Rolling contact fatigue pitting corrosion prediction method considering crystal plasticity, corresponding computer readable storage medium and electronic equipment

Abstract

The invention belongs to the field of prediction of fatigue failure caused by surface defects under rolling contact loads, and particularly relates to a rolling contact fatigue pitting corrosion prediction method considering crystal plasticity, a corresponding computer readable storage medium and electronic equipment. The method comprises the following steps: S1, constructing a crystal plastic constitutive model of a coupling damage accumulation theory; s2, developing the model in the step 1 into a UMAT subprogram by using Fortran, and embedding ABAQUS into the UMAT subprogram; s3, establishing a representative volume element crystal plastic finite element model; s4, applying a periodic boundary condition and a shear load, obtaining a shear stress-strain curve, carrying out a torsional fatigue test, obtaining fatigue damage parameters of the material, and determining constitutive model parameters of the material; s5, establishing a crystal plastic finite element model of the roller and raceway contact pair; s6, applying boundary conditions in two steps; and S7, observing a damage evolution path to the surface, and obtaining the rolling contact fatigue pitting corrosion life. The method can fill the blank of rolling contact fatigue pitting corrosion prediction methods at present.

Description

Technical Field

[0001] The present invention belongs to the field of fatigue failure prediction caused by surface defects under rolling contact loads, and particularly relates to a pitting prediction method for rolling contact fatigue considering crystal plasticity, and a corresponding computer-readable storage medium and electronic device. Background Art

[0002] In recent years, with the continuous progress of heat treatment technology in China, the ability to control the size and uniformity of particulate matter in M50 bearing steel has been gradually improved, which has greatly reduced the probability of fatigue spalling originating from the interface of subsurface particulate matter. As a result, the probability of failure of aeroengine main shaft bearings caused by surface damage has increased significantly. Under extreme working conditions, seal failure occurs, resulting in the entry of contaminated particulate matter into the contact area, or a change in the lubrication state, and abrasive particles generated by wear cannot be carried away by the lubricating oil in time. At this time, the particulate matter and abrasive particles will remain in the contact area and form micro-indentations under the action of heavy loads, thereby causing high local stress and resulting in pitting failure.

[0003] At present, a rolling contact fatigue life prediction method for the rolling contact fatigue pitting failure mode has not been proposed yet. The prediction models for rolling contact fatigue evolution mainly use the extended finite element method (XFEM) based on fracture mechanics, the cohesive zone method (CZM) based on interface mechanics, and the continuous damage method (CDM) based on continuum damage mechanics as the main research methods. Since fracture mechanics is a discipline established based on the existence of cracks, XFEM is suitable for models that already contain prefabricated cracks or known crack initiation positions, and cannot predict crack bifurcation. However, the crack initiation position of rolling contact fatigue cannot be predicted in advance, and the propagation process is complex, so XFEM is not applicable to the fatigue failure analysis under rolling contact loads. CZM assumes that cracks initiate at grain boundary positions and propagate along grain boundaries. CDM assumes the existence of a damage variable, and when the damage variable at a certain position reaches a certain level, it is considered that the position fails. CDM assumes that damage is continuous, and has also been used by scholars to carry out research related to rolling contact fatigue. However, the propagation of fatigue cracks in bearing steel includes transgranular and intergranular fractures, and its real micro-topography determines that the damage is discontinuous. Therefore, the above models cannot effectively predict the rolling contact fatigue pitting phenomenon. Summary of the Invention

[0004] Aiming at the problems that the rolling contact fatigue pitting prediction model in the existing technology cannot accurately predict the transgranular and intergranular fractures in fatigue crack propagation and cannot predict the discontinuous damage caused by the real grain distribution, the purpose of the present invention is to provide a rolling contact fatigue pitting prediction method considering crystal plasticity, a corresponding computer-readable storage medium, and an electronic device, based on the method combining crystal plasticity finite element and continuum damage mechanics theory, so as to realize the effective prediction of rolling contact fatigue pitting and overcome the prediction deviation problem of the existing method.

[0005] Based on the above purpose, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a rolling contact fatigue pitting prediction method considering crystal plasticity, including the following steps:

[0007] Step 1, construct a crystal plasticity constitutive model coupling damage accumulation theory;

[0008] Step 2, develop the model in Step 1 into a UMAT subroutine using Fortran and embed it in ABAQUS;

[0009] Step 3, establish a crystal plasticity finite element model of a representative volume element;

[0010] Step 4, apply periodic boundary conditions and shear loads in the x, y, and z directions, obtain the shear stress-strain curve, conduct torsional fatigue tests, obtain the fatigue damage parameters of the material, and determine the material constitutive model parameters;

[0011] Step 5, establish a crystal plasticity finite element model of a roller and raceway contact pair;

[0012] Step 6, apply boundary conditions in two steps;

[0013] Step 7, observe the damage evolution path to the surface and obtain the rolling contact fatigue pitting life.

[0014] Further, the method for establishing the model in Step 1 is as follows:

[0015] (a) Calculate the velocity gradient tensor L, and the expression is:

[0016]

[0017] Among them, is the derivative of the deformation gradient tensor with respect to time; F -1 is the inverse of the deformation gradient tensor;

[0018] (b) Decompose the deformation gradient tensor F into two parts, namely the elastic and plastic parts, and the expression is:

[0019] F = Fe F p ,

[0020] where F e is the elastic deformation gradient tensor; F p is the plastic deformation gradient tensor;

[0021] (c) The velocity gradient tensor, stretching tensor, and lattice rotation tensor are also divided into elastic and plastic parts, and the expressions are:

[0022] D e + Ω e = L e ,

[0023] D p + Ω p = L p ,

[0024] where L e is the elastic velocity gradient tensor, D e is the elastic stretching tensor, Ω e is the elastic lattice rotation tensor, L p is the plastic velocity gradient tensor, D p is the plastic stretching tensor, Ω p is the plastic lattice rotation tensor;

[0025] (d) Assuming that plastic deformation comes entirely from dislocation slip, the plastic velocity gradient tensor L p is calculated from the shear rate of dislocation slip, and the expression is:

[0026]

[0027] where α is the slip system, N s is the number of slip systems, is the plastic shear rate of slip system α, s (α) and m (α) are the normal and tangential unit vectors of slip system α respectively, μ α and ω α are the symmetric and asymmetric components of the Schmid tensor respectively;

[0028] (e) The plastic shear rate model is a non-linear kinematic hardening model, and the damage parameter is embedded in the model to introduce the influence of damage, and the expression is:

[0029]

[0030] where is the initial shear rate, is the resolved shear stress (RSS) after damage, χ αis the back stress of the slip system, is the strength of the slip system, D is the damage value, and n is the rate sensitivity coefficient;

[0031] (f) The strength calculation expression of the slip system is:

[0032]

[0033] h αβ (γ) = h(γ)[q + (1 - q)δ αβ ,

[0034]

[0035] where h αβ is the slip system hardening modulus (including self-hardening and latent hardening), β is the slip system, γ is the total shear rate of the total slip system, is the plastic shear rate of the slip system β, q and h0 are material coefficients, h(γ) is the self-hardening modulus, and δ αβ is the Kronecker symbol, τ s is the saturated anti-slip resistance stress, and τ0 is the initial slip resistance stress;

[0036] (g) The calculation expression of RSS is:

[0037]

[0038] T * = C local (D):E e ,

[0039] where T * is the second Piola-Kirchoff stress, E e is the Green strain tensor, F eT is the transpose matrix of the elastic deformation gradient tensor, and C local (D) is the local crystal anisotropic stiffness matrix, and the expression is:

[0040] C local (D) = (1 - D)C local .

[0041] Furthermore, based on the damage evolution theory of RSS, the expression is as follows:

[0042]

[0043] where τ max is the maximum RSS in a cyclic load, and Δγ maxis the maximum cumulative plastic shear strain increment of a cyclic load, S and Q are material coefficients of the fatigue damage evolution model, E is Young's modulus, N is the number of load cycles, C 11 and C 12 are elastic stiffness constants.

[0044] Furthermore, the material is M50 steel with a cubic structure, and its stiffness matrix expression is:

[0045]

[0046] Furthermore, in step 5, a crystal plasticity finite element model of the contact pair of particles, rollers and raceways is established, a corresponding grain distribution Voronoi model is generated for the local contact area of the contact pair and replaces the original model, and finally a finite element model for predicting rolling contact fatigue pitting considering the microscopic grain distribution is established.

[0047] Furthermore, in step 6, the boundary conditions are applied in two steps. The first step is to apply and release the particle indentation load, and the second step is to apply the cyclic rolling contact load; the material parameters obtained in step 4 are input, the particle indentation simulation test is carried out, the rolling contact pair model after being pressed by the particles is obtained, and then a finite element model for predicting rolling contact fatigue pitting considering the microscopic grain distribution and indentation is obtained.

[0048] Furthermore, in step 7, the cyclic rolling contact load is applied, the damage evolution path to the surface is observed, and the corresponding number of cycles at this time is defined as the rolling contact fatigue pitting life.

[0049] The second aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a computer processor, any step in the method for predicting rolling contact fatigue pitting considering crystal plasticity as described in the first aspect is realized.

[0050] The third aspect of the present invention provides an electronic device, including a memory and a processor, on which a computer program is stored, and when the processor executes the computer program, any step in the method for predicting rolling contact fatigue pitting considering crystal plasticity as described in the first aspect is realized.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] 1. The method proposed by the present invention predicts the rolling contact fatigue pitting life, taking into account the influence of the microscopic crystal distribution characteristics, indentation morphology and indentation residual stress in the contact area. While realizing the life prediction, it can also predict the evolution process of rolling contact fatigue pitting, so as to carry out mechanism research, which has important significance for guiding the microstructure design of materials.

[0053] 2. The method proposed by the present invention for predicting the pitting life of rolling contact fatigue can fill the gap in the current methods for predicting rolling contact fatigue pitting. Description of the Drawings

[0054] Figure 1 is the flow chart of the present invention in Example 1;

[0055] Figure 2 is the crystal plasticity finite element model of the representative volume element in Example 1;

[0056] Figure 3 is the shear stress-strain curve in Example 1;

[0057] Figure 4 is the crystal plasticity finite element model of the raceway contact pair in Example 1;

[0058] Figure 5 is the local grain distribution of the raceway contact pair in Example 1;

[0059] Figure 6 is the schematic diagram of the indentation and rolling contact load in Example 1;

[0060] Figure 7 is the schematic diagram of the damage evolution simulation result in Example 1;

[0061] Figure 8 is the schematic diagram of the result of the damage evolution model without considering crystal plasticity in Example 1;

[0062] Figure 9 is the test observation result of the rolling contact fatigue crack on the surface layer and subsurface layer in Example 1;

[0063] Figure 10 is the comparison chart of the rolling contact fatigue test result and the result of this model in Example 1. Detailed Embodiment

[0064] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below through embodiments in conjunction with the drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] Example 1

[0066] This example provides a method for predicting the pitting of rolling contact fatigue considering crystal plasticity. The process is as Figure 1 shown, and includes the following steps:

[0067] Step 1, establish a crystal plasticity constitutive model and couple it with a continuous damage mechanics model, so as to construct a crystal plasticity constitutive model coupling damage accumulation theory. The specific process is as follows:

[0068] (1) Calculate the velocity gradient tensor L, with the expression:

[0069]

[0070] where, is the derivative of the deformation gradient tensor with respect to time; F -1 is the inverse of the deformation gradient tensor;

[0071] (2) Decompose the deformation gradient tensor F into two parts, namely the elastic and plastic parts, with the expression:

[0072] F = F e F p ,

[0073] where, F e is the elastic deformation gradient tensor; F p is the plastic deformation gradient tensor;

[0074] (3) Similarly, decompose the velocity gradient tensor, stretching tensor, and lattice rotation tensor into elastic and plastic parts, with the expression:

[0075] D e + Ω e = L e ,

[0076] D p + Ω p = L p ,

[0077] where, L e is the elastic velocity gradient tensor, D e is the elastic stretching tensor, Ω e is the elastic lattice rotation tensor, L p is the plastic velocity gradient tensor, D p is the plastic stretching tensor, Ω p is the plastic lattice rotation tensor;

[0078] (4) Assume that plastic deformation all comes from dislocation slip, and the plastic velocity gradient tensor L p is obtained by calculating the shear rate of dislocation slip, with the expression:

[0079]

[0080] where, α is the slip system, N s is the number of slip systems, is the plastic shear rate of the slip system α, s (α) and m (α) are the normal and tangential unit vectors of the slip plane α respectively, μ α and ωα are the symmetric and asymmetric components of the Schmid tensor, respectively;

[0081] (5) The plastic shear rate model is a non-linear kinematic hardening model, and the damage parameter is embedded in the model to introduce the influence of damage. The expression is:

[0082]

[0083] where is the initial shear rate, is the resolved shear stress (RSS) after damage, χ α is the back stress of the slip system, is the strength of the slip system, D is the damage value, and n is the rate sensitivity coefficient;

[0084] (6) The calculation expression for the strength of the slip system is:

[0085]

[0086] h αβ h(γ) = h(γ)[q + (1 - q)δ αβ ,

[0087]

[0088] where h αβ is the slip system hardening modulus (including self-hardening and latent hardening), β is the slip system, γ is the total shear rate of the total slip system, is the plastic shear rate of the slip system β, q and h0 are material coefficients, h(γ) is the self-hardening modulus, δ αβ is the Kronecker symbol, τ s is the saturated anti-slip resistance stress, and τ0 is the initial slip resistance stress;

[0089] (7) The calculation expression for RSS is:

[0090]

[0091] T * = C local (D):E e ,

[0092] where T * is the second Piola-Kirchoff stress, C local (D) is the local crystal anisotropy stiffness matrix, E e is the Green strain tensor, and F eT is the transpose matrix of the elastic deformation gradient tensor;

[0093] (8) Here, taking M50 steel with a cubic structure as an example, its stiffness matrix expression is as follows:

[0094]

[0095] C local (D) = (1 - D)C local 。

[0096] (9) In the crystal plasticity constitutive model, RSS is the main driving force for damage evolution. Therefore, this patent proposes a damage evolution theory based on RSS, and the expression is as follows:

[0097]

[0098] Where, τ max is the maximum RSS in a cyclic load, Δγ max is the maximum cumulative plastic shear strain increment in a cyclic load, S and Q are the material coefficients of the fatigue damage evolution model, E is the Young's modulus, N is the number of load cycles, C 11 and C 12 are elastic stiffness constants.

[0099] Step 2: Develop the model described in Step 1 into a UMAT subroutine using Fortran and embed it in ABAQUS;

[0100] Step 3: Based on the ABAQUS preprocessing module and according to the grain distribution characteristics of the target material, use the Voronoi plug-in to establish a representative volume element of the target material. In this implementation scheme, for M50 bearing steel with an average grain size of 10 μm, establish a crystal plasticity finite element model of the representative volume element. The established model is as Figure 2 shown.

[0101] Step 4: Apply periodic boundary conditions and shear loads in the x, y, and z directions based on the ABAQUS plug-in EasyPBC; input the material constitutive parameters; conduct a torsion test to determine the material constitutive parameters; conduct a simulation calculation to obtain the shear stress-strain curve, as Figure 3 shown, compare it with the torsion test results, correct and determine the constitutive model parameters ( n, q, h0, τ s ). As can be seen from Figure 3 , the results of this example have a small error compared with the test results. Then, conduct a torsional fatigue test to obtain the stress-life curve and fit the damage evolution parameters (S, Q).

[0102] Step 5: Based on the ABAQUS preprocessing module and the Voronoi plug-in, establish a crystal plasticity finite element model of the roller and the raceway contact pair; among them, the crystal plasticity finite element model of the raceway contact pair is as shown in Figure 4 shown, and the local microscopic grain distribution is as shown in Figure 5 shown.

[0103] Establish a crystal plasticity finite element model of the particulate matter, the roller and the raceway contact pair, generate a corresponding grain distribution Voronoi model for the local contact area of the contact pair and replace the original model, and finally establish a finite element model for predicting rolling contact fatigue pitting considering the microscopic grain distribution.

[0104] Step 6: Apply boundary conditions in two steps. In the first step, apply particulate indentation loading and release. In the second step, apply cyclic rolling contact load to achieve continuous load from indentation simulation to cyclic rolling contact loading, and retain the particulate indentation morphology and the residual stress introduced by the indentation. The application schematic diagram is as shown in Figure 6 shown.

[0105] Input the material parameters obtained in Step 4, carry out particulate indentation simulation tests, obtain the rolling contact pair model under the pressure of particulate matter, and further obtain a finite element model for predicting rolling contact fatigue pitting considering microscopic grain distribution and indentation.

[0106] Step 7: Implement simulation calculations to obtain the damage evolution results until the damage spreads to the surface, as shown in Figure 7 shown; record the number of cycles of the rolling contact load that spreads to the surface. At this time, its life is the corresponding life of rolling contact fatigue pitting. As shown in Figure 7 shown, the results predicted by this example can simultaneously include the evolution processes of subsurface fatigue spalling and surface fatigue pitting, and can also simulate the propagation of transgranular and intergranular cracks. Compared with the conventional model, as shown in Figure 8 shown, the prediction accuracy of the model of the present invention is relatively high, and it is closer to the test results as shown in Figure 9 shown. In addition, this model calculates the rolling contact fatigue lives of two microscopic distributions, namely Voronoi A and Voronoi B, and has a relatively small error and high accuracy compared with the test results as shown in Figure 10 shown.

[0107] Example 2

[0108] This embodiment provides an electronic device, including a memory and a processor. A computer program is stored on the memory, and when the processor executes the computer program, any step in a method for predicting rolling contact fatigue pitting considering crystal plasticity as described in Example 1 is implemented.

[0109] The hardware of the electronic device in this embodiment further includes a GPU, a display buffer memory, a RAM D / A converter, and a radiator that cooperate with the processor; the GPU is responsible for processing the graphic display of the electronic device, providing image rendering and acceleration functions, and accelerating large-scale data-intensive tasks processed by leveraging its parallel computing advantages.

[0110] Furthermore, the process of the rolling contact fatigue pitting prediction method considering crystal plasticity described in Embodiment 1 can be implemented as a computer software program. For example, this embodiment includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method. In such an embodiment, the computer program can be downloaded and installed from a network, and / or installed from a removable medium. When the computer program is executed by a processor, it executes the above functions defined in the method of this application.

[0111] Embodiment 3

[0112] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any step in the method of implementing a rolling contact fatigue pitting prediction method considering crystal plasticity as described in Embodiment 1.

[0113] The computer-readable medium described in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0114] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Python, C++, and also include conventional procedural programming languages or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0115] The computer-readable storage medium of this embodiment can be accelerated by using hardware such as a GPU, and the parallel computing advantage of the GPU is utilized to accelerate any step in the method for predicting rolling contact fatigue pitting considering crystal plasticity as described in Embodiment 1.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting the protection scope of the present invention. Those skilled in the art can modify or equivalently replace the technical solutions of the present invention according to the idea of the present invention, without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A rolling contact fatigue pitting prediction method considering crystal plasticity, characterized in that: The following steps are involved: Step 1, construct a crystal plasticity constitutive model coupled with damage accumulation theory; Step 2, developing the model described in step 1 into a UMAT subroutine using Fortran and embedding it into ABAQUS; Step 3, establishing a representative volume unit crystal plasticity finite element model; Step 4: Apply periodic boundary conditions and shear loads in the x, y, and z directions, obtain shear stress-strain curves, conduct torsion fatigue tests, obtain fatigue damage parameters of the material, and determine the material constitutive model parameters; Step 5, establishing a crystal plastic finite element model of roller and raceway contact; Step 6, apply boundary conditions in two steps; Step 7: Observe the damage evolution path to the surface and obtain the rolling contact fatigue pitting life.

2. The rolling contact fatigue pitting prediction method considering crystal plasticity according to claim 1, characterized in that: The method for establishing the model in step 1 is: (a) Calculate the velocity gradient tensor L, which is expressed as: in, is the time derivative of the deformation gradient tensor; F -1 is the inverse of the deformation gradient tensor; (b) Decompose the deformation gradient tensor F into two parts, namely the elastic part and the plastic part, and its expression is: F=F e F p , Among them, F e is the elastic deformation gradient tensor; F p is the plastic deformation gradient tensor; (c) The velocity gradient tensor, stretch tensor and lattice rotation tensor are also divided into elastic and plastic parts, and the expression is: D e +Oh e =L e , D p +Oh p =L p , Among them, L e is the elastic velocity gradient tensor, D e is the elastic stretch tensor, Ω e is the elastic lattice rotation tensor, L p is the plastic velocity gradient tensor, D p is the plastic stretch tensor, Ω p is the plastic lattice rotation tensor; (d) Assuming that all plastic deformation comes from dislocation slip, the plastic velocity gradient tensor Lp is calculated by the shear rate of dislocation slip, and the expression is: Among them, α is the slip system, N s is the number of slip systems, is the plastic shear rate of slip system α, s (α) and m (α) are the normal and tangential unit vectors of the slip system α, μ α and ω α They are the symmetric and asymmetric components of the Schmid tensor, respectively; (e) The plastic shear rate model is a nonlinear kinematic hardening model. The damage parameter is embedded in the model to introduce the influence of damage. The expression is: in, is the initial shear rate, is the shear stress (RSS) after damage, χ α is the back stress of the slip system, is the strength of the slip system, D is the damage value, and n is the rate sensitivity coefficient; (f) The strength calculation expression of the slip system is: h αβ (γ)=h(γ)[q+(1-q)δ αβ ], Among them, h αβ is the hardening modulus of the slip system (including self-hardening and latent hardening), β is the slip system, γ is the total shear rate of the total slip system, is the plastic shear rate of the slip system β, q and h0 are material coefficients, h(γ) is the self-hardening modulus, δ αβ is the Kronecker symbol, τ s is the saturated anti-slip resistance stress, τ0 is the initial slip resistance stress; (g) The calculation expression of RSS is: T * =C local (D):E e , Among them, T * is the second Piola-Kirchoff stress, E e is the Green strain tensor, F eT is the transposed matrix of the elastic deformation gradient tensor, C local (D) is the local crystal anisotropic stiffness matrix, expressed as: C local (D)=(1-D)C local 。 3. The rolling contact fatigue pitting prediction method considering crystal plasticity according to claim 2, characterized in that: Based on the damage evolution theory of RSS, the expression is as follows: Among them, τ max is the maximum RSS in a cyclic load, Δγ max is the maximum cumulative plastic shear strain increment of a cyclic load, S and Q are the material coefficients of the fatigue damage evolution model, E is the Young's modulus, N is the number of load cycles, and C 11 and C 12 is the elastic stiffness constant.

4. The rolling contact fatigue pitting prediction method considering crystal plasticity according to claim 2, characterized in that: The material is M50 steel with a cubic structure, and its stiffness matrix expression is:

5. The rolling contact fatigue pitting prediction method considering crystal plasticity according to claim 1, characterized in that: In step 5, a crystal plasticity finite element model of the contact pair containing particles, rollers and raceways is established, a corresponding grain distribution Voronoi model is generated for the local contact area of ​​the contact pair and the original model is replaced, and finally a rolling contact fatigue pitting prediction finite element model considering the microscopic grain distribution is established.

6. The rolling contact fatigue pitting prediction method considering crystal plasticity according to claim 1, characterized in that: In step 6, the boundary conditions are applied in two steps, namely, the first step is to apply particle indentation loading and release, and the second step is to apply cyclic rolling contact load; the material parameters obtained in step 4 are input, and a particle indentation simulation test is carried out to obtain a rolling contact pair model after being pressed by particles, and then a rolling contact fatigue pitting prediction finite element model considering micro grain distribution and indentation is obtained.

7. The rolling contact fatigue pitting prediction method considering crystal plasticity according to claim 1, characterized in that: In step 7, a cyclic rolling contact load is applied, and the damage evolution path to the surface is observed, and the corresponding number of cycles at this time is defined as the rolling contact fatigue pitting life.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a computer processor, any step of the rolling contact fatigue pitting prediction method considering crystal plasticity as described in any one of claims 1 to 7 is implemented.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, any step of the rolling contact fatigue pitting prediction method considering crystal plasticity as described in any one of claims 1 to 7 is implemented.

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