A method for predicting initiation life considering grain orientation dispersion

Abstract

The present invention relates to the technical field of structural strength and fatigue failure analysis, and in particular to a method for predicting the initiation life taking into account the dispersion of grain orientation, comprising: establishing a first number of test piece test sections with different grain orientations, and determining the slip bands of the test piece test sections; setting boundary conditions and fatigue loading conditions for each of the test piece test sections, performing fatigue loading finite element simulation, and obtaining the cumulative shear strain increment; determining the initiation life of each test piece test section based on the slip band corresponding to the maximum value of the cumulative shear strain increment; determining a converged initiation life-cumulative distribution function of the test piece test section from the initiation life, and determining it as an initiation life prediction result taking into account the dispersion of grain orientation; the present invention can accurately predict the initiation life of fatigue cracks in a material.

Description

Technical Field

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

[0002] Turbine disks, operating at high temperatures and speeds, are life-limiting components in aircraft engines. As engine generations increase, temperatures in front of the turbine continue to rise, subjecting the disks to increasing thermal and mechanical stresses, making them susceptible to fatigue failure. Therefore, accurately predicting the life of aircraft engine turbine disks is crucial for preventing aviation accidents.

[0003] Under fatigue loading, a fatigue crack evolves through the following stages: initiation, propagation, and transient rupture. The transient rupture stage is very short and generally negligible. Currently, life prediction methods for the fatigue crack propagation stage are relatively mature, but methods for predicting the initiation life of fatigue cracks are less well-defined. For aviation high-temperature alloys, the initiation stage accounts for the vast majority of the total fatigue life. Therefore, research on initiation life prediction is necessary.

[0004] Slip band cracking in grains induces crack initiation, so crack initiation needs to be simulated at the grain slip band scale. Initiation is affected by the grain orientation at the initiation location, resulting in a significant dispersion in the initiation lifetime. Studies have shown that repeated tests under the same experimental conditions can result in initiation lifetimes of different specimens varying by more than five times due to differences in grain orientation at the initiation location. This suggests that the randomness of grain orientation is a key factor contributing to the dispersion in initiation lifetime. However, existing initiation lifetime prediction models do not consider the impact of grain orientation dispersion on initiation lifetime and cannot accurately predict initiation lifetime.

[0005] In summary, accurate prediction of the initiation lifetime requires simulating crack initiation at the grain slip band level and considering the effect of grain orientation dispersion in the initiation zone on the initiation lifetime. Therefore, there is an urgent need for an initiation lifetime prediction method that considers grain orientation dispersion and can predict the initiation lifetime 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 lifespan taking into account the dispersion of grain orientation, comprising the following steps:

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

[0009] Step S2: setting boundary conditions and fatigue loading conditions for each test section of the test piece according to actual displacement constraints and test loading conditions, performing fatigue loading finite element simulation, and obtaining the cumulative shear strain increment when the cumulative shear strain increment of each slip system in the slip band stabilizes;

[0010] Step S3: determining the initiation life of each test section of the test piece 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 first number of test piece test sections based on the initiation life of each test piece test section, and determining 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: establishing a first number of finite element models of the test piece test sections based on the shape and size of the test piece test sections, dividing the finite element models of the test piece test sections into a grain region and a non-grain region, wherein the grain region is located in the notch region of the test piece, and the portion outside the grain region is the non-grain region; setting the material properties of the grain region to a rate-dependent crystal plasticity constitutive model, and setting the material properties of the non-grain region 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 region into slip bands according to the vector direction of each slip plane of the test section of the test piece, to obtain the slip bands of the test section of the test piece.

[0018] Preferably, in step S1-1, setting the material properties of the grain region to a 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 test 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 Indicates the calculation of the absolute value to the power of n, sign(·) indicates 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 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.

[0026] The cumulative 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] Where I is the second-order unit tensor, L is the elastic modulus tensor with a set of completely symmetric properties, is the corotational 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 area, 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 the grain orientation parameters corresponding to the random crystal axis vector information to each grain in the grain area.

[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; and determining the initial value of the number of cycles;

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

[0036] Step S2-3, obtaining a cumulative shear strain mean increment of a single slip system after the fatigue loading of the last cycle 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 increments of all slip systems in all slip bands under the last cycle number are output.

[0038] Preferably, step S3 specifically includes:

[0039] The initiation life of the specimen 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] Where X is a random variable representing the initiation life span, 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) This paper simulates the microstructure and anisotropic properties of the material in detail by introducing a division between grain and non-grain regions in the finite element model and using randomly generated grain orientation parameters. By calculating and analyzing the cumulative shear strain increments 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, effectively considering 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) This invention characterizes the initiation life of different grain orientations under the same test conditions in a probabilistic manner, providing a reliable basis for fatigue crack initiation life prediction. 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 purposes of illustrating particular embodiments and are not to be considered limiting of the invention.

[0051] Figure 1 A flow chart of the method for predicting the initiation life taking into account the grain orientation dispersion provided by the present invention.

[0052] Figure 2 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 the boundary conditions and fatigue loading conditions of the test piece model provided by the present invention.

[0055] Figure 5This is a graph showing the variation of the average cumulative shear strain of 12 slip systems in the grain region provided by the present invention with the number of cycles.

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

[110] slip system in the grain region provided by the present invention.

[0057] Figure 7 This is a 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 objects, features and advantages of the present invention, the present invention is further described in detail below with reference to 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. Therefore, the scope of protection 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 dispersion of grain orientation 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 based on the shape and size of the test piece's test section, and establish a grain region in the notch area of ​​the finite element model based on the grain morphology characteristics near the notch of the test piece's test section. The remaining area of ​​the finite element model is modeled as a non-grain region, and the grain region is meshed;

[0063] In this step, if Figure 3 As shown in the figure, the test section of the test piece was modeled in ABAQUS, and the finite element model of the test section was divided into two regions: the grain region and the non-grain region. The stress concentration in the notch area of ​​the test piece will induce crack initiation, so the grain region was established in the notch area of ​​the test piece, and the non-grain region was established in the area away from the notch. According to the grain morphology near the notch of the test piece, the Thiessen polygon method was used to generate grains in the grain region. Figure 3The medium-grained region contains a total of 190 grains. A macro-micro finite element modeling approach combining the grained and non-grained regions enables microscopic simulation of the initiation zone while reducing simulation time. To accurately simulate crack initiation, the grained region mesh is encrypted, with the mesh size set to one-seventh of the grain size to ensure mesh 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-granular region and the crystalline region respectively.

[0065] In this step, isotropic material properties are used to describe the material properties of the non-grained region, and a rate-dependent crystal plasticity constitutive model is used to describe the material properties of the grained region. Specifically, a UMAT user material subroutine can be written in FORTRAN 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 Indicates the calculation of the absolute value to the power of n, sign(·) indicates 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 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.

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

[0076]

[0077] The plastic constitutive relation of rate-dependent crystals is expressed using 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 corotational 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 uses 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, grain orientation parameters are assigned to the grains in the grain zone, where grain orientation is represented by crystal axis vectors. A MATLAB script is written to generate random crystal axis vectors equal to the number of grains in the grain zone using orthogonal triangulation (QR) decomposition and output them to a spreadsheet file. A Python script is written to read the crystal axis vector information in the spreadsheet file and run the Python script in ABAQUS to assign grain orientation parameters to each grain in the grain zone.

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

[0089] The FGH96 alloy used in the present invention is a face-centered cubic metal with 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 zone 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. A script was written in Python to realize the automatic division of the slip bands of the grain zone in ABAQUS, and the unit number information contained in each slip band in each grain under the four slip planes was output to a text file.

[0090] Through the above method, batch modeling is carried out to obtain finite element models of the 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: setting boundary conditions and fatigue loading conditions for each test section of the test piece according to actual displacement constraints and test loading conditions, performing fatigue loading finite element simulation, and obtaining the cumulative shear strain increment when the cumulative shear strain increment of each slip system in the slip band is stable.

[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 model boundary conditions and fatigue loading conditions in ABAQUS according to the actual displacement constraints and experimental loading conditions, and run the finite element simulation after setting.

[0095] In this embodiment, the boundary conditions and fatigue loading conditions of the model are as follows: Figure 4 As shown in the figure, 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 lowest node on the left side. The fatigue loading conditions are as follows: maximum load of 860 MPa, stress ratio of 0.2, frequency of 10 Hz, loading waveform of sinusoidal wave, and initial number of cycles of 10. The specimen model is fatigue loaded with this 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 under each cycle, and output it to a text file, and draw a curve chart showing the cumulative shear strain mean value of the 12 slip systems in the grain area as a function of the cycle number, as shown in the following example: Figure 5 The results show that when the cycle number is 10, the mean increment of the cumulative shear strain of a single slip system under a single cycle is a constant value. Therefore, in this example, the shear strain increment of the slip system when the cycle number 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, (1 1 1) [-1 1 0] slip system in slip plane 1 (11 1); the (-1 11) [1 0 1] slip system, (-1 1 1) [1 1 0] slip system, (-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, (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, (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 under 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 the 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 of the (1-11)

[110] slip system in the grain region. The grain circled by 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 numbered 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 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 germination life is predicted based on 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 The initiation life of the test piece is 14898 cycles according to formula (8) and formula (9).

[0108] Step S4: determine 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 judge 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 number and return to step S1; when the initiation life-cumulative distribution function converges, determine 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 the number of num samples, 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 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] Where X is a random variable representing the initiation life span, 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 cumulative distribution function curves of initiation life under the conditions of 100, 200, 300, 400 and 500 samples were established respectively. Figure 8As can be seen, when the sample number reaches 200 or more, there is no significant difference in the initiation lifespan-CDF curves for different sample numbers, and the curves have reached a convergence state. Therefore, in this example, 500 samples were used to establish the initiation lifespan-CDF curve to meet research needs.

[0115] After completing all the above steps, the initiation life prediction considering grain orientation dispersion is complete. This method considers the influence of grain orientation dispersion on the initiation life and achieves a probabilistic representation of the initiation life. Based on the initiation life-cumulative distribution function curve, all possible initiation lives and their corresponding probability of occurrence for the specimen under the same test conditions are obtained, providing support for fatigue crack initiation life prediction and component safety design.

[0116] Although the specific embodiments of the present invention have been described in a particular order, it should be understood that such actions or steps are required to be performed in the particular order shown or in a sequential order, or that all illustrated actions or steps are required to be performed to obtain the desired result. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Some 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 embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for predicting initiation life considering grain orientation dispersion, characterized in that: The following steps are involved: Step S1, establishing a first number of test sections of the test piece with different grain orientations, and determining the slip bands of the test sections of the test piece; Step S2: setting boundary conditions and fatigue loading conditions for each test section of the test piece according to actual displacement constraints and test loading conditions, performing fatigue loading finite element simulation, and obtaining the cumulative shear strain increment when the cumulative shear strain increment of each slip system in the slip band stabilizes; Step S3: determining the initiation life of each test section of the test piece 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 first number of test piece test sections based on the initiation life of each test piece test section, and determining 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: establishing a first number of finite element models of the test piece test sections based on the shape and size of the test piece test sections, dividing the finite element models of the test piece test sections into a grain region and a non-grain region, wherein the grain region is located in the notch region of the test piece, and the portion outside the grain region is the non-grain region; setting the material properties of the grain region to a rate-dependent crystal plasticity constitutive model, and setting the material properties of the non-grain region 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 region into slip bands according to the vector direction of each slip plane of the test section of the test piece, to obtain the slip bands of the test section of the test piece.

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 test 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 Indicates the calculation of the absolute value to the power of n, sign(·) indicates 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 cumulative 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 corotational 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 area, 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 the grain orientation parameters corresponding to the random crystal axis vector information to each grain in the grain area.

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; and determining the initial value of the number of cycles; Step S2-2, performing fatigue loading finite element simulation on the test section of the test piece based on the boundary conditions, fatigue loading conditions and cycle number; Step S2-3, obtaining a cumulative shear strain mean increment of a single slip system after the fatigue loading of the last cycle 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 increments of all slip systems in all slip bands under the last cycle number are 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 specimen 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 lifespan.

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) Where X is a random variable representing the initiation life span, 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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