Metal material fatigue life prediction method, device and equipment considering non-uniform grain size distribution, medium and product

Abstract

The invention discloses a metal material fatigue life prediction method and device considering non-uniform grain size distribution, equipment, a medium and a product, and relates to the field of material fatigue life finite element numerical calculation. The method comprises the following steps: acquiring material parameters of a metal material; determining a crystal plastic constitutive model; determining a parameter group based on the crystal plasticity constitutive model; constructing a microstructure model to be tested; based on the microstructure model to be tested, according to the crystal plasticity constitutive model and the parameter group, applying a set loading condition and a set boundary condition, and determining material fatigue data; determining the fatigue crack initiation life according to the fatigue indication factor and the material fatigue data; according to the fatigue crack propagation curve, determining the fatigue crack propagation life by adopting a fitting mode; and determining the fatigue full life according to the fatigue crack initiation life and the fatigue crack propagation life. The method can improve the accuracy of determining the fatigue life of the metal material.

Description

Technical Field

[0001] The present application relates to the field of finite element numerical calculation of material fatigue life, and in particular to a method, device, equipment, medium and product for predicting the fatigue life of metal materials taking into account non-uniform grain size distribution. Background Art

[0002] With the implementation of major projects such as aviation, military industry, marine ships, and transportation, high-end equipment such as aircraft engines and launch vehicle engines are constantly seeking new solutions to challenge the "performance limit". Therefore, higher requirements are placed on the comprehensive performance, lightweight and greenness of structural materials.

[0003] Although the proportion of new materials such as composite materials and ceramics in the industrial field is gradually increasing, metal materials are still the most widely used structural materials due to their excellent comprehensive mechanical, physical and chemical properties. They have always been an important material basis for the progress and development of human society. Therefore, it is of great significance to develop metal structural materials with high performance, lightweight, sustainability and high reliability.

[0004] The strengthening and toughening of metal structural materials has always been a research hotspot in the field of materials. However, in many practical engineering applications, fatigue performance is the direct guarantee for the long-term service reliability of engineering equipment and components. In addition to a deep understanding of the deformation and strengthening mechanism of metal materials during fatigue, accurate prediction of their fatigue life is also of great significance to the optimal design of microstructure fatigue performance and engineering applications. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for predicting the fatigue life of metal materials taking into account the non-uniform grain size distribution, which can improve the accuracy of determining the fatigue life of metal materials.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for predicting the fatigue life of a metal material taking into account the non-uniform grain size distribution, comprising:

[0008] Obtaining material parameters of a metal material; the material parameters include: grain size;

[0009] Determine a crystal plasticity constitutive model; the crystal plasticity constitutive model is obtained by evolving the dislocation density based on the initial total dislocation model according to the material parameters; the crystal plasticity constitutive model includes: a statistical dislocation density evolution model and a geometrically required dislocation density evolution model; the statistical dislocation density evolution model is a mathematical model for adjusting the size correlation coefficient of the annihilation of the edge-type statistical storage dislocation and the size correlation coefficient of the annihilation of the screw-type statistical storage dislocation; the geometrically required dislocation density evolution model is a mathematical model for controlling the size correlation coefficient of the geometrically required dislocation accumulation process and the size correlation coefficient of the geometrically required dislocation annihilation process;

[0010] Based on the crystal plastic constitutive model, a parameter group is determined; the parameter group is determined by a simulation fitting method according to the dislocation density evolution and the stress-strain relationship; the parameter group includes: a reference slip rate and a plastic flow parameter;

[0011] Constructing a microstructure model to be tested; the microstructure model to be tested is a finite element model obtained by performing finite element analysis on the metal material;

[0012] Based on the microstructure model to be tested, according to the crystal plasticity constitutive model and the parameter group, set loading conditions and set boundary conditions are applied to determine material fatigue data; the material fatigue data includes: fatigue behavior and corresponding data;

[0013] Determining fatigue crack initiation life based on the fatigue indicator factor and the fatigue data of the material;

[0014] Determine the fatigue crack growth life by fitting according to the fatigue crack growth curve; the fatigue crack growth curve is determined based on the fatigue data of the material;

[0015] The fatigue life is determined according to the fatigue crack initiation life and the fatigue crack growth life.

[0016] In a second aspect, the present application provides a metal material fatigue life prediction device considering non-uniform grain size distribution, the metal material fatigue life prediction device considering non-uniform grain size distribution comprises:

[0017] The material parameter acquisition module is used to acquire the material parameters of the metal material; the material parameters include: grain size;

[0018] A crystal plasticity constitutive model determination module is used to determine the crystal plasticity constitutive model; the crystal plasticity constitutive model is obtained by evolving the dislocation density based on the initial total dislocation model according to the material parameters; the crystal plasticity constitutive model includes: a statistical dislocation density evolution model and a geometrically required dislocation density evolution model; the statistical dislocation density evolution model is a mathematical model for adjusting the size correlation coefficient of the annihilation of the edge-type statistical storage dislocation and the size correlation coefficient of the annihilation of the screw-type statistical storage dislocation; the geometrically required dislocation density evolution model is a mathematical model for controlling the size correlation coefficient of the geometrically required dislocation accumulation process and the size correlation coefficient of the geometrically required dislocation annihilation process;

[0019] A parameter group determination module is used to determine a parameter group based on the crystal plasticity constitutive model; the parameter group is determined according to the dislocation density evolution and stress-strain relationship by using a simulation fitting method; the parameter group includes: a reference slip rate and a plastic flow parameter;

[0020] A microstructure model construction module to be tested, used to construct a microstructure model to be tested; the microstructure model to be tested is a finite element model obtained by performing finite element analysis on the metal material;

[0021] A material fatigue data determination module is used to determine material fatigue data based on the microstructure model to be measured, according to the crystal plasticity constitutive model and the parameter group, applying set loading conditions and set boundary conditions; the material fatigue data includes: fatigue behavior and corresponding data;

[0022] A fatigue crack initiation life determination module, used to determine the fatigue crack initiation life according to the fatigue indication factor and the material fatigue data;

[0023] A fatigue crack growth life determination module, used to determine the fatigue crack growth life by fitting according to a fatigue crack growth curve; the fatigue crack growth curve is determined based on the material fatigue data;

[0024] The fatigue life determination module is used to determine the fatigue life according to the fatigue crack initiation life and the fatigue crack growth life.

[0025] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for predicting the fatigue life of metal materials taking into account the non-uniform grain size distribution.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the fatigue life of metal materials taking into account the non-uniform grain size distribution.

[0027] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for predicting the fatigue life of metal materials taking into account the non-uniform grain size distribution.

[0028] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0029] The present application provides a method, device, equipment, medium and product for predicting the fatigue life of metal materials taking into account the non-uniform grain size distribution, which introduces the grain size effect into the initial dislocation density and dislocation density evolution, adopts periodic boundary conditions, selects a microstructure model representative of the macroscopic specimen, takes into account the mechanical mechanism induced by the non-uniform material, and takes into account the crack extension life of the metal material, and can accurately predict the fatigue life of metal materials with uniform and non-uniform structures, making up for the shortcomings of the traditional model that does not take into account the fatigue crack extension life of the metal material, resulting in insufficient fatigue life prediction accuracy. As a result, the accuracy of determining the fatigue life of the metal material can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flowchart of a fatigue life prediction method for metallic materials considering non-uniform grain size distribution for this application;

[0032] Figure 2 This is a flow chart of the different microstructure mechanical properties methods based on ABAQUS for this application;

[0033] Figure 3 Schematic diagram of the prediction and experimental results of initial dislocation density of metal materials with different grain sizes;

[0034] Figure 4 Schematic diagram of the prediction and experimental results of dislocation density evolution of metal materials with different grain sizes;

[0035] Figure 5 The diagram shows the prediction and experimental results of the first cycle fatigue hysteresis curve of uniform structural metal materials;

[0036] Figure 6 The figure shows the prediction and experimental results of the fatigue hysteresis curve of the tenth cycle of uniform structural metal materials;

[0037] Figure 7 The diagram shows the prediction and experimental results of the first cycle fatigue hysteresis curve of gradient structure metal materials;

[0038] Figure 8 The figure shows the prediction and experimental results of the fatigue hysteresis curve of the gradient structure metal material in the tenth cycle;

[0039] Fig. 9 This is the evolution diagram of fatigue crack initiation indicator factors of uniform structure metal materials;

[0040] Fig.10 This is the evolution diagram of fatigue crack initiation indicator factors of gradient structure metal materials;

[0041] Fig.11 It is the fitting diagram of fatigue crack growth curve of uniform and gradient structure metal materials;

[0042] Fig.12 The prediction accuracy diagram of fatigue initiation life of uniform and gradient structure metal materials;

[0043] Fig.13 Prediction accuracy diagrams considering fatigue life for uniform and gradient structured metallic materials;

[0044] Fig.14 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0047] like Figure 1 As shown, the embodiment of the present application provides a method for predicting the fatigue life of a metal material considering the non-uniform grain size distribution. The method for predicting the fatigue life of a metal material considering the non-uniform grain size distribution includes:

[0048] Step 100: Obtain material parameters of the metal material. The material parameters include: grain size.

[0049] Step 200: Determine the crystal plasticity constitutive model. The crystal plasticity constitutive model is obtained by evolving the dislocation density based on the initial total dislocation model according to the material parameters; the crystal plasticity constitutive model includes: a statistical dislocation density evolution model and a geometric required dislocation density evolution model. The statistical dislocation density evolution model is a mathematical model for adjusting the size correlation coefficient of the annihilation of the edge-type statistical storage dislocation and the size correlation coefficient of the annihilation of the screw-type statistical storage dislocation; the geometric required dislocation density evolution model is a mathematical model for controlling the size correlation coefficient of the geometric required dislocation accumulation process and the size correlation coefficient of the geometric required dislocation annihilation process.

[0050] Step 300: Determine a parameter group based on the crystal plasticity constitutive model. The parameter group is determined by a simulation fitting method according to the dislocation density evolution and the stress-strain relationship; the parameter group includes: a reference slip rate and a plastic flow parameter.

[0051] Step 400: construct a microstructure model to be tested. The microstructure model to be tested is a finite element model obtained by performing finite element analysis on a metal material.

[0052] Step 500: Based on the microstructure model to be tested, according to the crystal plasticity constitutive model and the parameter group, set loading conditions and set boundary conditions are applied to determine material fatigue data. Material fatigue data includes: fatigue behavior and corresponding data.

[0053] Step 600: Determine fatigue crack initiation life according to fatigue indicator factor and material fatigue data.

[0054] Step 700: Determine the fatigue crack growth life by fitting the fatigue crack growth curve. The fatigue crack growth curve is determined based on material fatigue data.

[0055] Step 800: Determine the fatigue life according to the fatigue crack initiation life and the fatigue crack growth life.

[0056] The initial total dislocation model specifically includes:

[0057]

[0058] Among them, ρ total is the initial total dislocation model; ρ S0 is the initial statistical dislocation density; ρ G0 is the initial geometric required dislocation density; d is the grain size; N is the total number of slip systems; The initial geometry of the αth slip system must be the dislocation density.

[0059] The statistical dislocation density evolution model includes:

[0060]

[0061] Among them, the statistical dislocation density includes: the edge statistical dislocation density of the αth slip system and the screw statistical dislocation density of the αth slip system is the edge statistical dislocation density for the αth slip system The evolution of ; b is the Burger vector; To adjust the size correlation coefficient of edge-shaped statistical storage dislocation annihilation; is the screw statistical dislocation density for the αth slip system evolution; is the mean free path associated with the total dislocation density; is the size correlation coefficient for adjusting the annihilation of screw-type statistical storage dislocations; N is the total number of slip systems; is the average edge dislocation spacing of the αth slip system; is the average screw dislocation spacing of the αth slip system; is the average dislocation spacing of the αth slip system; is the slip rate of the αth slip system; K i is the proportional parameter controlling the dislocation motion; e is the edge dislocation; s is the screw dislocation; is the edge statistical dislocation density of the βth slip system; is the screw statistical dislocation density of the βth slip system; is the geometric required dislocation density for the βth slip system.

[0062]

[0063] and All are reference coefficients; k e and k s are proportional factors; d c is the reference grain size for controlling dislocation annihilation at grain boundaries; d is the grain size.

[0064] The geometrically required dislocation density evolution model includes:

[0065]

[0066] in, is the necessary dislocation component of the pure screw geometry parallel to the m direction of the αth slip system; is the necessary dislocation component of the pure edge geometry parallel to the t direction of the αth slip system; is the necessary dislocation component of pure edge geometry parallel to the n direction of the αth slip system, m is the slip direction, n is the normal to the slip plane, t = m × n; To control the geometry the size dependence of the dislocation accumulation process is necessary; The size-dependent coefficient of the dislocation annihilation process is necessary to control the geometry; α Nye is the Nye factor; b is the Burger vector; curl() is the sum of the spatial gradients of the variables at the current integration point and the surrounding integration points; is the shear amount of the potential slip system; F p is the plastic strain gradient; is the geometric necessary dislocation density accumulated during the deformation of the αth slip system; is the cumulative geometric necessary dislocation density change rate during the deformation of the αth slip system parallel to the t direction; is the cumulative geometric necessary dislocation density change rate during the deformation of the αth slip system parallel to the n direction; is the cumulative geometric dislocation density change rate of the αth slip system parallel to the m direction during deformation; m α is the unit vector of the slip direction of the αth slip system;

[0067]

[0068] in, and n are dimensionless coefficients; is the saturation density of geometrically required dislocations; d c is the reference grain size for controlling dislocation annihilation at grain boundaries; d is the grain size; is the initial geometric required dislocation density of the αth slip system;

[0069] The back stress of each slip system in the crystal plasticity constitutive model is:

[0070]

[0071] Among them, X α is the back stress of the αth slip system; μ is the shear modulus; is the geometrically required dislocation stacking number in the effective area of ​​the αth slip system; v is the Poisson's ratio; w eff is the width of the dislocation pileup.

[0072] The storage energy density is used as the fatigue indicator factor; the method for determining the fatigue crack initiation life specifically includes:

[0073]

[0074] Where ζ is the fraction of plastic dissipated energy stored in the dislocation structure; G criis the cumulative energy density threshold of the local area within the material; G j is the accumulated storage energy density in the jth week; n is a dimensionless coefficient; G is the storage energy density; W is the dissipated energy density; ρ S is the total statistical dislocation density; ρ Gp is the total geometric required dislocation density; τ α is the shear stress of the αth slip system; is the shear amount of the α-slip potential slip system; N i is the fatigue crack initiation life.

[0075] The calculation formula for fatigue crack growth life is:

[0076]

[0077] Among them, N p is the fatigue crack growth life; a0 is the initial crack length; a c is the critical length of crack; C and m are fitting parameters; ΔK is the stress intensity factor; Δσ is the stress amplitude; a is the crack length; F is the crack shape factor; W is the dissipated energy density.

[0078] The purpose of this application is to provide a method for predicting the fatigue life of metal materials based on crystal plasticity and considering the non-uniform grain size distribution, so as to consider the fatigue behavior of metals with different grain sizes and predict the fatigue life of uniform and non-uniform structures. The main theoretical basis and assumptions of the method for predicting the fatigue life of metal materials based on crystal plasticity and considering the non-uniform grain size distribution are: (1) the slip deformation mechanism of thermally driven flow criterion is adopted; (2) the multi-scale slip resistance model takes into account the grain boundary strengthening and dislocation density strengthening related to grain size; (3) the modified back stress model takes into account the size-related geometric required dislocation density model; (4) the anisotropy of the microstructure is achieved by assigning each grain a random orientation or an actual orientation obtained according to the EBSD test method, and each grain is assigned a slip system and characteristic parameters corresponding to its grain structure type.

[0079] The fatigue life prediction method of metal materials based on crystal plasticity and considering non-uniform grain size distribution introduces the grain size effect into the initial dislocation density and dislocation density evolution, combines the physical back stress strengthening equation, writes it into the ABAQUS subroutine, and adopts periodic boundary conditions. By selecting the minimum microstructure representative of the macroscopic specimen to model, the mechanical mechanism induced by non-uniform materials is considered. Combined with the Paris formula, the crack extension life of metal materials is considered more simply, and the fatigue life of uniform and non-uniform structure metal materials can be accurately predicted, which makes up for the shortcomings of traditional models that do not consider the fatigue crack extension life of metal materials, resulting in insufficient fatigue life prediction accuracy. In addition, based on the ABAQUS finite element software, the deformation slip band, stress strain and damage concentration position and distribution map of each grain in the microstructure can be intuitively extracted to predict the crack initiation position, and the advantages of finite element simulation calculation can be fully utilized to realize complex boundary loading conditions and various visual post-processing operations, which has the advantages of intuitiveness, strong applicability and high accuracy. Furthermore, the crystal plasticity constitutive model of the method described in the present invention continues the theoretical mechanism of crystal plastic deformation of the heat-driven flow criterion, and is combined with the finite element method, adopts a grid model based on grain structure, and takes into account the grain size effect of the microstructure to be measured. It uses a physics-based dislocation evolution model and back stress evolution model, which can accurately predict the mechanical properties and fatigue damage evolution of different microstructures.

[0080] like Figure 2 In practical applications, the fatigue life prediction of metal materials considering non-uniform grain size distribution based on crystal plasticity includes the following steps:

[0081] Step S1: establishing an initial dislocation density and a dislocation density evolution model related to the grain size of a metal material, and embedding the model into a constitutive model of a user-defined unit UEL, wherein the user-defined unit UEL corresponds to a material parameter module.

[0082] In step S1, the following formula is used to establish the initial total dislocation (ρ total )Model:

[0083]

[0084] Specifically, step S1 includes: using the following formula to establish a statistical dislocation density evolution model related to the grain size of metal materials, where the statistical dislocation density is divided into edge-type statistical dislocation density: and screw statistical dislocation density Its evolution is:

[0085]

[0086]

[0087] in, The mean free path related to the total dislocation density, b represents the Burger vector, and represents the two coefficients of edge and screw dislocation generation in the α slip system. In addition, when the grain size drops below the critical threshold, a significant increase in the dislocation recovery rate is observed. and To adjust the size correlation coefficient of edge-type and screw-type statistical storage dislocation annihilation:

[0088]

[0089] in, and is the reference coefficient. k e and k s is a scaling factor, which is set equal in this application. c Represents the reference grain size that controls dislocation annihilation at grain boundaries.

[0090] The following formula is used to establish the geometric dislocation density evolution model related to the grain size of metal materials, and the evolution of geometric dislocations during deformation:

[0091]

[0092] The geometry must evolve the dislocation density as follows;

[0093]

[0094] and is the size-dependent coefficient that controls the geometric dislocation accumulation and annihilation process, as follows:

[0095]

[0096] in, and n are dimensionless coefficients, is the saturation density of the geometrically required dislocation.

[0097] The constitutive model of the user subroutine UEL is based on the framework of the existing crystal plasticity constitutive model; in the framework of the crystal plasticity constitutive model, the deformation of each grain is decomposed into an elastic strain gradient F e and the plastic strain gradient F p :F=F e F p .

[0098] The elastic strain is: ε e =C -1 :σg ,

[0099] Among them, ε e is the elastic strain in the elastoplastic constitutive equation, σ g is the stress tensor acting in the grain, C is the material elastic tensor;

[0100] Elastic strain ε e for: Where I is the identity matrix.

[0101] The plastic strain gradient of each grain is:

[0102] F p is the plastic strain gradient; P α is the direction factor tensor of the αth slip system, is the shear strain rate of the αth slip system.

[0103] Among them, P α is the direction factor tensor of the αth slip system, m α is the unit vector of the slip direction of the αth slip system, n α is the unit vector normal to the slip surface.

[0104] Shear amount of each potential slip system for:

[0105]

[0106] Among them, X α and S α are the kinematic strengthening and slip resistance of the αth slip system, respectively. K is the Boltzmann constant, and T is the absolute temperature. F0 and τ0 represent the total free energy and lattice friction required to overcome the lattice resistance at the current temperature, respectively. For a given slip system, p, q, and are two exponential parameters and the reference slip rate, the decomposed shear stress τ of the αth slip system α It is expressed as:

[0107]

[0108] Among them, T * is the second Piola-Kirchoof stress; T * ≈C:E e .

[0109] Among them, the slip resistance S of the αth slip system is α for:

[0110]

[0111] S gb =k gb d -1 / 2 .

[0112] Where μ represents the shear modulus, h s is the dislocation hardening coefficient, δ αβ represents the Kronecker delta symbol and the potential hardening ratio is denoted by w.

[0113] The back stress of each slip system is:

[0114]

[0115] where v is Poisson's ratio, is the geometrically required dislocation stacking number on the effective area of ​​slip system α:

[0116]

[0117] Among them, w eff Represents the width of the dislocation pileup.

[0118] Step S2: By simulating and fitting the dislocation evolution and stress-strain relationship of several microstructures of the metal material to be predicted, such as Figure 3 and Figure 4 As shown, a required parameter group is obtained, as shown in Table 1.

[0119] Table 1 Symbols, units and values ​​of parameters of dislocation-based crystal plasticity model

[0120]

[0121]

[0122] Step S2 specifically includes: obtaining a required parameter set by fitting the experimental results of two groups of microstructures with different grain sizes.

[0123] Step S3: Generate the microstructure finite element model required for prediction, combine the constitutive model of the user-defined unit UEL in step S1 and the parameter group in step S2, and calculate the fatigue behavior and related parameters of the material through ABAQUS software.

[0124] In step S3, the microstructure finite element model required for prediction is generated. Combining the constitutive model of the user-defined unit UEL in step S1 and the parameter group in step S2, the fatigue behavior and related parameters of the material are calculated by ABAQUS software. The fatigue fitting curve is as follows: Figure 5-Figure 8 shown.

[0125] Step S4: Select an appropriate fatigue indicator factor to predict the fatigue crack initiation life of the material.

[0126] In step S4, the storage energy density G is selected as the fatigue damage indicator to evaluate the dislocation accumulation energy required to cause crack initiation on a given length scale. In the framework of crystal plasticity, it can be expressed as:

[0127]

[0128] where ζ is the fraction of the plastically dissipated energy stored in the dislocation structure, usually taken as about 5% of the total stored in the dislocation accumulation.

[0129] As the number of fatigue cycles increases, the stored energy density continues to increase. When the accumulated energy density in the local area of ​​the material reaches the threshold G cri (This threshold is related to the material) to determine the crack initiation:

[0130]

[0131] Among them G j is the accumulated storage energy density in the jth week.

[0132] Fig. 9 and Fig.10 The evolution relationship between the storage energy density and the cycles is shown. It can be seen that the increment of the storage energy density after 5 cycles is basically stable and shows a linear cumulative trend, which is the same as the previous work. Therefore, the linear cumulative failure criterion is still used to calculate the fatigue initiation life N of the two structures. i The fatigue crack initiation life calculation formula is:

[0133]

[0134] The fatigue crack growth curve of the required predicted structure is obtained through experiments, and the fatigue crack growth life is obtained by fitting the Paris formula, and the fatigue life is obtained.

[0135] In step S5, the Paris formula is used to fit the crack growth curve of the desired predicted microstructure for life prediction:

[0136]

[0137] Among them, C and m are fitting parameters, and the fitting results are shown in Fig.11 As shown. Therefore, the fatigue crack growth life can be obtained from the following integral:

[0138]

[0139] Where a0 is the initial length of the crack, a cis the critical length of crack, which is 50μm and 500μm respectively. The fatigue crack initiation life of the material to be sought is added to the extension life to obtain the full life. The comparison results of the prediction method that only considers fatigue crack initiation and the full life prediction method proposed in this patent are as follows: Fig.12 and Fig.13 shown.

[0140] Based on the ABAQUS software platform, the user material subroutine is modified. Steps S1-S4 are all implemented through the user material subroutine. Steps S1-S4 are the specific calculation contents of a time increment step. After the calculation of all time increment steps is completed, the extraction of fatigue indicator factors under fatigue loads of metal materials and the calculation of fatigue crack initiation life are realized. Combined with the Paris formula fitting results of step S5, the fatigue crack propagation life is predicted, and finally the fatigue life is predicted. In addition, the method provided in this application can also be used to predict the fatigue life of other materials.

[0141] Through this embodiment, the following prediction results can be obtained:

[0142] 1. Prediction of initial dislocation density and dislocation density evolution for microstructures of different grain sizes.

[0143] Figure 3 and Figure 4 The experimental and simulation results of the initial dislocation density and dislocation density evolution of microstructures with different grain sizes through steps S1-S3 are shown. Figure 5-Figure 8 The comparison between the prediction and the experiment shows that the simulation prediction value is in good agreement with the experimental value, which proves the feasibility of dislocation density prediction.

[0144] 2. Prediction of cyclic behavior of uniform and gradient structures.

[0145] Figure 5-Figure 8 These are the results of the first and tenth hysteresis loops for uniform structure and gradient structure. It can be seen that the simulation prediction values ​​are in good agreement with the experimental values, proving the feasibility of the model.

[0146] 3. Fatigue life prediction of uniform structure and gradient structure.

[0147] Fig. 9 and Fig.10 is the evolution of fatigue crack initiation indicator factors for uniform and gradient structures, Fig.11 is the fatigue crack growth rate fitting curve, based on which, we can get Fig.12 and Fig.13 From the fatigue life prediction results, it can be seen that the prediction method that only considers fatigue crack initiation shows a large error band (4 times the error band), while the method mentioned in this application performs well and is more suitable for fatigue conditions in which the fatigue crack propagation stage accounts for a certain proportion.

[0148] This application establishes an initial dislocation density and dislocation density evolution model related to the grain size of metal materials, embeds it into the crystal plasticity constitutive model and integrates it into the user-defined unit UEL module of ABAQUS; obtains a required parameter set by simulating and fitting the dislocation density evolution and stress-strain relationship of several microstructures of metal materials to be predicted; establishes a finite element model of the microstructure to be predicted, applies loading and boundary conditions, and predicts the fatigue behavior of the material based on the parameter set; selects appropriate fatigue indicator factors to predict the fatigue crack initiation life; obtains the fatigue crack propagation curve of the desired predicted structure through experiments and obtains the fatigue crack propagation life through fitting the Paris formula to obtain the total fatigue life. This application can better consider the influence of dislocation density and energy accumulation on the fatigue behavior of materials, while considering the fatigue crack initiation and propagation life of metal materials, accurately predicting the full life of metal materials, and has the advantages of high precision, strong flexibility, material performance evaluation and intuitiveness.

[0149] Based on the same inventive concept, the embodiment of the present application also provides a device for predicting the fatigue life of a metal material considering non-uniform grain size distribution for realizing the method for predicting the fatigue life of a metal material considering non-uniform grain size distribution mentioned above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the device for predicting the fatigue life of a metal material considering non-uniform grain size distribution provided below can be referred to the limitations of the method for predicting the fatigue life of a metal material considering non-uniform grain size distribution mentioned above, and will not be repeated here.

[0150] In an exemplary embodiment, a metal material fatigue life prediction device considering non-uniform grain size distribution is provided, and the metal material fatigue life prediction device considering non-uniform grain size distribution includes:

[0151] The material parameter acquisition module is used to obtain the material parameters of the metal material; the material parameters include: grain size.

[0152] The crystal plasticity constitutive model determination module is used to determine the crystal plasticity constitutive model; the crystal plasticity constitutive model is obtained by evolving the dislocation density based on the initial total dislocation model according to the material parameters; the crystal plasticity constitutive model includes: a statistical dislocation density evolution model and a geometrically required dislocation density evolution model. The statistical dislocation density evolution model is a mathematical model for adjusting the size correlation coefficient of the annihilation of the edge-type statistical storage dislocation and the size correlation coefficient of the annihilation of the screw-type statistical storage dislocation; the geometrically required dislocation density evolution model is a mathematical model for controlling the size correlation coefficient of the geometrically required dislocation accumulation process and the size correlation coefficient of the geometrically required dislocation annihilation process.

[0153] The parameter group determination module is used to determine the parameter group based on the crystal plasticity constitutive model; the parameter group is determined according to the dislocation density evolution and stress-strain relationship by using a simulation fitting method; the parameter group includes: a reference slip rate and plastic flow parameters.

[0154] The module for constructing the microstructure model to be tested is used to construct the microstructure model to be tested; the microstructure model to be tested is a finite element model obtained by performing finite element analysis on metal materials.

[0155] The material fatigue data determination module is used to determine the material fatigue data based on the microstructure model to be tested, according to the crystal plasticity constitutive model and the parameter group, applying set loading conditions and set boundary conditions; the material fatigue data includes: fatigue behavior and corresponding data.

[0156] The fatigue crack initiation life determination module is used to determine the fatigue crack initiation life according to the fatigue indication factor and material fatigue data.

[0157] The fatigue crack growth life determination module is used to determine the fatigue crack growth life by fitting according to the fatigue crack growth curve; the fatigue crack growth curve is determined based on material fatigue data.

[0158] The fatigue life determination module is used to determine the fatigue life according to the fatigue crack initiation life and the fatigue crack growth life.

[0159] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.14 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store fatigue life prediction data of metal materials considering non-uniform grain size distribution. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the fatigue life of metal materials considering non-uniform grain size distribution is implemented.

[0160] Those skilled in the art will understand that Fig.14The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0161] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0162] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0163] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0164] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0165] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0166] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0167] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0168] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting fatigue life of metal materials considering non-uniform grain size distribution, characterized in that: The fatigue life prediction method of metal materials considering non-uniform grain size distribution includes: Obtaining material parameters of a metal material; the material parameters include: grain size; Determine a crystal plasticity constitutive model; the crystal plasticity constitutive model is obtained by evolving the dislocation density based on the initial total dislocation model according to the material parameters; the crystal plasticity constitutive model includes: a statistical dislocation density evolution model and a geometrically required dislocation density evolution model; the statistical dislocation density evolution model is a mathematical model for adjusting the size correlation coefficient of the annihilation of the edge-type statistical storage dislocation and the size correlation coefficient of the annihilation of the screw-type statistical storage dislocation; the geometrically required dislocation density evolution model is a mathematical model for controlling the size correlation coefficient of the geometrically required dislocation accumulation process and the size correlation coefficient of the geometrically required dislocation annihilation process; Based on the crystal plastic constitutive model, a parameter group is determined; the parameter group is determined by a simulation fitting method according to the dislocation density evolution and the stress-strain relationship; the parameter group includes: a reference slip rate and a plastic flow parameter; Constructing a microstructure model to be tested; the microstructure model to be tested is a finite element model obtained by performing finite element analysis on the metal material; Based on the microstructure model to be tested, according to the crystal plasticity constitutive model and the parameter group, set loading conditions and set boundary conditions are applied to determine material fatigue data; the material fatigue data includes: fatigue behavior and corresponding data; Determining fatigue crack initiation life based on the fatigue indicator factor and the fatigue data of the material; Determine the fatigue crack growth life by fitting according to the fatigue crack growth curve; the fatigue crack growth curve is determined based on the fatigue data of the material; The fatigue life is determined according to the fatigue crack initiation life and the fatigue crack growth life.

2. The method for predicting fatigue life of metal materials considering non-uniform grain size distribution according to claim 1, characterized in that: The initial total dislocation model specifically includes: Among them, ρ total is the initial total dislocation model; ρ S0 is the initial statistical dislocation density; ρ G0 is the initial geometric required dislocation density; d is the grain size; N is the total number of slip systems; The initial geometry of the αth slip system must be the dislocation density.

3. The method for predicting fatigue life of metal materials considering non-uniform grain size distribution according to claim 1, characterized in that: The statistical dislocation density evolution model includes: Among them, the statistical dislocation density includes: the edge statistical dislocation density of the αth slip system and the screw statistical dislocation density of the αth slip system is the edge statistical dislocation density for the αth slip system The evolution of ; b is the Burger vector; To adjust the size correlation coefficient of edge-shaped statistical storage dislocation annihilation; is the screw statistical dislocation density for the αth slip system The evolution of P i α is the mean free path associated with the total dislocation density; is the size correlation coefficient for adjusting the annihilation of screw-type statistical storage dislocations; N is the total number of slip systems; is the average edge dislocation spacing of the αth slip system; is the average screw dislocation spacing of the αth slip system; is the average dislocation spacing of the αth slip system; is the slip rate of the αth slip system; K i is the proportional parameter controlling the dislocation motion; e is the edge dislocation; s is the screw dislocation; is the edge statistical dislocation density of the βth slip system; is the screw statistical dislocation density of the βth slip system; is the geometric required dislocation density of the βth slip system; and All are reference coefficients; k e and k s are proportional factors; d c is the reference grain size for controlling dislocation annihilation at grain boundaries; d is the grain size.

4. The method for predicting fatigue life of metal materials considering non-uniform grain size distribution according to claim 1, characterized in that: The geometrically required dislocation density evolution model includes: in, is the necessary dislocation component of the pure screw geometry parallel to the m direction of the αth slip system; is the necessary dislocation component of the pure edge geometry parallel to the t direction of the αth slip system; is the necessary dislocation component of pure edge geometry parallel to the n direction of the αth slip system, m is the slip direction, n is the normal to the slip plane, t = m × n; To control the geometry the size dependence of the dislocation accumulation process is necessary; The size-dependent coefficient of the dislocation annihilation process is necessary to control the geometry; α Nye is the Nye factor; b is the Burger vector; curl() is the sum of the spatial gradients of the variables at the current integration point and the surrounding integration points; is the shear amount of the potential slip system; F p is the plastic strain gradient; is the geometric necessary dislocation density accumulated during the deformation of the αth slip system; is the cumulative geometric necessary dislocation density change rate during the deformation of the αth slip system parallel to the t direction; is the cumulative geometric necessary dislocation density change rate during the deformation of the αth slip system parallel to the n direction; is the cumulative geometric dislocation density change rate of the αth slip system parallel to the m direction during deformation; m α is the unit vector of the slip direction of the αth slip system; in, and n are dimensionless coefficients; is the saturation density of geometrically required dislocations; d c is the reference grain size for controlling dislocation annihilation at grain boundaries; d is the grain size; is the initial geometric required dislocation density of the αth slip system; The back stress of each slip system in the crystal plasticity constitutive model is: Among them, X α is the back stress of the αth slip system; μ is the shear modulus; is the geometrically required dislocation stacking number in the effective area of ​​the αth slip system; v is the Poisson's ratio; w eff is the width of the dislocation pileup.

5. The method for predicting fatigue life of metal materials considering non-uniform grain size distribution according to claim 1, characterized in that: The storage energy density is used as the fatigue indicator factor; the method for determining the fatigue crack initiation life specifically includes: Where ζ is the fraction of plastic dissipated energy stored in the dislocation structure; G cri is the cumulative energy density threshold of the local area within the material; G j is the accumulated storage energy density in the jth week; n is a dimensionless coefficient; G is the storage energy density; W is the dissipated energy density; ρ S is the total statistical dislocation density; ρ Gp is the total geometric required dislocation density; τ α is the shear stress of the αth slip system; is the shear amount of the α-slip potential slip system; N i is the fatigue crack initiation life.

6. The method for predicting fatigue life of metal materials considering non-uniform grain size distribution according to claim 1, characterized in that: The calculation formula of the fatigue crack growth life is: Among them, N p is the fatigue crack growth life; a0 is the initial crack length; a c is the critical length of crack; C and m are fitting parameters; ΔK is the stress intensity factor; Δσ is the stress amplitude; a is the crack length; F is the crack shape factor; W is the dissipated energy density.

7. A fatigue life prediction device for metal materials considering non-uniform grain size distribution, characterized in that: The metal material fatigue life prediction device considering non-uniform grain size distribution comprises: The material parameter acquisition module is used to acquire the material parameters of the metal material; the material parameters include: grain size; A crystal plasticity constitutive model determination module is used to determine the crystal plasticity constitutive model; the crystal plasticity constitutive model is obtained by evolving the dislocation density based on the initial total dislocation model according to the material parameters; the crystal plasticity constitutive model includes: a statistical dislocation density evolution model and a geometrically required dislocation density evolution model; the statistical dislocation density evolution model is a mathematical model for adjusting the size correlation coefficient of the annihilation of the edge-type statistical storage dislocation and the size correlation coefficient of the annihilation of the screw-type statistical storage dislocation; the geometrically required dislocation density evolution model is a mathematical model for controlling the size correlation coefficient of the geometrically required dislocation accumulation process and the size correlation coefficient of the geometrically required dislocation annihilation process; A parameter group determination module is used to determine a parameter group based on the crystal plasticity constitutive model; the parameter group is determined according to the dislocation density evolution and stress-strain relationship by using a simulation fitting method; the parameter group includes: a reference slip rate and a plastic flow parameter; A microstructure model construction module to be tested, used to construct a microstructure model to be tested; the microstructure model to be tested is a finite element model obtained by performing finite element analysis on the metal material; A material fatigue data determination module is used to determine material fatigue data based on the microstructure model to be measured, according to the crystal plasticity constitutive model and the parameter group, applying set loading conditions and set boundary conditions; the material fatigue data includes: fatigue behavior and corresponding data; A fatigue crack initiation life determination module, used to determine the fatigue crack initiation life according to the fatigue indication factor and the material fatigue data; A fatigue crack growth life determination module, used to determine the fatigue crack growth life by fitting according to a fatigue crack growth curve; the fatigue crack growth curve is determined based on the material fatigue data; The fatigue life determination module is used to determine the fatigue life according to the fatigue crack initiation life and the fatigue crack growth life.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the fatigue life prediction method for metal materials considering non-uniform grain size distribution as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the fatigue life of a metallic material taking into account the non-uniform grain size distribution as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the fatigue life of a metal material taking into account the non-uniform grain size distribution as described in any one of claims 1 to 6 is implemented.

Images