Grain orientation dispersibility-considered initiation life prediction method

Abstract

The invention relates to the technical field of structural strength and fatigue failure analysis, in particular to an initiation life prediction method considering grain orientation dispersity, which comprises the following steps: establishing a first number of test piece examination sections with different grain orientations, and determining slip bands of the test piece examination sections; setting a boundary condition and a fatigue loading condition for each test piece examination section, and performing fatigue loading finite element simulation to obtain the accumulated shear strain increment; based on the slip band corresponding to the maximum value of the accumulated shear strain increment, determining the initiation life of each test piece examination section; determining a convergent initiation life-cumulative distribution function of the examination section of the test piece according to the initiation life, and determining the function as an initiation life prediction result considering the grain orientation dispersibility; according to the method, the initiation life of the fatigue crack of the material can be accurately predicted.

Description

Technical Field

[0001] The invention relates to the technical field of structural strength and fatigue failure analysis, and in particular to a method for predicting initiation life taking into account grain orientation dispersion. Background Art

[0002] The turbine disk works in a high temperature and high speed environment and is the life-limiting part of the aircraft engine. With the increase of engine generation, the temperature before the turbine continues to rise, and the turbine disk is subjected to increasing thermal and mechanical stress, which makes fatigue failure very easy. Therefore, accurately predicting the life of aircraft engine turbine disks is the key to preventing aviation accidents.

[0003] Under fatigue load, the evolution process of fatigue crack is: initiation stage, extension stage, and transient stage. The transient stage is very short and generally negligible. At present, the life prediction method for fatigue crack extension stage is relatively mature, but the initiation life prediction method for fatigue crack initiation stage is still unclear. For high-temperature alloy materials for aviation, the initiation stage accounts for the vast majority of the total fatigue life. Therefore, it is necessary to carry out initiation life prediction research.

[0004] Slip band cracking in grains induces crack initiation, so it is necessary to simulate crack initiation at the scale of grain slip bands. Initiation is affected by the grain orientation at the initiation position, resulting in great dispersion of the initiation life. Studies have shown that when repeated tests are carried out under the same test conditions, the difference in initiation life of different test pieces caused by the difference in grain orientation at the initiation position can reach more than 5 times. It can be seen that the randomness of grain orientation is the key factor causing the dispersion of initiation life. However, the existing initiation life prediction model does not consider the influence of grain orientation dispersion on the initiation life, and cannot accurately predict the initiation life.

[0005] In summary, if accurate prediction of the initiation life is to be achieved, it is necessary to simulate crack initiation at the grain slip band level and consider the effect of grain orientation dispersion in the initiation zone on the initiation life. Therefore, there is an urgent need for an initiation life prediction method that considers grain orientation dispersion and is required to be able to predict the initiation life under different grain orientation distributions. Summary of the invention

[0006] In view of the above problems, the present invention provides a method for predicting the initiation life of fatigue cracks in consideration of the dispersion of grain orientation, which solves the technical problem in the prior art that it is difficult to accurately predict the initiation life of fatigue cracks in materials.

[0007] The present invention provides a method for predicting the initiation life taking into account the dispersion of grain orientation, comprising the following steps:

[0008] Step S1, establishing a first number of test pieces with different grain orientations, and determining the slip bands of the test pieces;

[0009] Step S2, according to the actual displacement constraint and the test loading condition, the boundary condition and the fatigue loading condition are set for each test section of the test piece, and the fatigue loading finite element simulation is performed, and when the cumulative shear strain increment of each slip system in the slip band is stable, the cumulative shear strain increment is obtained;

[0010] Step S3, determining the initiation life of each test piece assessment section based on the slip band corresponding to the maximum value of the cumulative shear strain increment;

[0011] Step S4, determining the initiation life-cumulative distribution function of the current first number of test piece assessment sections based on the initiation life of each test piece assessment section, and judging whether the initiation life-cumulative distribution function converges as the first number increases;

[0012] When the initiation life-cumulative distribution function does not converge, increase the first quantity and return to step S1;

[0013] When the initiation lifetime-cumulative distribution function converges, the converged initiation lifetime-cumulative distribution function is determined as an initiation lifetime prediction result considering grain orientation dispersion.

[0014] Preferably, step S1 specifically includes:

[0015] Step S1-1, according to the shape and size of the test piece test section, establish a first number of test piece test section finite element models, divide the test piece test section finite element model into a grain area and a non-grain area, the grain area is located in the notch area of ​​the test piece, and the part outside the grain area is the non-grain area; set the material property of the grain area to a rate-dependent crystal plasticity constitutive model, and set the material property of the non-grain area to an isotropic material;

[0016] Step S1-2, assigning randomly generated grain orientation parameters to the grains in the grain zone;

[0017] Step S1-3: divide each grain in the grain area into slip bands according to the vector direction of each slip plane of the test piece test section to obtain the slip bands of the test piece test section.

[0018] Preferably, in step S1-1, setting the material properties of the grain region to a rate-dependent crystal plasticity constitutive model specifically comprises: using FORTRAN language to write a UMAT user material subroutine to describe the deformation behavior of the test piece assessment section, including:

[0019] An exponential model is used to describe the slip shear strain rate of the crystal

[0020]

[0021]

[0022] in, is the slip shear strain rate of the αth slip system, is the reference shear strain rate, n is the sensitivity index, |·| n represents the calculation of the absolute value to the power of n, sign(·) represents the sign function, τ α is the decomposed shear stress of the αth slip system, g α is the slip hardening resistance of the αth slip system, g α The rate of change, h αβ is the latent hardening modulus, is the slip shear strain rate of the βth slip system, N is the total number of slip systems;

[0023] The Voce hardening model is used to describe the hardening behavior of the slip system:

[0024]

[0025] Where γ is the cumulative shear strain of all slip systems, h αα (γ) is the self-hardening modulus of the accumulated shear strain γ, h 0 、h s , τ s are the initial hardening modulus, saturation hardening modulus, and saturation shear stress, respectively; exp(·) represents the natural exponential function; q is a constant that characterizes the relationship between latent hardening and self-hardening behaviors of the material; and t represents time;

[0026] The accumulated shear strain γ of the αth slip system α The expression is:

[0027]

[0028] The plastic constitutive relation of rate-dependent crystals adopts the Jaumann rate form, which is expressed as follows:

[0029]

[0030] Among them, I is the second-order unit tensor, L is the elastic modulus tensor with a set of completely symmetric properties, is the co-rotation stress rate on the lattice rotation axis, σ is the stress tensor, D * is the elastic part of the deformation rate tensor.

[0031] Preferably, step S1-2 specifically includes:

[0032] Write a MATLAB script, use the orthogonal triangulation decomposition method to generate random crystal axis vector information with the same number of grains in the grain zone, and output it to a table file, write a python script to read the random crystal axis vector information in the table file, run the python script in ABAQUS, and assign grain orientation parameters corresponding to the random crystal axis vector information to each grain in the grain zone.

[0033] Preferably, step S2 specifically includes:

[0034] Step S2-1, determining the boundary conditions of the displacement constraint, including the position of the hinge and the position of the displacement constraint node; determining the fatigue loading conditions, including the maximum load, stress ratio and loading frequency; determining the initial value of the number of cycles;

[0035] Step S2-2, performing fatigue loading finite element simulation on the test piece test section according to the boundary conditions, fatigue loading conditions and cycle number;

[0036] Step S2-3, obtaining the cumulative shear strain mean increment of a single slip system after the fatigue loading of the last cycle number is completed, and determining whether the cumulative shear strain mean increment is a constant value;

[0037] If it is not a constant value, the cycle number is increased and the process returns to step S2-2; if it is a constant value, the cumulative shear strain increment of all slip systems in all slip bands under the last cycle number is output.

[0038] Preferably, step S3 specifically includes:

[0039] The initiation life of the test piece is predicted based on the maximum value of the cumulative shear strain increment of each single slip system and the Tanaka-Mura model; the Tanaka-Mura model is expressed by the following formula:

[0040]

[0041] Where d is the slip band length, Δγ α is the cumulative shear strain increment of the ath slip system, FIP α is the critical plane fatigue indicator parameter, max(·) means the maximum value, A TM is the material constant, N i For the germination life span.

[0042] Preferably, in step S4, the expression of the initiation life-cumulative distribution function is:

[0043] F(x)=P(X≤x)

[0044] Among them, X is a random variable representing the initiation life, and x is an arbitrary real number.

[0045] Preferably, in step S4, the method for judging whether the initiation life-cumulative distribution function converges as the first number increases is: judging whether the curves of the initiation life-cumulative distribution function under different first numbers are consistent; if the curves are consistent, it is judged that the convergence state has been reached, otherwise, it is judged that the convergence state has not been reached.

[0046] Compared with the prior art, the present invention has at least the following beneficial effects:

[0047] (1) The present invention simulates the microstructure and anisotropic properties of the material in detail by introducing the division of grain area and non-grain area in the finite element model and using randomly generated grain orientation parameters. By calculating and analyzing the cumulative shear strain increment under different grain orientations, the significant effect of grain orientation dispersion on fatigue crack initiation life is revealed.

[0048] (2) The present invention batch generates multiple finite element models with different grain orientations, repeatedly performs fatigue loading simulation and cumulative shear strain calculation, and finally establishes a cumulative distribution function curve of the initiation life, which effectively considers the influence of grain orientation dispersion on the fatigue behavior of the material. By statistically analyzing the initiation life under different grain orientations, the fatigue crack initiation life of the material under actual working conditions can be more comprehensively predicted.

[0049] (3) The present invention characterizes the initiation life of different grain orientations under the same test conditions in a probabilistic form, providing a reliable basis for the prediction of fatigue crack initiation life. The convergence analysis of the cumulative distribution function curve ensures the stability and reliability of the prediction results, providing a guarantee for the safety design of components in engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0051] Figure 1 A flow chart of the initiation life prediction method considering grain orientation dispersion provided by the present invention.

[0052] Figure 2 A detailed flow chart of the initiation life prediction method considering grain orientation dispersion provided by the present invention.

[0053] Figure 3 Schematic diagram of the macro-micro crystal plasticity finite element model of the test piece assessment section provided by the present invention.

[0054] Figure 4 Schematic diagram of boundary conditions and fatigue loading conditions of the test piece model provided by the present invention.

[0055] Figure 5The present invention provides a curve diagram showing the variation of the average value of the cumulative shear strain of 12 slip systems in the grain region with the number of cycles.

[0056] Figure 6 The cumulative shear strain cloud diagram of the grain zone (1-11)

[110] slip system provided by the present invention.

[0057] Figure 7 The scatter plot of the initiation life of the test pieces under 500 different grain orientations provided by the present invention.

[0058] Figure 8 This is a schematic diagram of the initiation life-cumulative distribution function curve under different sample numbers provided by the present invention. DETAILED DESCRIPTION

[0059] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0060] In order to illustrate the effectiveness of the method proposed in the present invention, the above technical solution of the present invention is described in detail below through a specific embodiment. A specific embodiment of the present invention is as follows: Figure 1 , Figure 2 As shown, a method for predicting the initiation life considering the grain orientation dispersion is disclosed, and the specific implementation steps are as follows:

[0061] Step S1, establishing a first number of test pieces with different grain orientations, and determining the slip bands of the test pieces.

[0062] 1) Establish a finite element model according to the shape and size of the test piece test section, and establish a grain area in the notch area of ​​the finite element model based on the grain morphology characteristics near the notch of the test piece test section. The rest of the finite element model is modeled as a non-grain area, and the grain area is meshed;

[0063] In this step, if Figure 3 As shown in the figure, the test section of the test piece is modeled in ABAQUS, and the finite element model of the test section is divided into two areas: the grain area and the non-grain area. The stress concentration in the notch area of ​​the test piece will induce crack initiation, so the grain area is established in the notch area of ​​the test piece, and the non-grain area is established away from the notch area. According to the grain morphology near the notch of the test section of the test piece, the Thiessen polygon method is used to generate grains in the grain area. Figure 3The medium grain area contains a total of 190 grains. The macro-micro finite element modeling method combining the grain area and the non-grain area can not only realize the microscopic simulation of the initiation zone, but also reduce the simulation time cost. In order to accurately simulate crack initiation, the grain area grid is encrypted and the grain area grid size is set to one seventh of the grain size to meet the grid independence.

[0064] 2) Assign material properties to the finite element model of the test piece assessment section, and use isotropic material properties and rate-dependent crystal plasticity constitutive model to describe the material properties of the non-grained region and the grained region respectively.

[0065] In this step, isotropic material properties are used to describe the material properties of the non-grain region, and a rate-dependent crystal plasticity constitutive model is used to describe the material properties of the grain region. For the rate-dependent crystal plasticity constitutive model, specifically, a UMAT user material subroutine can be written in FORTRAN language to describe the deformation behavior of the metal material.

[0066] In the rate-dependent crystal plasticity constitutive model, an exponential model is used to describe the slip shear strain rate of the crystal. As shown in formulas (1) and (2):

[0067]

[0068]

[0069] in, is the slip shear strain rate of the αth slip system, is the reference shear strain rate, n is the sensitivity index, |·| n represents the calculation of the absolute value to the power of n, sign(·) represents the sign function, τ α is the decomposed shear stress of the αth slip system, g α is the slip hardening resistance of the αth slip system, g α The rate of change, h αβ is the latent hardening modulus, is the slip shear strain rate of the βth slip system, and N is the total number of slip systems.

[0070] In the rate-dependent crystal plasticity constitutive model, the hardening behavior of the slip system is described by the Voce hardening model, as shown in formulas (3)-(5):

[0071]

[0072] h αβ =qh αα (γ) (β≠α) (4)

[0073]

[0074] Where γ is the cumulative shear strain of all slip systems, h αα (γ) is the self-hardening modulus of the accumulated shear strain γ, h 0 、h s , τ s are the initial hardening modulus, saturation hardening modulus, and saturation shear stress, respectively; exp(·) represents the natural exponential function; q is a constant that characterizes the relationship between latent hardening and self-hardening behaviors of the material; and t represents time.

[0075] In the rate-dependent crystal plasticity constitutive model, the cumulative shear strain γ of the αth slip system is α It is expressed by formula (6):

[0076]

[0077] The plastic constitutive relation of rate-dependent crystals is expressed in the Jaumann rate form proposed by Hill:

[0078]

[0079] Where I is the second-order unit tensor, L is the elastic modulus tensor with a set of completely symmetric properties, is the co-rotation stress rate on the lattice rotation axis, σ is the stress tensor, D * is the elastic part of the deformation rate tensor.

[0080] The present invention adopts FGH96 alloy metal material for modeling. The isotropic material property parameters of FGH96 alloy at 530°C are shown in Table 1, and the rate-dependent crystal plasticity constitutive model parameters are shown in Table 2.

[0081] Table 1

[0082]

[0083] Table 2

[0084]

[0085] Among them, C11, C12, and C44 are elastic constants.

[0086] 3) Write a MATLAB script to generate random grain orientation parameters, write a python script to read the grain orientation parameters, and run the python script in ABAQUS to assign grain orientation parameters to the grains in the grain zone.

[0087] In this step, the grain orientation parameters are assigned to the grains in the grain zone, and the grain orientation is represented by the crystal axis vector. A MATLAB script is written to use orthogonal triangulation (QR) decomposition to generate random crystal axis vectors with the same number of grains in the grain zone, and output them to a table file. A python script is written to read the crystal axis vector information in the table file, and the python script is run in ABAQUS to assign grain orientation parameters to each grain in the grain zone.

[0088] 4) Divide each grain in the grain area into slip bands according to the vector direction of each slip plane of the metal material, obtain the slip band division results of the grain area under different slip planes, and output the unit number information contained in each slip band;

[0089] The FGH96 alloy used in the experiment of the present invention is a metal with a face-centered cubic structure and a slip system of {111} <110> , with four slip planes, three slip directions, and a total of 12 slip systems. The four slip planes of FGH96 alloy are: slip plane 1 (11 1), slip plane 2 (-1 1 1), slip plane 3 (1 -1 1), and slip plane 4 (11-1). The grains in the grain area are divided according to the vector direction of the slip plane and the width of the slip band, and each grain is divided into several slip bands. The script is written in python language to realize the automatic division of the slip bands of the grains in the grain area of ​​ABAQUS, and the unit number information contained in each slip band in each grain under the four slip planes is output to a text file.

[0090] Through the above method, batch modeling is carried out to obtain finite element models of test sections of multiple test pieces with different grain orientations and slip band division methods. These model information will be used for batch analysis in subsequent steps.

[0091] The specific process of batch modeling is: batch generate num test pieces with different grain orientations for the macro-micro crystal plasticity finite element model, and the initial value of num is 500.

[0092] Step S2: according to the actual displacement constraints and the test loading conditions, boundary conditions and fatigue loading conditions are set for each test section of the test piece, and fatigue loading finite element simulation is performed. When the cumulative shear strain increment of each slip system in the slip band is stable, the cumulative shear strain increment is obtained.

[0093] In step S2, fatigue loading finite element simulation is performed on each finite element model generated in batches. The specific process includes:

[0094] 5) Set the boundary conditions and fatigue loading conditions of the model in ABAQUS according to the actual displacement constraints and experimental loading conditions, and run the finite element simulation after the settings are completed.

[0095] In this embodiment, the boundary conditions and fatigue loading conditions of the model are as follows: Figure 4 As shown. The boundary conditions are as follows: the left side of the model is hinged, and the displacement in the X and Y directions is constrained at the left bottom node. The fatigue loading conditions are as follows: the maximum load is 860MPa, the stress ratio is 0.2, the frequency is 10Hz, the loading waveform is a sine wave, the initial value of the number of cycles is 10, and the test piece model is fatigue loaded with the said number of cycles.

[0096] Run the finite element simulation to obtain the cumulative shear strain mean increment of a single slip system after the fatigue loading of the last cycle is completed. After the ABAQUS simulation is completed, write a python script to extract the cumulative shear strain mean value of the 12 slip systems in the grain area of ​​the FGH96 alloy in each cycle, output it to a text file, and draw a curve of the cumulative shear strain mean value of the 12 slip systems in the grain area versus the number of cycles, as shown in Figure 5 The results show that when the number of cycles is 10, the mean increment of the accumulated shear strain of a single slip system under a single cycle is a constant. Therefore, in this embodiment, the shear strain increment of the slip system when the number of cycles is 10 is used for the prediction of the initiation life.

[0097] 6) Write a python script to extract the cumulative shear strain γ of all slip systems in all slip bands in the grain region at the start and end of the cycle under the last cycle number α .

[0098] Specifically, in some embodiments, according to the slip band division result output in step 4), a python script is written to extract the (1 1 1) [0 -11] slip system, (1 1 1) [1 0 -1] slip system, and (1 1 1) [-1 1 0] slip system in slip plane 1 (11 1) at the start time and the end time of all slip bands under the 10th cycle number; the (-1 11) [1 0 1] slip system, (-1 1 1) [1 1 0] slip system, and (-1 1 1) [0 -1 1] slip system in slip plane 2 (-1 1 1); the (1 -1 1) [0 1 1] slip system, (1 -1 1) [1 1 0] slip system, and (1 -1 1) [1 0 -1] slip system in slip plane 3 (1 -1 1); the (1 -1 1) [0 1 1] slip system, (1 -1 1) [1 1 0] slip system, and (1 -1 1) [1 0 -1] slip system in slip plane 4 (11 The cumulative shear strain γ of the (1 1 -1)[0 1 1] slip system, (1 1 -1)[1 0 1] slip system, and (1 1 -1)[-1 1 0] slip system in α .

[0099] Step S3: determining the initiation life of the test section of the test piece based on the slip band corresponding to the maximum value of the cumulative shear strain increment.

[0100] 7) Write a MATLAB script to calculate the cumulative shear strain increment Δγ of all slip systems in all slip bands under the last cycle number α , and extract the cumulative shear strain increment Δγ of a single slip system α The maximum value of and its corresponding slip band.

[0101] In this step, a MATLAB script is written to calculate the cumulative shear strain increment Δγ of 12 slip systems in the slip band when the number of cycles is 10. α , output the cumulative shear strain increment Δγ of a single slip system in all slip bands α The maximum value of and its corresponding slip band. The maximum single slip system cumulative shear strain increment Δγ α The slip zone is the crack initiation location. Figure 6 The cumulative shear strain cloud diagram of the (1 -1 1) [1 1 0] slip system in the grain region. The grain circled in the box contains the grain with the largest cumulative shear strain increment Δγ of the single slip system. α The grains of the slip band. The numbers in the box are the unit numbers in ABAQUS. The slip band where cracks initiate is composed of units with unit numbers of 2501, 2201, 2202, 2301, 2302, 2303, 2401, 2402, 2403, and 2502. The slip system that induces the cracking of the slip band is (1 -1 1) [1 1 0]. The corresponding single slip system cumulative shear strain increment Δγ α The maximum value of is 0.027749.

[0102] 8) The cumulative shear strain increment Δγ of each single slip system obtained in step 7) α The maximum value of and Tanaka-Mura model predict the initiation life of the test piece;

[0103] In this step, the initiation life is predicted according to the Tanaka-Mura model, which is expressed by formula (8) and formula (9):

[0104]

[0105]

[0106] Where d is the slip band length, Δγ α is the cumulative shear strain increment of the ath slip system, FIP α is the critical plane fatigue indicator parameter, max(·) means the maximum value, A Tm is the material constant, N i For the germination life span.

[0107] Material constant A of FGH96 alloy at 530℃TM It is 18.20 μm. According to formula (8) and formula (9), the initiation life of the test piece is calculated to be 14898 cycles.

[0108] Step S4, determining the initiation life-cumulative distribution function of the current first number of test piece assessment sections based on the initiation life of each test piece assessment section, and judging whether the initiation life-cumulative distribution function converges with the increase of the first number; when the initiation life-cumulative distribution function does not converge, increase the first number and return to step S1; when the initiation life-cumulative distribution function converges, determining the converged initiation life-cumulative distribution function as the initiation life prediction result considering the grain orientation dispersion.

[0109] 9) For the batch generated num (initial value is 500) test pieces with different grain orientations, the macro-micro crystal plasticity finite element model is evaluated, and the initiation life of the test pieces under 500 different grain orientations is obtained, such as Figure 7 Shown is a scatter plot of the initiation life of the test pieces under 500 different grain orientations in the present invention.

[0110] 10) Establish the initiation life-cumulative distribution function curve under different sample numbers to determine the appropriate sample. If the initiation life-cumulative distribution function curve reaches convergence under num sample numbers, the initiation life prediction considering the grain orientation dispersion is completed; if the initiation life-cumulative distribution function curve does not reach the convergence state, set num=num+100, and re-execute steps 1) to 10) until the initiation life-cumulative distribution function curve reaches the convergence state.

[0111] In this step, the initiation life-cumulative distribution function curve is used to realize the probabilistic characterization of the initiation life, so as to consider the influence of grain orientation dispersion on the initiation life. The initiation life-cumulative distribution function is expressed by formula (10):

[0112] F(x)=P(X≤x)(10)

[0113] Among them, X is a random variable representing the initiation life, and x is an arbitrary real number.

[0114] According to formula (10), the initiation life-cumulative distribution function curves under different sample numbers are established to determine the appropriate sample number. Figure 8 As shown in the figure, the initiation life-cumulative distribution function curves were established for 100, 200, 300, 400, and 500 samples. Figure 8It can be seen that when the number of samples reaches more than 200, there is no obvious difference in the initiation life-cumulative distribution function curve under different sample numbers, and the curve reaches a convergence state. Therefore, in this embodiment, 500 samples are used to establish the initiation life-cumulative distribution function curve to meet the research needs.

[0115] After executing all the above steps, the prediction of initiation life considering grain orientation dispersion is completed. This method takes into account the influence of grain orientation dispersion on initiation life and realizes the probabilistic characterization of initiation life. According to the initiation life-cumulative distribution function curve, all possible initiation lives of the test piece under the same test conditions and the corresponding probability of occurrence of the initiation life can be obtained, which provides a guarantee for fatigue crack initiation life prediction and component safety design.

[0116] Although the specific embodiments of the present invention have been described in a specific order, each action or step should be understood to require that such action or step be performed in the specific order shown or in a sequential order, or require that all illustrated actions or steps should be performed to obtain the desired result. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination.

[0117] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting the initiation life considering the dispersion of grain orientation, characterized in that: The following steps are involved: Step S1, establishing a first number of test pieces with different grain orientations, and determining the slip bands of the test pieces; Step S2, according to the actual displacement constraint and the test loading condition, the boundary condition and the fatigue loading condition are set for each test section of the test piece, and the fatigue loading finite element simulation is performed, and when the cumulative shear strain increment of each slip system in the slip band is stable, the cumulative shear strain increment is obtained; Step S3, determining the initiation life of each test piece assessment section based on the slip band corresponding to the maximum value of the cumulative shear strain increment; Step S4, determining the initiation life-cumulative distribution function of the current first number of test piece assessment sections based on the initiation life of each test piece assessment section, and judging whether the initiation life-cumulative distribution function converges as the first number increases; When the initiation life-cumulative distribution function does not converge, increase the first quantity and return to step S1; When the initiation lifetime-cumulative distribution function converges, the converged initiation lifetime-cumulative distribution function is determined as an initiation lifetime prediction result considering grain orientation dispersion.

2. The method for predicting the initiation life considering the grain orientation dispersion according to claim 1, characterized in that: Step S1 specifically includes: Step S1-1, according to the shape and size of the test piece test section, establish a first number of test piece test section finite element models, divide the test piece test section finite element model into a grain area and a non-grain area, the grain area is located in the notch area of ​​the test piece, and the part outside the grain area is the non-grain area; set the material property of the grain area to a rate-dependent crystal plasticity constitutive model, and set the material property of the non-grain area to an isotropic material; Step S1-2, assigning randomly generated grain orientation parameters to the grains in the grain zone; Step S1-3: divide each grain in the grain area into slip bands according to the vector direction of each slip plane of the test piece test section to obtain the slip bands of the test piece test section.

3. The method for predicting the initiation life considering the grain orientation dispersion according to claim 2, characterized in that: In step S1-1, setting the material properties of the grain region to the rate-dependent crystal plasticity constitutive model specifically includes: using FORTRAN language to write a UMAT user material subroutine to describe the deformation behavior of the test piece assessment section, including: An exponential model is used to describe the slip shear strain rate of the crystal in, is the slip shear strain rate of the αth slip system, is the reference shear strain rate, n is the sensitivity index, |·| n represents the calculation of the absolute value to the power of n, sign(·) represents the sign function, τ α is the decomposed shear stress of the αth slip system, g α is the slip hardening resistance of the αth slip system, g α The rate of change, h αβ is the latent hardening modulus, is the slip shear strain rate of the βth slip system, N is the total number of slip systems; The Voce hardening model is used to describe the hardening behavior of the slip system: h αβ =qh αα (γ) (β≠α) Where γ is the cumulative shear strain of all slip systems, h αα (γ) is the self-hardening modulus of the cumulative shear strain γ, h0, h s , τ s are the initial hardening modulus, saturation hardening modulus, and saturation shear stress, respectively; exp(·) represents the natural exponential function; q is a constant that characterizes the relationship between latent hardening and self-hardening behaviors of the material; and t represents time; The accumulated shear strain γ of the αth slip system α The expression is: The plastic constitutive relation of rate-dependent crystals adopts the Jaumann rate form, which is expressed as follows: Where I is the second-order unit tensor, L is the elastic modulus tensor with a set of completely symmetric properties, is the co-rotation stress rate on the lattice rotation axis, σ is the stress tensor, D * is the elastic part of the deformation rate tensor.

4. The method for predicting the initiation life considering the grain orientation dispersion according to claim 3, characterized in that: Step S1-2 specifically includes: Write a MATLAB script, use the orthogonal triangulation decomposition method to generate random crystal axis vector information with the same number of grains in the grain zone, and output it to a table file, write a python script to read the random crystal axis vector information in the table file, run the python script in ABAQUS, and assign grain orientation parameters corresponding to the random crystal axis vector information to each grain in the grain zone.

5. The method for predicting the initiation life considering the grain orientation dispersion according to claim 4, characterized in that: Step S2 specifically includes: Step S2-1, determining the boundary conditions of the displacement constraint, including the position of the hinge and the position of the displacement constraint node; determining the fatigue loading conditions, including the maximum load, stress ratio and loading frequency; determining the initial value of the number of cycles; Step S2-2, performing fatigue loading finite element simulation on the test piece test section according to the boundary conditions, fatigue loading conditions and cycle number; Step S2-3, obtaining the cumulative shear strain mean increment of a single slip system after the fatigue loading of the last cycle number is completed, and determining whether the cumulative shear strain mean increment is a constant value; If it is not a constant value, the cycle number is increased and the process returns to step S2-2; if it is a constant value, the cumulative shear strain increment of all slip systems in all slip bands under the last cycle number is output.

6. The method for predicting the initiation life considering the grain orientation dispersion according to claim 5, characterized in that: Step S3 specifically includes: The initiation life of the test piece is predicted based on the maximum value of the cumulative shear strain increment of each single slip system and the Tanaka-Mura model; the Tanaka-Mura model is expressed by the following formula: Where d is the slip band length, Δγ α is the cumulative shear strain increment of the ath slip system, FIP α is the critical plane fatigue indicator parameter, max(·) means the maximum value, A TM is the material constant, N i For the germination life span.

7. The method for predicting the initiation life considering the grain orientation dispersion according to claim 6, characterized in that: In step S4, the expression of the initiation life-cumulative distribution function is: F(x)=P(X≤x) Among them, X is a random variable representing the initiation life, and x is an arbitrary real number.

8. The method for predicting the initiation life considering the grain orientation dispersion according to claim 7, characterized in that: In step S4, the method of judging whether the initiation life-cumulative distribution function converges as the first number increases is: judging whether the curves of the initiation life-cumulative distribution function under different first numbers are consistent; If the curves are consistent, it is judged that the convergence state has been reached, otherwise, it is judged that the convergence state has not been reached.

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