Welding joint high-cycle fatigue failure prediction method and system based on microcrack evolution

Abstract

The invention discloses a welding joint high-cycle fatigue failure prediction method and system based on microcrack evolution, and relates to the field of component high-cycle fatigue damage, and the method comprises the steps: building a finite element model of a to-be-detected welding joint, and determining an initial fatigue damage value of the finite element model; a cyclic load is applied to the finite element model, for any cycle, the applied stress amplitude of the finite element model in the cycle and the number of cycles of the load are determined, and a fatigue damage variable of the finite element model in the cycle is determined by adopting a high-cycle fatigue damage model; determining a fatigue damage predicted value of the finite element model in the cycle based on the fatigue damage variable of the finite element model in the cycle and the fatigue damage predicted value of the finite element model in the previous cycle; and according to the fatigue damage prediction value of the finite element model in the cycle, whether the welding joint has fatigue failure in the cycle is determined, and whether the welding joint has fatigue failure or not is accurately and conveniently predicted.

Description

TECHNICAL FIELD

[0001] The application relates to the field of component high-cycle fatigue damage, in particular to a high-cycle fatigue failure prediction method and system for welded joints based on micro-crack evolution. BACKGROUND

[0002] Welded structures bear a large number of different forms of cyclic loads in actual service environments, thereby causing performance degradation, i.e., fatigue. Data show that fatigue failure is one of the main failure modes of titanium alloy structural components. In the context of growing demand in modern society, it is required that service components have a longer safe life. High-cycle fatigue performance of welded structures is an important part of fatigue research.

[0003] To prevent structural failure and ensure normal and smooth operation of structural components, high-cycle fatigue life assessment of structural components is an important research direction of fatigue performance prediction. After decades of development, a large number of prediction methods and models have been proposed. Fatigue damage model based on damage mechanics research is one of the important research methods. The research approach can be divided into two categories: macroscopic constitutive theory and microscopic constitutive theory based on physical mechanism. At present, if the microscopic model is directly calculated, the calculation is complex and time-consuming, and the engineering universality is low. The macroscopic phenomenological model focuses on the macroscopic results of damage and cannot reflect the physical mechanism behind the damage. Therefore, it is necessary to explore a macro-micro parallel development high-cycle fatigue damage model suitable for welded joints.

[0004] The micro-crack initiation and propagation stage accounts for a large part of the total life of high-cycle fatigue. With the increase of fatigue load cycles, fatigue short cracks propagate and converge, and when the fatigue damage accumulates to a certain extent, the short cracks diffuse into long cracks and quickly fracture. Therefore, the critical condition of the transition of short cracks to long cracks is a key problem for fatigue failure prediction of short crack damage. SUMMARY

[0005] The purpose of the application is to provide a high-cycle fatigue failure prediction method and system for welded joints based on micro-crack evolution. The high-cycle fatigue damage model can more accurately and conveniently predict whether fatigue failure occurs in the welded joint, and determine the fatigue life of the welded joint when fatigue failure occurs in the welded joint, thereby providing a better theoretical basis for the development of subsequent materials and life assessment. At the same time, the high-cycle fatigue damage model can improve the calculation efficiency of edge computing devices and servers.

[0006] To achieve the above purpose, the application provides the following solutions. In a first aspect, the application provides a high-cycle fatigue failure prediction method for welded joints based on micro-crack evolution, comprising: establishing a finite element model of the welding joint to be tested, and determining an initial fatigue damage value of the finite element model; applying cyclic loading to the finite element model, and determining, for each cycle, an applied stress amplitude and a number of cycles of loading of the finite element model at the cycle; based on the applied stress amplitude and the number of cycles of loading of the finite element model at the cycle, determining a fatigue damage variable of the finite element model at the cycle using a high-cycle fatigue damage model; the high-cycle fatigue damage model is used to determine the fatigue damage variable based on a micro-crack evolution process according to a micro-crack nucleation rate factor parameter, a micro-crack propagation rate factor parameter, and the number of cycles of loading; the micro-crack nucleation rate factor parameter and the micro-crack propagation rate factor parameter are determined by the applied stress amplitude; based on the fatigue damage variable of the finite element model at the cycle and a fatigue damage prediction value of the finite element model at a previous cycle, determining a fatigue damage prediction value of the finite element model at the cycle; the fatigue damage prediction value of the finite element model at the previous cycle is determined based on the initial fatigue damage value; determining whether the welding joint fails in fatigue at the cycle according to the fatigue damage prediction value of the finite element model at the cycle.

[0007] In a second aspect, the present application provides a high-cycle fatigue failure prediction system for a welding joint based on micro-crack evolution, comprising: a finite element model establishing module, configured to establish a finite element model of the welding joint to be tested, and determine an initial fatigue damage value of the finite element model; a stress amplitude and cycle number determining module, configured to apply cyclic loading to the finite element model, and determine, for each cycle, an applied stress amplitude and a number of cycles of loading of the finite element model at the cycle; a fatigue damage variable determining module, configured to determine a fatigue damage variable of the finite element model at the cycle using a high-cycle fatigue damage model based on the applied stress amplitude and the number of cycles of loading of the finite element model at the cycle; the high-cycle fatigue damage model is used to determine the fatigue damage variable based on a micro-crack evolution process according to a micro-crack nucleation rate factor parameter, a micro-crack propagation rate factor parameter, and the number of cycles of loading; the micro-crack nucleation rate factor parameter and the micro-crack propagation rate factor parameter are determined by the applied stress amplitude; a fatigue damage prediction value determining module, configured to determine a fatigue damage prediction value of the finite element model at the cycle based on the fatigue damage variable of the finite element model at the cycle and a fatigue damage prediction value of the finite element model at a previous cycle; the fatigue damage prediction value of the finite element model at the previous cycle is determined based on the initial fatigue damage value; a fatigue failure determining module, configured to determine whether the welding joint fails in fatigue at the cycle according to the fatigue damage prediction value of the finite element model at the cycle.

[0008] According to the specific embodiments provided in the application, the application has the following technical effects: The application provides a micro-crack evolution-based high-cycle fatigue failure prediction method and system for a welded joint. A high-cycle fatigue damage model is established based on a micro-crack evolution process. The high-cycle fatigue damage model is used to more accurately and conveniently predict whether the welded joint has fatigue failure. When the welded joint has fatigue failure, the fatigue life of the welded joint is determined, thereby providing a more optimal theoretical basis for subsequent material development and life assessment. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative labor.

[0010] Figure 1 A flowchart of a micro-crack evolution-based high-cycle fatigue failure prediction method for a welded joint according to an embodiment of the application is provided. Figure 2 A functional module diagram of a micro-crack evolution-based high-cycle fatigue failure prediction system for a welded joint according to an embodiment of the application is provided. Figure 3 A comparison diagram of a fatigue damage evolution curve and a high-cycle fatigue damage model fitting curve obtained according to experimental data according to an embodiment of the application is provided. Fig. 4(a) is a fitting result diagram obtained by fitting the values of Fig. 4(a) using a high-cycle fatigue damage model according to an embodiment of the application. Fig. 4(b) is a fitting result diagram obtained by fitting the values of Fig. 4(b) using a high-cycle fatigue damage model. Fig. 4(c) is a fitting result diagram obtained by fitting the values of Fig. 4(c) using a high-cycle fatigue damage model. Fig. 4(c) is a fitting result diagram obtained by fitting the values of Fig. 4(c) using a high-cycle fatigue damage model. Figure 5 A comparison diagram of a predicted life and an experimental life according to an embodiment of the application is provided. Figure 6 A structural diagram of a computer device according to an embodiment of the application is provided. DETAILED DESCRIPTION

[0011] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of the present application.

[0012] The purposes, characteristics and advantages of the present application will be more obvious and easy to understand. The present application will be further described in detail below with reference to the drawings and specific embodiments.

[0013] In one exemplary embodiment, as shown in Figure 1 A micro-crack evolution-based welded joint high-cycle fatigue failure prediction method is provided, including the following steps 101 to 105. Wherein: Step 101, a finite element model of a welded joint to be tested is established, and an initial fatigue damage value of the finite element model is determined.

[0014] Step 102, a cyclic load is applied to the finite element model, and for each cycle, the applied stress amplitude and the number of cycles of the load of the finite element model at the cycle are determined. The number of cycles of the load is the cumulative number of cycles of the load.

[0015] Step 103, based on the applied stress amplitude and the number of cycles of the load of the finite element model at the cycle, a high-cycle fatigue damage model is used to determine the fatigue damage variable of the finite element model at the cycle; the high-cycle fatigue damage model is used to determine the fatigue damage variable based on the micro-crack nucleation rate factor parameter, the micro-crack propagation rate factor parameter and the number of cycles of the load according to the micro-crack evolution process; the micro-crack nucleation rate factor parameter and the micro-crack propagation rate factor parameter are determined by the applied stress amplitude.

[0016] Step 104, based on the fatigue damage variable of the finite element model at the cycle and the fatigue damage prediction value of the finite element model at the previous cycle, the fatigue damage prediction value of the finite element model at the cycle is determined; the fatigue damage prediction value of the finite element model at the previous cycle is determined based on the initial fatigue damage value.

[0017] Step 105, according to the fatigue damage prediction value of the finite element model at the cycle, it is determined whether the fatigue failure of the welded joint occurs at the cycle.

[0018] In another exemplary embodiment of the present application, after step 101, it further includes: Step 201: Establish a calculation formula for fatigue damage variables based on the ratio of the cross-sectional area of ​​each microcrack to the cross-sectional area of ​​the REV cross section, where REV stands for Representative Volume Element (REV).

[0019] In this application, the scale of a single microcrack is set to characterization, Represents the cross-sectional area of ​​the microcrack. For the convenience of calculation, microcracks of different geometric shapes are simplified to circles, and the fatigue damage variable of the i-th microcrack is It can be expressed as a radius of The ratio of the cross-sectional area of ​​the microcracks to the cross-sectional area of ​​the REV cross section S. Therefore, the fatigue damage variable D can be defined as the sum of the ratios of the cross-sectional areas of all microcracks to the cross-sectional area of ​​the REV cross section S. The calculation formula of the fatigue damage variable is: .

[0020] in, is the fatigue damage variable, is the fatigue damage variable of the i-th microcrack, is the radius of the i-th microcrack, is the cross-sectional area of ​​REV, is the number of load cycles, is the density distribution function of the i-th microcrack at the N-th cycle, .

[0021] Step 202 : determining a microcrack number density distribution function evolution equation, and solving the microcrack number density distribution function in the microcrack number density distribution function evolution equation based on a microcrack nucleation rate model and a microcrack extension model.

[0022] The process of determining the evolution equation of the microcrack number density distribution function is as follows: Assume that the load cycle is loaded from N to , the microcrack radius expands from r to , then the microcrack number density increment is: .in, For the The number density distribution function of the i-th microcrack at the cycle, For the The number density distribution function of the i-th microcrack at the cycle.

[0023] Because the evolution of microcracks is mainly determined by the nucleation and propagation processes, the first part of the increment comes from The number of newly nucleated microcracks is affected by the microcrack nucleation rate. First, define is the microcrack nucleation rate density distribution function at the Nth cycle. The rest of the increment is contributed by the microcrack propagation, which is affected by the microcrack propagation rate, defined as is the ith microcrack propagation rate function, then the following equation holds: .

[0024] where is the integral variable, is the ith microcrack nucleation rate density distribution function at the Nth cycle, is the microcrack number density distribution function at the Nth cycle with radius r, is the ith microcrack propagation rate function at the Nth cycle with radius r, is the microcrack number density distribution function at the Nth cycle with radius r, is the ith microcrack propagation rate function at the Nth cycle with radius r, is the microcrack number density distribution function at the Nth cycle with radius r, is the ith microcrack propagation rate function at the Nth cycle with radius r. is the microcrack number density distribution function at the Nth cycle with radius r, is the ith microcrack propagation rate function at the Nth cycle with radius r.

[0025] Dividing both sides of the above equation by and , and letting , , the microcrack number density distribution function evolution equation is given by .

[0026] where is the microcrack number density distribution function, is the microcrack propagation rate function, is the radius of the microcrack, is the number of load cycles, is the microcrack nucleation rate density distribution function at the Nth cycle.

[0027] The microcrack nucleation rate model is given by .

[0028] The propagation model is given by .

[0029] .

[0030] .

[0031] .

[0032] The initial condition is assumed to be ​​, the microcrack number density distribution function is: .

[0033] in, is the number of microcracks at the Nth cycle, is the microcrack nucleation rate factor, is the second parameter of the microcrack growth rate factor, is the microcrack growth rate factor, is the fluctuation parameter, is the extension radius of the microcrack, It is the third parameter of the microcrack nucleation rate factor.

[0034] Step 203: Based on the microcrack number density distribution function and the calculation formula of the fatigue damage variable, a high cycle fatigue damage model is obtained. The high cycle fatigue damage model can be expressed as: .

[0035] in, is the fatigue damage variable, is the number of load cycles, is the applied stress amplitude, is the first parameter of the microcrack nucleation rate factor, is the second parameter of the microcrack nucleation rate factor, It is the first parameter of microcrack growth rate factor. 、 and All need to be fitted with different stress amplitudes ( ) under the fatigue damage evolution curve ( ) is obtained, and then the fitting data is established and relationship curve.

[0036] In another exemplary embodiment of the present application, step 104 specifically includes the following steps 301 to 303. Among them: Step 301 : determining a damage rate of the finite element model in the cycle based on a fatigue damage variable of the finite element model in the cycle and the number of cycles of the load.

[0037] Step 302 : determining a jump value of the finite element model in the cycle based on a damage rate of the finite element model in the cycle.

[0038] Step 303 : determining a fatigue damage prediction value of the finite element model in the cycle based on the jump value of the finite element model in the cycle, the damage rate of the finite element model in the cycle, and the fatigue damage prediction value of the finite element model in the previous cycle.

[0039] In another exemplary embodiment of the present application, the damage rate of the finite element model at the Nth cycle is determined using the following formula: .

[0040] wherein, is the damage rate of the finite element model at the Nth cycle, is the fatigue damage variable, is the number of cycles of the load, is the applied stress amplitude, is the first parameter of the micro-crack nucleation rate factor, is the second parameter of the micro-crack nucleation rate factor, is the first parameter of the micro-crack propagation rate factor.

[0041] In another exemplary embodiment of the present application, the micro-cracks are multiple; the jump value of the finite element model at the Nth cycle is determined using the following formula: .

[0042] wherein, is the jump value of the finite element model at the Nth cycle, A is the jump value parameter, which can be adjusted according to the calculation accuracy, is the serial number of the micro-crack, is the maximum value of the damage rate of all the micro-cracks of the finite element model at the Nth cycle.

[0043] In another exemplary embodiment of the present application, the fatigue damage prediction value of the finite element model at the Nth cycle is determined using the following formula: .

[0044] wherein, is the fatigue damage prediction value of the finite element model at the Nth cycle, is the fatigue damage prediction value of the finite element model at the previous cycle, is the jump value of the finite element model at the Nth cycle, is the damage rate of the i-th micro-crack of the finite element model at the Nth cycle.

[0045] In another exemplary embodiment of the present application, the UMAT subroutine in ABAQUS is used to realize the simulation of the fatigue damage evolution process of the welded joint. The evolution process of the fatigue damage variable D is characterized by the decrease of the unit stiffness, and the elastic modulus of the unit is updated after each cycle of loading ( ), is the initial elastic modulus, and when the elastic modulus decreases to 0, it indicates that fatigue failure occurs. The specific program flow is as follows: 1. Finite element model is established according to the size of the welded joint, and the load mode and boundary conditions are the same as the actual high-cycle fatigue test in order to reflect the actual fatigue loading condition.

[0046] 2. After the mesh independence test, the maximum and minimum stresses at the weld in the Nth cycle are extracted , and the stress amplitude at the weld under cyclic loading is calculated according to . .

[0047] 3. The damage rate in the Nth cycle is calculated according to .

[0048] 4. For cyclic loading in fatigue, the step-by-step calculation in time becomes daunting when the cycle number increases. Therefore, the cycle skipping technique is adopted to shorten the calculation time. The larger the skip value , the smaller the required calculation time, but the larger the fatigue damage calculation error. Therefore, a reasonable calculation value can effectively improve the calculation efficiency and ensure the result accuracy. The skip value in each cycle is represented by the actual loading cycle number, and is used to calculate .

[0049] 5. After each skip, the fatigue damage prediction value is calculated using .

[0050] 6. The elastic modulus in the Nth cycle is calculated, the predicted value of the fatigue life of the finite element model is recorded, and it is determined whether fatigue failure occurs (D≥1). If D<1, the calculation result of is returned and updated, and the above process is repeated. If D≥1, the calculation is stopped, and the predicted value of the fatigue life of the finite element model is output.

[0051] Finally, based on the fatigue test data (the experimental data of the fatigue process is obtained from the room temperature high-cycle fatigue test), the fatigue life values of the finite element model corresponding to different maximum cyclic stresses are obtained, and then compared with the predicted values of the fatigue life simulated by the high-cycle fatigue damage model.

[0052] ​The application determines whether fatigue failure occurs through the fatigue damage threshold (D=1), and determines the fatigue life of the welded joint when fatigue failure occurs. The verification results show that the predicted value of the fatigue life obtained by the application is close to the fatigue life value obtained by the experiment, which indicates that the high-cycle fatigue damage model designed by the application is effective, and compared with the traditional high-cycle fatigue damage model, the micro-crack evolution framework is proved to be effective in characterizing the micro-scale fatigue damage progress, which is basically impossible to achieve by the traditional modeling method. And the design idea of the application is clear and logical, and each link is connected and complementary, which provides a better theoretical basis for the development and life evaluation of subsequent materials, and has high practical value.

[0053] Based on the same inventive concept, the application also provides a micro-crack evolution-based high-cycle fatigue failure prediction system for a welded joint. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more micro-crack evolution-based high-cycle fatigue failure prediction system embodiments provided below can be referred to the limitations of the micro-crack evolution-based high-cycle fatigue failure prediction method in the above text, which will not be repeated here. As shown in the following Figure 2 The system comprises: A finite element model establishing module 201 is configured to establish a finite element model of a welded joint to be tested, and determine an initial fatigue damage value of the finite element model.

[0054] A stress amplitude and cycle number determining module 202 is configured to apply a cyclic load to the finite element model, and determine, for each cycle, an applied stress amplitude of the finite element model and a cycle number of the load.

[0055] A fatigue damage variable determining module 203 is configured to determine, based on the applied stress amplitude of the finite element model and the cycle number of the load, a fatigue damage variable of the finite element model in the cycle by using a high-cycle fatigue damage model; the high-cycle fatigue damage model is configured to determine the fatigue damage variable based on a micro-crack nucleation rate factor parameter, a micro-crack propagation rate factor parameter, and the cycle number of the load according to a micro-crack evolution process; and the micro-crack nucleation rate factor parameter and the micro-crack propagation rate factor parameter are determined by the applied stress amplitude.

[0056] A fatigue damage prediction value determining module 204 is configured to determine, based on the fatigue damage variable of the finite element model in the cycle and a fatigue damage prediction value of the finite element model in a previous cycle, a fatigue damage prediction value of the finite element model in the cycle; and the fatigue damage prediction value of the finite element model in the previous cycle is determined based on the initial fatigue damage value.

[0057] The fatigue failure determination module 205 is configured to determine whether the welded joint has fatigue failure under the cycle according to the fatigue damage prediction value of the finite element model under the cycle.

[0058] In another exemplary embodiment of the present application, first, TC4 titanium alloy plate in rolling state is selected as the experimental material for welding, and laser-MIG hybrid welding is used for butt welding. High cycle fatigue tests are performed on the welded joint at room temperature under different maximum initial cyclic stresses (450 MPa, 445 MPa, 440 MPa and 435 MPa). The specific experimental steps are as follows: a QBG-100 type high cycle fatigue testing machine is used to perform axial loading tensile-tensile high cycle fatigue test at room temperature. The clamping mode of the fatigue sample is fixed at the lower end and the cyclic load is applied at the upper end. The load loading mode is stress ratio , the average frequency is 80 Hz, and the remaining experimental parameters are shown in Table 1. Then, according to the obtained experimental data (Table 2), the values of the different maximum initial cyclic stresses and the fatigue life are obtained, and then the fatigue damage evolution curve of Figure 3 is fitted by using the high cycle fatigue damage model to obtain the values of , and . The fitting results and curves are shown in Figures 4(a), 4(b) and 4(c).

[0059] Table 1 TC4 titanium alloy pulse laser-MIG hybrid welding process parameters

[0060] Table 2 High cycle fatigue test results

[0061] The high cycle fatigue damage model of the present embodiment is verified by using the above experimental data. First, in the above fatigue test results, the average value (average fatigue life) of the four test results under different maximum cyclic load stresses is regarded as the final fatigue life under the stress level. Figure 5 The purple triangular points in represent the average fatigue life of the experimental data points under different maximum cyclic load stresses, and the red circular points represent the correspondence between the experimental value and the predicted value under each stress level.

[0062] The fatigue life value (predicted life) predicted by the high cycle fatigue damage model is compared with the fatigue life (experimental life) finally obtained by the fatigue experiment, as shown in Figure 5The results show that the fatigue life prediction results under four stress levels are very consistent with the experimental results, and the high-cycle fatigue damage model prediction results of the fatigue life fall within the two times error band, which shows that the proposed high-cycle fatigue damage model has high precision, and verifies the reliability of the high-cycle fatigue damage model for evaluating the fatigue life of the TC4 titanium alloy laser-arc hybrid welded joint.

[0063] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 6 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store a finite element model. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a high-cycle fatigue failure prediction method for welded joints based on micro-crack evolution.

[0064] Those skilled in the art can understand that Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0065] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0066] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for predicting high cycle fatigue failure of welded joints based on microcrack evolution, characterized in that: The method comprises: Establish a finite element model of the welded joint to be tested and determine the initial fatigue damage value of the finite element model; Applying a cyclic load to the finite element model and determining, for any cycle, the applied stress amplitude and the number of load cycles of the finite element model in the cycle; Based on the applied stress amplitude of the finite element model in the cycle and the number of load cycles, a high cycle fatigue damage model is used to determine the fatigue damage variable of the finite element model in the cycle; the high cycle fatigue damage model is used to determine the fatigue damage variable based on the microcrack evolution process according to the microcrack nucleation rate factor parameter, the microcrack growth rate factor parameter and the number of load cycles; the microcrack nucleation rate factor parameter and the microcrack growth rate factor parameter are determined by the applied stress amplitude; determining a fatigue damage prediction value of the finite element model in the cycle based on a fatigue damage variable of the finite element model in the cycle and a fatigue damage prediction value of the finite element model in the previous cycle; the fatigue damage prediction value of the finite element model in the previous cycle is determined based on the initial fatigue damage value; According to the fatigue damage prediction value of the finite element model in the cycle, it is determined whether fatigue failure occurs in the weld joint in the cycle.

2. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 1, characterized in that: The high cycle fatigue damage model is: ； in, is the fatigue damage variable, is the number of load cycles, is the applied stress amplitude, is the first parameter of the microcrack nucleation rate factor, is the second parameter of the microcrack nucleation rate factor, It is the first parameter of microcrack growth rate factor.

3. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 1, characterized in that: Determining the fatigue damage prediction value of the finite element model in the cycle based on the fatigue damage variable of the finite element model in the cycle and the fatigue damage prediction value of the finite element model in the previous cycle, specifically including: determining a damage rate of the finite element model in the cycle based on a fatigue damage variable of the finite element model in the cycle and the number of cycles of the load; determining a jump value of the finite element model at the cycle based on a damage rate of the finite element model at the cycle; The fatigue damage prediction value of the finite element model in the cycle is determined based on the jump value of the finite element model in the cycle, the damage rate of the finite element model in the cycle and the fatigue damage prediction value of the finite element model in the previous cycle.

4. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 3, characterized in that: The damage rate of the finite element model at the Nth cycle is determined using the following formula: ； in, is the damage rate of the finite element model in the Nth cycle, is the fatigue damage variable, is the number of load cycles, is the applied stress amplitude, is the first parameter of the microcrack nucleation rate factor, is the second parameter of the microcrack nucleation rate factor, It is the first parameter of microcrack growth rate factor.

5. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 3, characterized in that: There are multiple microcracks; the following formula is used to determine the jump value of the finite element model in the Nth cycle: ； in, is the jump value of the finite element model in the Nth cycle, A is the jump value parameter, is the serial number of the microcrack, is the maximum value of the damage rate of all microcracks in the finite element model at the Nth cycle.

6. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 5, characterized in that: The following formula is used to determine the fatigue damage prediction value of the finite element model at the Nth cycle: ； in, is the fatigue damage prediction value of the finite element model in the Nth cycle, is the fatigue damage prediction value of the finite element model in the previous cycle, is the jump value of the finite element model in the Nth cycle, is the damage rate of the i-th microcrack in the finite element model at the N-th cycle.

7. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 5, characterized in that: After establishing the finite element model of the weld joint to be tested and determining the initial fatigue damage value of the finite element model, the following steps are also included: Based on the ratio of the cross-sectional area of ​​each microcrack to the cross-sectional area of ​​the REV, a calculation formula for the fatigue damage variable is established; Determine the microcrack number density distribution function evolution equation, and solve the microcrack number density distribution function in the microcrack number density distribution function evolution equation based on the microcrack nucleation rate model and the microcrack propagation model; Based on the microcrack number density distribution function and the calculation formula of the fatigue damage variable, a high cycle fatigue damage model is obtained.

8. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 7, characterized in that: The calculation formula of the fatigue damage variable is: ； in, is the fatigue damage variable, is the fatigue damage variable of the i-th microcrack, is the radius of the i-th microcrack, is the cross-sectional area of ​​REV, is the number of load cycles, is the density distribution function of the i-th microcrack number at the N-th cycle.

9. The method for predicting high cycle fatigue failure of welded joints based on microcrack evolution according to claim 7, characterized in that: The microcrack number density distribution function evolution equation is: ； The microcrack nucleation rate model is: ； The extended model is: ； ； ； The microcrack number density distribution function is: ； in, is the microcrack number density distribution function, is the microcrack growth rate function, is the radius of the microcrack, is the number of load cycles, is the microcrack nucleation rate density distribution function at the Nth cycle, is the number of microcracks at the Nth cycle, is the microcrack nucleation rate factor, is the second parameter of the microcrack growth rate factor, is the microcrack growth rate factor, is the fluctuation parameter, is the extension radius of the microcrack.

10. A weld joint high cycle fatigue failure prediction system based on microcrack evolution, applying the weld joint high cycle fatigue failure prediction method based on microcrack evolution according to any one of claims 1 to 9, characterized in that: The system comprises: Finite element model building module, used to build the finite element model of the weld joint to be tested and determine the initial fatigue damage value of the finite element model; A stress amplitude and cycle number determination module is used to apply a cyclic load to the finite element model and, for any cycle, determine the applied stress amplitude and the number of cycles of the load of the finite element model in the cycle; a fatigue damage variable determination module, configured to determine the fatigue damage variable of the finite element model in the cycle based on the applied stress amplitude of the finite element model in the cycle and the number of load cycles, using a high-cycle fatigue damage model; the high-cycle fatigue damage model is configured to determine the fatigue damage variable based on the microcrack evolution process, according to a microcrack nucleation rate factor parameter, a microcrack growth rate factor parameter, and the number of load cycles; the microcrack nucleation rate factor parameter and the microcrack growth rate factor parameter are determined by the applied stress amplitude; a fatigue damage prediction value determination module, configured to determine a fatigue damage prediction value of the finite element model in the cycle based on a fatigue damage variable of the finite element model in the cycle and a fatigue damage prediction value of the finite element model in the previous cycle; the fatigue damage prediction value of the finite element model in the previous cycle is determined based on the initial fatigue damage value; The fatigue failure determination module is used to determine whether fatigue failure occurs in the weld joint in the cycle according to the fatigue damage prediction value of the finite element model in the cycle.

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