Fatigue life prediction method of alloy materials based on crack growth rate

Abstract

The present invention relates to the technical field of alloy material life prediction, and in particular to a method for predicting the fatigue life of an alloy material based on crack growth rate, comprising: S1, preparing an alloy material test block and obtaining fatigue crack growth experimental data of the alloy material; S2, establishing an alloy fatigue crack growth rate model and obtaining a functional relationship between a stress intensity factor and a crack growth rate; S3, determining the crack growth rate of the alloy material according to the alloy fatigue crack growth rate model, and completing fatigue life prediction. The present invention uses a continuous piecewise logarithmic linear crack growth rate model to describe the crack growth rate data of the alloy; establishes a continuous piecewise model by introducing the Heaviside step function and the transition stress intensity factor amplitude into the classic Paris model, thereby improving the accuracy of the model; and performs crack growth experiments on titanium alloy material specimens, indicating that the crack growth rate of the material is uniform and isotropic.

Description

Technical Field

[0001] The present invention belongs to the technical field of metal material life prediction, and in particular relates to a method for predicting the fatigue life of alloy materials based on crack growth rate. Background Art

[0002] As industrial equipment develops towards large-scale, multifunctional and high-reliability, additive manufacturing technology, as a key basic technology supporting the innovative development of the manufacturing industry, has received extensive attention and research in the industrial field.

[0003] For key components subjected to alternating cyclic loads, fatigue failure is the most important failure mode that causes the greatest economic losses. Given the characteristics of additive manufacturing technology itself, during the layer-by-layer stacking of raw materials, non-uniform microstructures and tiny defects are inevitably generated in the additive material. Therefore, when conducting fatigue design, it is necessary to consider the impact of microstructure and initial defects on fatigue life. The most appropriate analysis method is to use the damage tolerance design method to analyze the service life of key components of additive material structures and determine their safe service life. Therefore, when evaluating the structural integrity of additive parts, it is necessary to establish an accurate fatigue crack growth rate model based on the damage tolerance design method and a full understanding of the material fatigue crack growth mechanism to achieve a reliable prediction of the service life. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a fatigue life prediction method for alloy materials based on crack growth rate. The crack growth rate data of the alloy are described using a continuous piecewise log-linear crack growth rate model. A continuous piecewise model is established by introducing the Heaviside step function and the transition stress intensity factor amplitude into the classic Paris model, thereby improving the model accuracy. Crack growth experiments are conducted on titanium alloy specimens, indicating that the crack growth rate of the material is uniform and isotropic.

[0005] To achieve the above objectives, the present invention discloses the following technical solutions:

[0006] A method for predicting fatigue life of alloy materials based on crack growth rate, comprising:

[0007] S1: Prepare alloy material test blocks and obtain fatigue crack growth experimental data of the alloy materials;

[0008] A compact tensile alloy specimen with a target crack propagation direction was machined at a set position on the alloy specimen. A fatigue crack propagation experiment was conducted to obtain the fatigue crack length a and the corresponding cyclic stress loading period N, and the crack propagation rate da / dN. The stress intensity factor amplitude ΔK was calculated based on the fatigue crack length a and the applied stress amplitude Δσ as follows:

[0009]

[0010] Where ΔK is the stress intensity factor amplitude; Δσ is the applied stress amplitude; a is the fatigue crack length; β is the geometric correction factor;

[0011] S2: Establish an alloy fatigue crack growth rate model and obtain the functional relationship between stress intensity factor and crack growth rate;

[0012] S21: Plot the crack growth rate da / dN and the stress intensity factor amplitude ΔK in step S1 on a double logarithmic coordinate system to determine the presence of a transition stress intensity factor amplitude ΔK n , the fatigue crack growth data of the alloy material is divided into the first crack growth stage and the second crack growth stage, and a continuity equation is used to establish the alloy fatigue crack growth rate model;

[0013] S22: Transform the stress intensity factor amplitude ΔK n As the unknown parameters, the fatigue crack growth rate model of the alloy is established using the Heaviside step function and the classic Paris model:

[0014]

[0015] in, is the crack growth rate data; N is the cyclic stress loading cycle; C is the first material parameter of the alloy fatigue crack growth rate model; m1 is the second material parameter of the alloy fatigue crack growth rate model; m2 is the third material parameter of the alloy fatigue crack growth rate model; ΔK n is the amplitude of the transition stress intensity factor; ΔK is the amplitude data of the stress intensity factor; H[ln(ΔK n )-ln(ΔK)] is the Heaviside step function, and [ln(ΔK n )-ln(ΔK)] is the independent variable of the Heaviside step function;

[0016] S23: Determine the functional relationship between the stress intensity factor amplitude ΔK and the crack growth rate da / dN; in the first crack growth stage, ΔK ≤ ΔK n , the first slope is (m1+m2), and the first intercept is [lnC-m2·ln(ΔK n )]; in the second crack extension stage, ΔK>ΔK n , the second slope is m1, and the second intercept is lnc; the Heaviside step function can ensure that the alloy fatigue crack growth rate model has overall continuity;

[0017] S3: Determine the crack growth rate of the alloy material according to the alloy fatigue crack growth rate model and complete the fatigue life prediction;

[0018] Analyze the actual stress state of the alloy material and obtain the initial crack size a0 and critical crack size a c Based on the alloy fatigue crack growth rate model determined in step S2, numerical integration is performed to obtain the fatigue remaining service life N of the alloy material. c for:

[0019]

[0020] Among them, N c is the fatigue residual service life of the alloy material; a0 is the initial crack size; a c is the critical crack size.

[0021] Preferably, in step S1, the alloy material test block is prepared using alloy wire based on arc additive manufacturing technology, specifically: a cold metal transfer arc welding mode is adopted, a six-axis robotic arm is used to drive the welding gun, and the alloy wire is stacked layer by layer on the forged alloy plate to prepare the alloy material test block, and each layer adopts an oscillation stacking strategy to construct an alloy material test block with a target thickness. The processing parameters that need to be set include: arc current, welding gun travel speed and shielding gas flow rate.

[0022] Preferably, the fatigue crack growth experiment in step S1 is specifically as follows: performing a fatigue crack growth experiment on a compact tensile alloy material specimen, obtaining the fatigue crack length a and the corresponding cyclic stress loading period N, and calculating the crack growth rate da / dN using a seven-point increasing polynomial method.

[0023] Preferably, in step S21, there is a transition stress intensity factor amplitude ΔK n , the fatigue crack growth data of alloy materials are divided into two sections, specifically: for alloy materials, the crack growth rate da / dN and stress intensity factor amplitude ΔK data of all alloy material specimens are plotted in a double logarithmic coordinate system, which can determine the presence of transition stress intensity factor amplitude ΔK n , the crack growth rate data ln(da / dN) and the stress intensity factor amplitude data ln(ΔK) are divided into two segments.

[0024] Preferably, the Heaviside step function H in step S22 is specifically:

[0025] In the Heaviside step function, if the independent variable [ln(ΔK n )-ln(ΔK)] is greater than or equal to zero, the function takes the value of 1, otherwise it takes the value of 0, specifically:

[0026]

[0027] Among them, H[ln(ΔK n)-ln(ΔK)] is the Heaviside step function.

[0028] Preferably, the alloy fatigue crack growth rate model in step S22 needs to determine the transition stress intensity factor amplitude ΔK using the least squares method based on the fatigue crack growth experimental data of the alloy material. n .

[0029] Preferably, in step S22, the first material parameter, the second material parameter, and the third material parameter of the alloy fatigue crack growth rate model can be determined by fitting the crack growth rate experimental data by the least squares method; and the material parameters (C, m1, m2) of the fatigue crack growth rate model of each alloy material specimen are obtained based on the crack growth rate data of each specimen taken from the set position and the target crack growth direction of the alloy material specimen.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention uses a continuous piecewise logarithmic linear crack growth rate model to describe the crack growth rate data of the alloy. By introducing the Heaviside step function and the transition stress intensity factor amplitude into the classic Paris model, a continuous piecewise model is established. Through the study of the piecewise logarithmic linear crack growth rate, the uncertainty in selecting the transition stress intensity factor amplitude when using two discrete equations for fatigue crack growth rate modeling is effectively eliminated, thereby improving the accuracy of the model.

[0032] (2) The present invention conducts crack propagation experiments on arc additively manufactured titanium alloy material specimens, obtains crack propagation rate data of compact tensile alloy material specimens with target crack propagation directions sampled from set positions of test blocks prepared by an oscillating stacking strategy, and models the crack propagation experimental data.

[0033] (3) There is no statistical difference in the material parameters of the crack growth rate model set among the specimens in the present invention, indicating that the crack growth rate of the arc additively manufactured alloy material or metal material is uniform and isotropic, and can be promoted and applied in practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the method for predicting fatigue life of alloy materials based on crack growth rate of the present invention;

[0035] Figure 2 Schematic diagram of preparing a test block for the cold metal transfer arc welding mode of the present invention;

[0036] Figure 3 This is a schematic diagram of a test block obtained by adopting the oscillation stacking strategy of the present invention;

[0037] Figure 4Schematic diagram of the sampling scheme for 9 specimens of the present invention;

[0038] Figure 5 This is a structural diagram of a compact tensile alloy material specimen of the present invention;

[0039] Figure 6 Schematic diagram of da / dN and ΔK data of the test piece set in the double logarithmic coordinate system of the present invention;

[0040] Figure 7 The first stage model residual histogram and normal distribution fitting diagram of the present invention;

[0041] Figure 8 The residual histogram and normal distribution fitting diagram of the second stage model of the present invention are shown;

[0042] Figure 9 This is a linear fitting result diagram of the fatigue crack growth rate model parameters in the first stage of the present invention;

[0043] Figure 10 This is a linear fitting result diagram of the fatigue crack growth rate model parameters in the second stage of the present invention;

[0044] Figure 11 This is the growth life distribution diagram of the embedded elliptical crack considering the uncertainty of material parameters in the present invention. DETAILED DESCRIPTION

[0045] The exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0046] The embodiment of the present invention provides a method for predicting the fatigue life of alloy materials based on crack growth rate, which uses a continuous piecewise logarithmic linear crack growth rate model to realize the fatigue life prediction of alloy or metal materials; Figure 1 As shown, alloy material test blocks are prepared, fatigue crack growth experimental data of the alloy material is obtained, a fatigue crack growth rate model of the alloy is established, the functional relationship between stress intensity factor and crack growth rate is obtained, and fatigue life prediction is completed; the specific steps include:

[0047] Step S1: preparing a test block of an alloy material and obtaining fatigue crack growth experimental data of the alloy material.

[0048] In a preferred embodiment of the present invention, a titanium wire with a diameter of 1.2 mm was used to manufacture an arc additive test block. The chemical composition of the wire is shown in Table 1. A forged titanium alloy plate substrate 10 with geometric dimensions of 280 mm × 200 mm × 15 mm was prepared for subsequent wire stacking. Using the cold metal transfer arc welding mode, a six-axis robot arm drove a CMT welding gun 20 to stack the test block layer by layer. The stacking direction was set to the Z axis, and the X and Y axes represented the coordinate axes within the stacking layer, as shown in FIG. Figure 2 Each layer adopts an oscillating stacking strategy to construct a test block with a certain thickness, as shown in Figure 3 The normalized process parameters used for this test block are: arc current 150A, welding gun 20 travel speed 4m / min, shielding gas (99.99% - pure argon) flow rate 15L / min. The effective size of the test block is as follows Figure 3 shown.

[0049] Table 1 Chemical composition of TA15 wire (wt.%)

[0050]

[0051] In order to study the fatigue crack growth rate of the material, 9 compact tensile alloy material specimens were processed from different positions of the arc additively manufactured titanium alloy TA15 material specimen. The sampling scheme of all compact tensile alloy material specimens is shown in the figure. Figure 4 As shown. The orientation of each compact tensile alloy material specimen is marked by the angle between the load application direction and the specimen stacking direction. 90° means that the direction of the force applied to the compact tensile alloy material specimen is perpendicular to the stacking direction. The compact tensile alloy material specimen is prepared, and the dimensions of the compact tensile alloy material specimen are as follows: Figure 5 As shown, the gap size a n =5mm, notch root radius ρ≤0.25mm. A dovetail groove is prepared on the compact tensile alloy material specimen, and a fine blade is machined in the dovetail groove to accommodate the crack opening displacement (COD) gauge.

[0052] Then, a fatigue crack growth experiment was carried out to obtain the fatigue crack length a and the corresponding cyclic stress loading cycle N, and the crack growth rate da / dN was obtained; the fatigue crack growth experiment was carried out on a compact tensile alloy material specimen with a target crack growth direction processed at a set position of the alloy material specimen, and the fatigue crack length a and the corresponding cyclic stress loading cycle N were obtained, and the crack growth rate da / dN was calculated using the seven-point increasing polynomial method.

[0053] In the embodiment of the present invention, a fatigue crack growth test was conducted on a compact tensile alloy material specimen according to the ASTM E647 standard. In this experiment, a COD gauge was used to monitor the crack length during the fatigue crack growth process. A sine wave with a frequency of 10 Hz was applied as the fatigue load. The stress ratio of the cyclic load was constant at R = 0.1. A sharp pre-crack with a length of 1 mm was prepared before the formal fatigue crack growth experiment. During the formal crack growth experiment, the gradient descent method was first used to obtain da / dN < 10 -8 Fatigue crack growth rate data of m / cycle (implementation C = -0.2mm -1 In the stable crack growth stage, a constant amplitude load control experiment is carried out, and the maximum force F of the applied load is set. max =1.5kN, until the compact tensile alloy material specimen breaks.

[0054] Finally, the stress intensity factor amplitude ΔK is calculated based on the fatigue crack length a and the applied stress amplitude Δσ:

[0055]

[0056] Where ΔK is the stress intensity factor amplitude; Δσ is the applied stress amplitude; a is the fatigue crack length; and β is the geometric correction factor.

[0057] In an embodiment of the present invention, the stress intensity factor amplitude ΔK is used as an effective driving factor for the associated fatigue crack growth rate. For compact tensile alloy material specimens, this embodiment provides a calculation formula for ΔK of such specimens:

[0058]

[0059] Where ΔP is the amplitude of the applied cyclic load; B is the thickness of the compact tensile alloy material specimen; W is the width of the compact tensile alloy material specimen; α is a parameter based on the fatigue crack length, and α = a / W.

[0060] Step S2: Establishing an alloy fatigue crack growth rate model to obtain a functional relationship between stress intensity factor and crack growth rate.

[0061] Step S21: Plot the crack growth rate da / dN and the stress intensity factor amplitude ΔK in step S1 on a double logarithmic coordinate system, as shown in Figure 6 As shown; it can be observed that there is a transition stress intensity factor amplitude ΔK n ≈ This value divides the ln(da / dN) data and ln(ΔK) data into two stages; the first crack growth stage, i.e., ΔK≤ΔK n In the second crack growth stage, the fatigue crack growth rate data shows greater fluctuation and slope. nIn the first stage, the fatigue crack growth rate data has a small slope and a weak fluctuation. Then a continuity equation is used to establish the fatigue crack growth rate model of the alloy.

[0062] Step S22: Transform the stress intensity factor amplitude ΔK n As the unknown parameters, the fatigue crack growth rate model of the alloy is established using the Heaviside step function and the classic Paris model:

[0063]

[0064] in, is the crack growth rate data; N is the cyclic stress loading cycle; C is the first material parameter of the alloy fatigue crack growth rate model; m1 is the second material parameter of the alloy fatigue crack growth rate model; m2 is the third material parameter of the alloy fatigue crack growth rate model; ΔK n is the amplitude data of the stress intensity factor; ΔK is the amplitude data of the stress intensity factor; H[ln(ΔK n )-ln(ΔK)] is the Heaviside step function, and [ln(ΔK n )-ln(ΔK)] is the independent variable of the Heaviside step function.

[0065] In the Heaviside step function H, if the independent variable [ln(ΔK n )-ln(ΔK)] is greater than or equal to zero, the function takes the value of 1, otherwise it takes the value of 0, specifically:

[0066]

[0067] Among them, H[ln(ΔK n )-ln(ΔK)] is the Heaviside step function.

[0068] Based on the experimental data in the embodiment of the present invention, the material correlation coefficient ΔK of the model is determined using the proposed continuous piecewise logarithmic linear fatigue crack growth rate model using the least squares method. n , C, m1 and m2. The optimal And the model parameter mean (lnC,m1,m2)=(-17.171,2.786,1.932). n and ΔK>ΔK n The model slopes of the stages are 4.718 (i.e., m1+m2) and 2.786 (i.e., m1). Figure 7 The figure shows the residual histogram and normal distribution fitting diagram of the first stage model of the present invention. nThe stage produces a large standard deviation of 0.283; Figure 8 The figure shows the residual histogram and normal distribution fitting diagram of the second stage model of the present invention. n The stage standard deviation is relatively small, at 0.130; the degree of data fluctuation between the two stages is quantified using the standard deviation of the model residuals.

[0069] Step S23: Determine the functional relationship between the stress intensity factor amplitude ΔK and the crack growth rate da / dN; in the first crack growth stage, ΔK≤ΔK n , the first slope is (m1+m2), and the first intercept is [lnC-m2·ln(ΔK n )]; in the second crack extension stage, ΔK>ΔK n , the second slope is m1, and the second intercept is lnC; the Heaviside step function can ensure that the alloy fatigue crack growth rate model has overall continuity.

[0070] Determine the transition stress intensity factor amplitude ΔK n Finally, the least squares method was used to determine the model fitting parameters of each compact tensile alloy material specimen. Table 2 lists in detail the parameter fitting results and mean square errors of the models of all compact tensile alloy material specimens.

[0071] Table 2 Parameter fitting results and RMSE of the model parameters of each compact tensile alloy material specimen.

[0072]

[0073] like Figure 9 The figure shows the linear fitting result of the fatigue crack growth rate model parameters in the first stage of the present invention, that is, ΔK≤ΔK n The {[lnC-m2·ln(ΔK n )],(m1+m2)} linear fitting results; such as Figure 10 The figure shows the linear fitting result of the fatigue crack growth rate model parameters in the second stage of the present invention, that is, when ΔK>ΔK n The linear fitting results of the model parameters (lnC,m1) of the stage; the model fitting parameters of all compact tensile alloy material specimens show a strong linear correlation, which means that there is no significant difference between the compact tensile alloy material specimens with different sampling positions and directions. n The average mean square error of the model in the stage is 0.178, and ΔK>ΔK n The average mean square error of the model in the stage is 0.049, indicating that the fatigue crack growth rate data is in the range of ΔK≤ΔK n There are large fluctuations in the region, which is consistent with Figure 7 and Figure 8The results shown are consistent.

[0074] Step S3: Determine the crack growth rate of the alloy material according to the alloy fatigue crack growth rate model to complete the fatigue life prediction.

[0075] Analyze the actual stress state of the alloy material and obtain the initial crack size a0 and critical crack size a c Based on the alloy fatigue crack growth rate model determined in step S2, numerical integration is performed to obtain the fatigue remaining service life N of the alloy material. c for:

[0076]

[0077] Among them, N c is the fatigue residual service life of the alloy material; a0 is the initial crack size; a c is the critical crack size.

[0078] In the embodiment of the present invention, the arc additive manufacturing TA15 titanium alloy material has an embedded elliptical crack, and the size of the embedded elliptical crack is a0 = 0.316mm. When subjected to a constant amplitude cyclic load with a stress amplitude of ΔP = 200MPa, the fracture threshold Calculate the crack length threshold a c =35.217mm. Considering the uncertainty of material parameters in fatigue crack growth model, the probabilistic life prediction is carried out. Using Monte Carlo method, 4×10 8 The fatigue crack growth life is predicted by using samples. The prediction results are as follows: Figure 11 As shown, the predicted remaining life average is 6.113×10 5 cycles, proving that the present invention can reliably predict the remaining service life of the alloy material.

[0079] The beneficial effects of the present invention are as follows: an embodiment of the present invention provides a fatigue life prediction method for alloy materials based on crack growth rate, uses a continuous piecewise logarithmic linear crack growth rate model to describe the crack growth rate data of the alloy, and establishes a continuous piecewise model by introducing the Heaviside step function and the transition stress intensity factor amplitude into the classic Paris model, effectively eliminating the uncertainty of researchers in selecting the transition stress intensity factor amplitude when using two discrete equations for fatigue crack growth rate modeling, thereby improving the accuracy of the model; an embodiment of the present invention conducts a crack growth experiment on arc additively manufactured titanium alloy materials, obtains crack growth rate data of compact tensile alloy material specimens with a target crack growth direction sampled from a set position of a test block prepared by an oscillating stacking strategy, and models the crack growth experimental data; through statistical analysis of the results obtained by the present invention, it is shown that the crack growth rate of arc additively manufactured titanium alloy materials is uniform and isotropic, and this model can reliably predict the remaining service life and can be promoted and applied in practice.

[0080] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for predicting fatigue life of alloy materials based on crack growth rate, characterized in that: It includes: S1: Prepare alloy material test blocks and obtain fatigue crack growth experimental data of the alloy materials; A compact tensile alloy specimen with a target crack propagation direction was machined at a set position on the alloy specimen. A fatigue crack propagation experiment was conducted to obtain the fatigue crack length a and the corresponding cyclic stress loading period N, and the crack propagation rate da / dN. The stress intensity factor amplitude ΔK was calculated based on the fatigue crack length a and the applied stress amplitude Δσ as follows: Where ΔK is the stress intensity factor amplitude; Δσ is the applied stress amplitude; a is the fatigue crack length; β is the geometric correction factor; S2: Establish an alloy fatigue crack growth rate model and obtain the functional relationship between stress intensity factor and crack growth rate; S21: Plot the crack growth rate da / dN and the stress intensity factor amplitude ΔK in step S1 on a double logarithmic coordinate system to determine the presence of a transition stress intensity factor amplitude ΔK n , the fatigue crack growth data of the alloy material is divided into the first crack growth stage and the second crack growth stage, and a continuity equation is used to establish the alloy fatigue crack growth rate model; S22: Transform the stress intensity factor amplitude ΔK n As the unknown parameters, the fatigue crack growth rate model of the alloy is established using the Heaviside step function and the classic Paris model: in, is the crack growth rate data; N is the cyclic stress loading cycle; C is the first material parameter of the alloy fatigue crack growth rate model; m1 is the second material parameter of the alloy fatigue crack growth rate model; m2 is the third material parameter of the alloy fatigue crack growth rate model; ΔK n is the amplitude of the transition stress intensity factor; ΔK is the amplitude data of the stress intensity factor; H[ln(ΔK n )-ln(ΔK)] is the Heaviside step function, and [ln(ΔK n )-ln(ΔK)] is the independent variable of the Heaviside step function; S23: Determine the functional relationship between the stress intensity factor amplitude ΔK and the crack growth rate da / dN; in the first crack growth stage, ΔK ≤ ΔK n , the first slope is (m1+m2), and the first intercept is [lnC-m2·ln(ΔK n )]; in the second crack extension stage, ΔK>ΔK n , the second slope is m1, and the second intercept is lnC; the Heaviside step function can ensure that the alloy fatigue crack growth rate model has overall continuity; S3: Determine the crack growth rate of the alloy material according to the alloy fatigue crack growth rate model and complete the fatigue life prediction; Analyze the actual stress state of the alloy material and obtain the initial crack size a0 and critical crack size a c Based on the alloy fatigue crack growth rate model determined in step S2, numerical integration is performed to obtain the fatigue remaining service life N of the alloy material. c for: Among them, N c is the fatigue residual service life of the alloy material; a0 is the initial crack size; a c is the critical crack size.

2. The method for predicting fatigue life of alloy materials based on crack growth rate according to claim 1, characterized in that: The preparation of the alloy material test block using the alloy wire in step S1 is specifically as follows: using the cold metal transfer arc welding mode, using a six-axis robotic arm to drive the welding gun, and stacking the alloy wire layer by layer on the forged alloy plate to prepare the alloy material test block. Each layer adopts an oscillation stacking strategy to construct an alloy material test block with a target thickness. The set processing parameters include arc current, welding gun travel speed and shielding gas flow rate.

3. The method for predicting fatigue life of alloy materials based on crack growth rate according to claim 1, characterized in that: The fatigue crack growth experiment in step S1 specifically includes: performing a fatigue crack growth experiment on a compact tensile alloy material specimen, obtaining the fatigue crack length a and the corresponding cyclic stress loading period N, and calculating the crack growth rate da / dN using a seven-point increasing polynomial method.

4. The method for predicting fatigue life of alloy materials based on crack growth rate according to claim 1, characterized in that: In step S21, there is a transition stress intensity factor amplitude ΔK n , the fatigue crack growth data of alloy materials are divided into the first crack growth stage and the second crack growth stage. Specifically, for alloy materials, the crack growth rate da / dN and the stress intensity factor amplitude ΔK data of all alloy material specimens are plotted in a double logarithmic coordinate system to determine the presence of the transition stress intensity factor amplitude ΔK n , the crack growth rate data ln(da / dN) and the stress intensity factor amplitude data ln(ΔK) are divided into the first crack growth stage and the second crack growth stage.

5. The method for predicting fatigue life of alloy materials based on crack growth rate according to claim 1, characterized in that: The Heaviside step function H[ln(ΔK n )-ln(ΔK)], specifically: In the Heaviside step function, if the independent variable [ln(ΔK n )-ln(ΔK)] is greater than or equal to zero, the function takes the value of 1, otherwise the function takes the value of 0, specifically: Among them, H[ln(ΔK n )-ln(ΔK)] is the Heaviside step function.

6. The method for predicting fatigue life of alloy materials based on crack growth rate according to claim 1, characterized in that: The alloy fatigue crack growth rate model in step S22 needs to use the least squares method to determine the transition stress intensity factor amplitude ΔK based on the fatigue crack growth experimental data of the alloy material. n .

7. The method for predicting fatigue life of alloy materials based on crack growth rate according to claim 1, characterized in that: In step S22, the first material parameter, the second material parameter and the third material parameter of the alloy fatigue crack growth rate model are determined by fitting the crack growth rate experimental data by the least squares method; and the material parameters (C, m1, m2) of the fatigue crack growth rate model of each alloy material specimen are obtained based on the crack growth rate data of each specimen taken from the set position and the target crack growth direction of the alloy material specimen.

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