicting alloy high-temperature tensile and creep deformation parameters

Abstract

The present invention discloses a method, device and storage medium for identifying and predicting high-temperature tensile and creep deformation parameters of alloys based on physical mechanisms, and relates to the field of identifying and predicting high-temperature tensile and creep plastic constitutive parameters of alloys. The method comprises the following steps: performing tensile tests and creep tests on samples to obtain experimental data, and analyzing the data to obtain nominal yield stress and steady-state creep rate; using backscattered electron diffraction analysis technology to measure and analyze the samples to obtain data, establishing a finite element model based on 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 the first-order target parameters of the improved plastic flow law through the least squares method; and obtaining matching second-order target parameters by comparing the true stress-strain values ​​and creep strain time values ​​obtained from the experimental data and the simulation data, thereby predicting the high-temperature mechanical response, deformation behavior and failure behavior of the samples under different states.

Description

Technical Field

[0001] The present invention relates to the field of identification and prediction of alloy high-temperature tensile and creep plastic constitutive parameters, and 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 numerous industrial fields. In particular, in fields such as aerospace, energy, automotive manufacturing, and the nuclear industry, alloy materials must withstand the combined effects of high temperatures, high pressures, and complex stresses. Their high-temperature tensile and creep properties directly impact the reliability, safety, and service life of equipment. In alloy materials, high-temperature tensile and creep deformation often involve the interaction of multiple mechanisms, including dislocation slip, grain boundary diffusion, and precipitation phase evolution. This interplay of these mechanisms makes the creep deformation process complex and difficult to predict.

[0003] Accurate calibration of alloy material parameters is the cornerstone for a deep understanding and prediction of the deformation characteristics of alloy materials. It is crucial for evaluating their mechanical properties, optimizing material design, and developing efficient processing strategies. Crystal plasticity constitutive models, as advanced theoretical frameworks that describe the macroscopic mechanical response of materials from a microstructural perspective, significantly incorporate the dynamic evolution of microscopic defects in materials compared to traditional macroscopic phenomenological constitutive models, thus endowing the models with a deeper physical background and theoretical support. This characteristic has earned them widespread attention and recognition in the fields of materials science and mechanics. The construction of crystal plasticity constitutive models relies on a comprehensive set of material parameters, totaling 11 key parameters. These include three elastic constants (such as the elastic modulus and Poisson's ratio, which define the material's response during elastic deformation) and eight plasticity parameters (which address aspects such as slip system activation and hardening behavior, collectively characterizing the complex mechanisms of the material during plastic deformation). Accurate parameter calibration is a prerequisite for model effectiveness and serves as a bridge between theory and experiment, and between the microscopic and macroscopic levels.

[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 response differences 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, a more advanced and physically meaningful strategy is 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 mechanisms, the method comprising the following steps:

[0008] S1. Perform tensile and creep tests on the samples to obtain experimental data and draw tensile stress-strain curves and creep strain-time curves. Analyze the experimental data to obtain nominal yield stress and steady-state creep rate under different conditions.

[0009] S2. Using backscattered electron diffraction (EBSD) analysis technology 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. According to the nominal yield stress and steady-state creep rate, obtain the primary target parameters of the improved plastic flow law through the least squares method, and bring the primary 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 from the experimental data and the simulation data, the matching secondary target parameters are obtained;

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

[0013] Preferably, a tensile test and a creep test are performed on the sample 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 was used to conduct tensile tests. The displacement of the gauge point was measured using an optical extensometer. The tensile machine was used to output the loads at 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 a finite element model based on EBSD data are:

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

[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 is imported into the finite element analysis software, and the boundary conditions, loading methods and solution parameters are 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 the alloy, the slip resistance depends on the dislocation density and M 23 Volume fraction and size of C6 carbides or precipitates;

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

[0031] All slip systems have the same initial slip resistance;

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

[0033] By expanding the plastic flow law equation, we can obtain the solution expression of the critical shear stress τ0 with clear physical meaning. The solution expression of τ0 can be used to obtain the shear stress of the alloy material at an absolute temperature of 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 Taylor homogenization.

[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 i-th 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 from the experimental data and the simulation data, the specific contents of the matched secondary target parameters are obtained as follows:

[0042] By comparing the true stress and strain experimental data of uniaxial tension with the true stress and 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 and strain values, which are compared with the uniaxial tension true stress and strain experimental data. When the true stress and strain values ​​and the uniaxial tension true stress and 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 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, which is characterized by comprising: 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 the alloy as described in any one of claims S1-S5 is performed.

[0047] A storage medium having a program stored thereon, characterized in that when the program is executed by a processor, it realizes 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.

[0048] In summary, the present invention is a method, device and storage medium for identifying and predicting parameters of high-temperature tensile and creep deformation of alloys based on physical mechanisms. Compared with traditional technologies, the present invention addresses the problem that existing constitutive calibration methods for high-temperature tensile and creep deformation of alloys are uncertain and consume calibration computing power. It solves this problem by calibrating the decomposed shear stress at an absolute temperature of 0K based on physical meaning, calibrating some parameters using a homogenized macroscopic constitutive model, and calibrating the remaining parameters using an RVE model simulation based on microscopic EBSD results. This achieves a crystal plasticity parameter calibration based on physical meaning and fully considers multiple scales. At the same time, the finite element process problems that arise when simulating the high-temperature tensile and creep deformation of the alloy using the calibrated parameter results are solved. By determining the steps of the finite element simulation, the calculation and prediction of the high-temperature tensile and creep deformation of the alloy are achieved. The invention is suitable for the identification and prediction of high-temperature tensile and creep deformation parameters of alloys based on physical mechanisms. By using this method, the calibration parameter results can be made consistent with the physical mechanism and the accuracy and efficiency of the identification of high-temperature tensile and creep deformation parameters of the alloy 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 This is a flow chart of a physical mechanism-based method for identifying and predicting alloy high-temperature tensile deformation parameters in Example 1 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 The true strain and true stress relationship diagram of the P91 material at different temperatures in Example 1 of the present invention is compared. 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 of the P91 material at 500°C in Example 1 of the invention, where Figure 3 Figure 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;

[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 Example 2 of the present invention;

[0055] Figure 6 is the predicted steady-state creep rate and stress curve in Example 2 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 This is the architecture of a computer device in the third embodiment of the present invention;

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

[0059] The technical solutions of the present invention are further described below through the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and values ​​described in these embodiments do not limit the scope of this 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 disclosure, its application, or uses.

[0061] Technologies, systems, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they 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 same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0064] Example 1

[0065] The material used in the present invention is P91 martensitic heat-resistant steel, the chemical composition of which is shown in Table 1. This material has a body-centered cubic (BCC) crystal structure, and the microstructure of martensitic heat-resistant steel consists of pre-austenite grains (PAGs), martensite blocks, martensite laths, and carbide-reinforced phases. It is understood that the materials used in the embodiments of the present invention may also be alloys containing other components, and the present invention is not limited thereto.

[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 This is a flow chart of a method for identifying high-temperature tensile parameters of martensitic heat-resistant steel provided in Example 1 of the present invention. EBSD (Electron Back Scatter Diffraction) is a technique that uses a diffracted electron beam to identify the crystallographic orientation of a sample. The RVE model stands for Representative Volume Element. The method includes the following steps:

[0069] S1. Perform tensile and creep tests on the samples to obtain experimental data and draw tensile stress-strain curves and creep strain-time curves. Analyze the experimental data to obtain nominal yield stress and steady-state creep rate under different conditions.

[0070] Preferably, a tensile test and a creep test are performed on the sample 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 was used to conduct tensile tests. The displacement of the gauge point was measured using an optical extensometer. The tensile machine was used to output the loads at 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 this embodiment is to obtain tensile and creep curves of the alloy material through tensile and creep tests. The tensile tests are conducted using a German Zwick 100 kN universal mechanical testing machine. The displacement of the gauge point is measured using an optical extensometer. The Zwick machine outputs the loads at both ends of the component during loading in real time. Each set of experiments is repeated 3-5 times, and the average curve of these multiple tests is used 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. A straight line parallel to the elastic segment is drawn through the 0.2% strain point. The intersection of the line 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 (EBSD) analysis technology 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 a finite element model based on EBSD data are:

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

[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 RVE representative volume element;

[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 is imported into the finite element analysis software, and the boundary conditions, loading methods and solution parameters are 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 free of scratches to ensure the accuracy of the EBSD analysis; then, the processed sample is placed in the 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 analyzing the crystal orientation information of each pixel point; finally, the collected data is processed using professional software MTEX ​​to generate the material's crystallographic orientation map, phase distribution map and grain boundary information, etc., to fully reveal the material's microstructural characteristics 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 constructing the RVE; then, according to the microstructural characteristics of the material, such as grain size, shape, distribution and grain boundary properties, the geometric model of the RVE is constructed using a scripting language; then, based on the crystal orientation information measured by EBSD, corresponding material properties and constitutive relationships are assigned to each grain in the 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, and the boundary conditions, loading method and solution parameters are set to perform numerical simulation analysis 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. According to the nominal yield stress and steady-state creep rate, obtain the primary target parameters of the improved plastic flow law through the least squares method, and bring the primary 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 the alloy, the slip resistance depends on the dislocation density and M 23 Volume fraction and size of C6 carbides or precipitates;

[0093] Through 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 );

[0094] All slip systems have the same initial slip resistance;

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

[0096] By expanding the plastic flow law equation, we can obtain the solution expression of the critical shear stress τ0 with clear physical meaning. The solution expression of τ0 can be used to obtain the shear stress of the alloy material at an absolute temperature of 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 Taylor homogenization.

[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 i-th 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. 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 slip 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 the two main strengthening mechanisms caused by the existence of . In addition, it is assumed that the initial slip resistance of all slip 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 the 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 saturation 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 distance between sediments and K is a dimensionless constant. l can be given by:

[0123]

[0124] Where, 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 follows:

[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 to expand the plastic flow law equation to obtain a solution expression for the critical shear stress τ0 with a clear physical meaning. This solution can obtain the shear stress of the alloy material at an absolute temperature of 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 obtain 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] Substitute 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 to substitute the nominal yield stress and shear modulus of the alloy material at different temperatures to obtain a specific value of τ0. 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 of the P91 material at 500°C in Example 1 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, with a ratio of 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 the material when it is at an absolute temperature of T C When no force is required, the material will undergo plastic flow. Substituting in the value of τ0 we can get.

[0146] A further technical solution of the present invention is to modify the crystal plastic flow law using the Taylor homogenization method to make it 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 the 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 an embodiment of the present invention is to find the optimal parameters corresponding to the macroscopic plastic flow law by the least squares method based on the nominal yield stress corresponding to different temperatures and strain rates obtained from the tensile test. 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 i-th data point. By limiting the search range of parameters in the crystal plasticity constitutive model and performing a least squares search within the range, a set of most suitable parameters is obtained. This set of parameters will be suitable for P91 material tensile simulation and related analysis over a wide temperature range.

[0152] S4. By comparing the true stress-strain values ​​and creep strain time values ​​obtained from the experimental data and 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 from the experimental data and the simulation data, the specific contents of the matched secondary target parameters are obtained as follows:

[0154] By comparing the true stress and strain experimental data of uniaxial tension with the true stress and 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 sThese 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 so that the simulation data are 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 and strain values, which are compared with the uniaxial tension true stress and strain experimental data. When the true stress and strain values ​​and the uniaxial tension true stress and 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 to compare the true stress and strain experimental data of uniaxial tension with the true stress and strain values ​​obtained by numerical simulation of representative random distribution RVE generated by EBSD information to obtain the 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 and strain values ​​obtained by the RVE numerical simulation of the microstructure, which are compared with the true stress and 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 sample 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] Example 2

[0161] A representative voxel model is constructed based on the measured EBSD images. The representative voxel 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 required 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 model is constructed using a regular mesh with a mesh element size equal to the EBSD step size. The RVE size is chosen to ensure a size-independent solution. The finite element model typically contains about 30,000 linear (8-node) three-dimensional 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] This paper describes a procedure for determining the flow rules and parameters of the evolution equation 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 the C6 precipitate is 131 nm. A scaling parameter for the relative strengthening contributions of the precipitates and dislocations is also defined. Studies have shown that the relative contributions of the precipitates and dislocation forests to the overall strength are comparable, so this is assumed here. The initial dislocation density of the martensitic steel is set to 4.2×10^8mm -2 .

[0165] The five flow rule parameters in the equations were calibrated using numerical predictions from experimental data and polycrystalline RVEs as described below. Since the process of calibrating single-crystal models of martensitic steel microstructures using polycrystalline RVEs 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 was required. A least-squares optimization of the five flow rule parameters was then performed to minimize the discrepancy between the Taylor model predictions and the experimental data. The experimental data used for calibration, in the form of θ and stress, were obtained from the present invention and related literature.

[0166] Figure 4The figure shows a comparison between measured creep strain vs. time data and RVE predictions using a calibrated single crystal plasticity model, which is used to describe the behavior of each single crystal block at 600°C and 130 MPa stress. It can be seen that the predicted creep strain vs. 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, 130 MPa: The corresponding experimental and simulated data correspond to the loading condition of holding the stress constant at 130 MPa. Experiment is the creep strain-time data obtained experimentally, and Model is the creep strain-time data obtained from the finite element simulation.

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

[0168] Figure 5 The comparison of the RVE prediction results of the creep strain-time curves at different stress levels at 600°C with 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 through experiments by Kimura, Orlova, Basirat et al., and the corresponding loading conditions are to control the stress values ​​to be constant at 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 constant at 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℃ 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 within the same grade of P91 steel, its creep behavior can vary significantly due to differences in heat treatment conditions and inherent microstructure. The predicted results under the T0 condition, where the initial carbide size is 131 nm, are also presented. As can be seen, the predicted trends are consistent with the experimental results. Figure 7 The error bands for the measured and predicted values ​​are shown in Figure 2. The upper and lower bounds of the cyan region were chosen to encompass all data and minimize scatter. The horizontal axis represents the minimum creep rate obtained from experimental measurements, while the vertical axis represents the minimum creep rate predicted using finite element methods. (Basirat et al. (2012), Shrestha et al. (2012), Kimura et al. (2009), Sklenicka et al. (2003): Legend: The horizontal axis represents the minimum creep rate data obtained experimentally by Basirat, Shrestha, Kimura et al., and the vertical axis represents 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, which is characterized by comprising: 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 the alloy as described in any one of claims S1-S5 are executed.

[0172] A storage medium having a program stored thereon, characterized in that when the program is executed by a processor, it realizes 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.

[0173] The Parameter Calibration Center is used to calibrate the parameters of mechanical models. Its core function is to store and manage model parameters used for material behavior prediction and automate processing based on a predefined calibration process. First, the center stores the precalibrated parameters of the Taylor model. These initial parameters serve as the starting point for calibration and are set based on theoretical derivation or historical experimental data. The center then receives and imports actual experimental data related to high-temperature tensile and creep testing of alloys, which provides the basis for the calibration process.

[0174] The system processes the imported data according to a specific calibration algorithm. This algorithm combines the experimental results of the material with the model's predictions and adjusts the model parameters through iterative optimization to more accurately reflect the material's actual behavior. Using least squares or other optimization algorithms, the experimental stress-strain curve is fitted to the model's calculated results, thereby revising the model's initial parameters. This process provides a refined calibration tailored to each set of experimental conditions, such as temperature, stress level, and time.

[0175] After calibration, the optimized parameters are automatically stored on the hard drive as a permanent record. These calibrated parameter files use standardized naming and formatting for easy subsequent access and retrieval. Furthermore, the storage system supports version control and backup of parameters, ensuring transparency and data security during each calibration process. Through this parameter calibration center, researchers can efficiently and accurately manage and optimize model parameters, improving the accuracy and reliability of material predictions.

[0176] Using the calibration parameters stored above, the saved high-temperature tensile or creep model 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 calls the optimization parameters previously stored on 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 under various scenarios based on the set working conditions. This includes simulating the performance of materials in high-temperature environments within different temperature ranges, such as 500°C and 600°C. 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 the material under long-term continuous load. 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 material selection and use, but also guide actual engineering production.

[0179] Through a 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. They 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 solutions of the present invention rather than to limit the same. 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 solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying and predicting alloy high-temperature tensile and creep deformation parameters based on physical mechanisms, characterized in that: The following steps are involved: S1. Perform tensile and creep tests on the sample to obtain experimental data and draw tensile stress-strain curves and creep strain-time curves. Analyze the experimental data and curves to obtain nominal yield stress and steady-state creep rate under different states. S2. Use backscattered electron diffraction analysis technology (EBSD) to measure and analyze the sample to obtain EBSD data, establish a finite element model based on the EBSD data, and conduct tensile simulation tests and creep simulation experiments to obtain simulation data. The specific contents of establishing the finite element model based on the EBSD data are as follows: Pre-treat the sample and place the treated sample in the EBSD analysis equipment; 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; Import the constructed RVE model into the finite element analysis software, set the boundary conditions, loading method and solution parameters, and perform numerical simulation analysis; S3. Construct a crystal plasticity constitutive model. According to the nominal yield stress and steady-state creep rate, obtain the primary target parameters of the improved plastic flow law through the least squares method. Then bring the primary target parameters into the crystal plasticity constitutive model for simulation. The specific contents of constructing the crystal plasticity constitutive model are as follows: The key equation is the plastic flow law, which defines the crystal slip rate , the decomposed shear stress of the αth slip system , material slip resistance The governing equations of For the high temperature deformation behavior of alloys, the slip resistance depends on the dislocation density and volume fraction and size of carbides or precipitates; Through the forest dislocation and precipitate The initial sliding resistance is formed by the addition of two main strengthening mechanisms caused by the existence of ; All slip systems have the same initial slip resistance; Introducing equivalent accumulated plastic strain To describe the plastic deformation of the deformation process; Expanding the plastic flow law equation, we obtain the critical shear stress with clear physical meaning. The solution expression of The solution of the expression is to obtain the shear stress of the alloy material at an absolute temperature of 0K; Substituting the nominal yield stress and shear modulus of the alloy material at different temperatures, we can obtain The specific value of Modification of plastic flow laws using Taylor homogenization method; The specific content of the primary target parameters of the improved plastic flow law obtained by the least squares method is: The relative difference between the predicted and experimental yield stress values It is defined by the following formula: ； In the formula is the total number of data points, is the experimental loading rate of the ith data point, represents the yield stress of the i-th data point; By limiting the search range of parameters in the crystal plasticity constitutive model, the least squares method is used to search within the range to obtain the primary target parameters. S4. By comparing the true stress-strain values ​​and creep strain time values ​​obtained from the experimental data and 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 mechanisms 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 was used to conduct tensile tests. The displacement of the gauge point was measured using an optical extensometer. The tensile machine was used to output the loads at 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 method for identifying and predicting alloy high-temperature tensile and creep deformation parameters based on physical mechanisms according to claim 1, 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 matching secondary target parameters are obtained as follows: By comparing the true stress and strain experimental data of uniaxial tension with the true stress and strain values ​​obtained by numerical simulation of representative random distribution RVE generated by EBSD information, the matching and value; Specifically including: Substituting different and The finite element simulation is performed on the microstructure RVE numerical simulation to obtain the true stress and strain values, which are compared with the uniaxial tension true stress and strain experimental data. When the true stress and strain values ​​and the uniaxial tension true stress and strain experimental data reach the standard consistency, the calibration is successful and the saturated slip resistance in the current limited crystal plasticity constitutive model is determined. , material hardening parameters is the secondary target parameter.

4. The method for identifying and predicting alloy high-temperature tensile and creep deformation parameters based on physical mechanisms according to claim 3, 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 conditions 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.

5. A method and device for identifying and predicting alloy high-temperature tensile and creep deformation parameters based on physical mechanisms, characterized in that: include: processor; and a memory, wherein the memory stores a computer executable program, and when the processor executes the computer executable program, the alloy high-temperature tensile and creep deformation parameter identification and prediction method according to any one of claims 1 to 4 is executed.

6. A storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the method for identifying and predicting alloy high-temperature tensile and creep deformation parameters based on physical mechanisms as described in any one of claims 1 to 4 is implemented.

Images