A fatigue life prediction method considering the surface state characteristics and gradient distribution of modified and strengthened surfaces

Abstract

The present invention provides a fatigue life prediction method considering the surface state characteristics of the modified and strengthened material and its gradient distribution, comprising the following steps: preparing fatigue specimens with different surface states by using different strengthening parameters; detecting the surface roughness profile, the residual stress with gradient distribution along the depth direction and the microhardness with gradient distribution along the depth direction of the fatigue specimens with different surface states respectively; determining the stress concentration factor of the fatigue specimen; fitting the residual stress and microhardness of the fatigue specimens with different surface states respectively by the cosine decay function to obtain the fitting curve; calculating the average area enclosed by the residual stress and microhardness distribution curves and the matrix value respectively; obtaining the fatigue life of the fatigue specimens with different surface states by fatigue test; establishing a fatigue life prediction model, and predicting the fatigue life of the test specimens by the fatigue life prediction model. The present invention can scientifically and accurately predict the fatigue life of the modified and strengthened material under different loads.

Description

Technical Field

[0001] The invention relates to the field of material life prediction, and in particular to a fatigue life prediction method taking into account modified and strengthened surface state characteristics and gradient distribution thereof. Background Art

[0002] Key components such as aircraft engine compressors serve in harsh environments of high temperature, high pressure and high speed, and are prone to fatigue failure, resulting in serious flight accidents or even aircraft destruction and loss of life. In order to improve the fatigue life and reliability of key components of aircraft engines, mechanical strengthening methods such as rolling, shot peening and ultrasonic impact are used to strengthen the surface of components to improve their service reliability and stability. The modified strengthening process significantly changes the surface morphology of the component, improves the surface hardness, introduces residual compressive stress and nanocrystalline layer, and significantly improves the fatigue life of the component. The fatigue life of the modified strengthening process is the result of the combined effect of surface state characteristics, and the characteristics such as residual stress and microhardness have gradient changes, which makes fatigue life prediction very complicated. It is difficult to achieve scientific prediction of fatigue life by considering only one factor (such as residual stress) and its influence at a certain location (such as crack initiation).

[0003] Most of the existing fatigue life prediction methods are based on stress, strain, energy and fracture mechanics. Among them, the stress and strain-based methods include SWT model, FS model, etc. This type of method not only needs to obtain material-related fatigue parameters (such as fatigue ductility index and fatigue ductility coefficient, etc.) through experiments, which consumes a lot of financial and material resources, but also calculates the life prediction based on the stress and strain information at a certain point. In addition, the influence of more factors such as surface roughness is not included in the model. Similarly, the energy-based method and the fracture mechanics-based method do not consider or fully consider the influence of the modified and strengthened surface state characteristics and their gradient distribution in principle. They are not suitable for the modified and strengthened fatigue life prediction and cannot effectively guarantee the scientificity and accuracy of the prediction results. Summary of the invention

[0004] In view of the shortcomings in the prior art, the present invention provides a fatigue life prediction method that takes into account the modified and strengthened surface state characteristics and its gradient distribution, which solves the problem that the traditional prediction model does not take into account the modified and strengthened surface state characteristics or only considers a single factor and its influence at a single position such as crack initiation, lacks scientificity and makes it difficult to ensure the accuracy of fatigue life prediction.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] A fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution comprises the following steps:

[0007] Fatigue specimens with different surface conditions were prepared using different strengthening parameters;

[0008] The surface roughness profile, the residual stress gradient distribution along the depth direction and the microhardness gradient distribution along the depth direction of fatigue specimens with different surface conditions were tested respectively;

[0009] The stress concentration factor Kst of fatigue specimens is determined by finite element simulation according to the surface roughness profiles of fatigue specimens with different surface conditions;

[0010] The residual stress gradient distribution diagram is drawn with the depth as the abscissa and the residual stress gradient distribution along the depth direction as the ordinate; the residual stress of fatigue specimens with different surface conditions is fitted by the cosine decay function to obtain the fitted residual stress fitting curve; the average area enclosed by the residual stress distribution curve and the horizontal line of the matrix residual stress value is calculated;

[0011] A microhardness gradient distribution diagram is drawn with depth as the horizontal coordinate and the microhardness value of the gradient distribution along the depth direction as the vertical coordinate; the microhardness of fatigue specimens with different surface conditions is fitted by a cosine attenuation function to obtain a fitted microhardness fitting curve; the average area enclosed by the microhardness distribution curve and the horizontal line where the matrix hardness value is located is calculated;

[0012] The fatigue life of fatigue specimens with different surface conditions is obtained through fatigue tests;

[0013] According to the surface stress concentration factor Kst, the average envelope area of ​​microhardness δ HV , the average area enclosed by residual stress Establish a fatigue life prediction model and determine the parameters in the fatigue life prediction model according to the fatigue life of fatigue specimens with different surface conditions;

[0014] The fatigue life of the test sample is predicted by the fatigue life prediction model.

[0015] Furthermore, the residual stress of fatigue specimens with different surface conditions is fitted by a cosine decay function to obtain a fitting residual stress curve, which is specifically determined according to the following formula:

[0016]

[0017] Where: r (h) is the residual stress; σ r0 is the matrix residual stress; h is the depth below the surface; A σ is the first control factor of the residual stress field gradient distribution curve; σ is the second control factor of the residual stress field gradient distribution curve; ω σ is the third control factor of the residual stress field gradient distribution curve; θσ It is the fourth control factor of the residual stress field gradient distribution curve.

[0018] Further, the average area enclosed by the residual stress distribution curve and the horizontal line of the matrix residual stress value is calculated, which is specifically determined according to the following formula:

[0019]

[0020] Where: is the average envelope area of ​​residual stress; h σ is the depth of residual stress variation layer; σ' r0 is the measured value of residual stress.

[0021] Furthermore, the microhardness of fatigue specimens with different surface conditions is fitted by a cosine attenuation function to obtain a fitted microhardness fitting curve, which is specifically determined according to the following formula:

[0022]

[0023] Where HV(h) is the microhardness; HV0 is the microhardness value of the matrix; A HV is the first control factor of the microhardness field gradient distribution curve; HV is the second control factor of the microhardness field gradient distribution curve; ω HV is the third control factor of the microhardness field gradient distribution curve; θ HV It is the fourth control factor of the microhardness field gradient distribution curve.

[0024] Further, the average area enclosed by the microhardness distribution curve and the horizontal line where the matrix hardness value is located is calculated, which is specifically determined according to the following formula:

[0025]

[0026] In the formula, δ HV is the average envelope area of ​​microhardness; h HV is the depth of the microhardness variation layer; HV'0 is the average measured microhardness of the matrix.

[0027] Furthermore, according to the surface stress concentration factor Kst and the average envelope area of ​​microhardness δ HV , the average area enclosed by residual stress The fatigue life prediction model is established, which is specifically expressed as:

[0028]

[0029] In the formula, ΔK st is the difference between the surface stress concentration coefficient after strengthening and the stress concentration without; δ HVis the average value of the area enclosed by the microhardness distribution curve and the matrix hardness, is the average value of the area enclosed by the residual stress distribution curve; A is parameter one in the fatigue life model; α1 is parameter two in the fatigue life model; α2 is parameter three in the fatigue life model; α4 is parameter four in the fatigue life model; α1<0, α2>0, α3>0.

[0030] Furthermore, the rolling strengthening parameters include rolling pressure, rolling feed, rolling speed and rolling times; at least one rolling strengthening parameter is changed to prepare no less than 4 rolling fatigue specimens with different surface conditions.

[0031] Furthermore, the surface roughness profiles of fatigue specimens with different surface conditions were detected by a roughness profile measuring instrument; the residual stress gradient distribution along the depth direction of fatigue specimens with different surface conditions was detected by an X-ray diffraction residual stress tester; and the microhardness gradient distribution along the depth direction of fatigue specimens with different surface conditions was detected by a Wechsler microhardness tester.

[0032] The beneficial effects of the present invention are:

[0033] The fatigue life prediction method considering the surface state characteristics of the modified reinforcement and its gradient distribution described in the present invention introduces three parameters, namely the modified reinforcement surface stress concentration coefficient, the average residual stress distribution and the average microhardness distribution, on the basis of comprehensively considering the surface state characteristics of the modified reinforcement, and establishes a modified reinforcement fatigue life prediction model, which solves the problem that the traditional prediction model does not consider the surface state characteristics of the modified reinforcement or only considers a single factor and its influence at a single position such as crack initiation, lacks scientificity and makes it difficult to ensure the accuracy of fatigue life prediction. The technical solution proposed in the present invention can scientifically and accurately predict the fatigue life of the modified reinforcement under different loads, and is less expensive and has a high engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. The drawings described below are some embodiments of the present invention. For ordinary technicians in this field, it is obvious that other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 The present invention provides a flow chart of a fatigue life prediction method that takes into account the modified and strengthened surface state characteristics and their gradient distribution.

[0036] Figure 2 This is a surface roughness profile diagram of different rolling parameters in this embodiment.

[0037] Figure 3Graph showing the initial residual stress distribution and average envelope area of ​​this embodiment.

[0038] Figure 4 Graph showing the initial microhardness distribution and average envelope area of ​​this embodiment.

[0039] Figure 5 This is a schematic diagram of the finite element calculation of the stress concentration factor in this embodiment.

[0040] Figure 6 This is the roughness contour diagram of the model verification group in this embodiment.

[0041] Figure 7 This is the residual stress and microhardness distribution and fitting diagram of the model verification group of this embodiment.

[0042] Figure 8 This is a comparison chart between the fatigue life prediction value and the test value of this embodiment. DETAILED DESCRIPTION

[0043] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0044] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "axial", "radial", "vertical", "horizontal", "inner", "outer" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0045] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] like Figure 1 As shown, the fatigue life prediction of Ti60 rolled hardened under different service loads is taken as an example to illustrate the present invention in detail, and the specific process is as follows:

[0047] S01: Use different strengthening parameters to prepare fatigue specimens with different surface states. To ensure significant differentiation between surface states, specifically: the rolling strengthening parameters include rolling pressure, rolling feed, rolling speed and rolling times; at least one rolling strengthening parameter is changed to prepare 5 rolling fatigue specimens with different surface states; considering the dispersion of fatigue life, 3 specimens are prepared for each type. The specific rolling parameters are shown in Table 1. Fatigue test bars are made according to national standards, and fatigue test bars are divided into normal temperature fatigue test bars and high temperature fatigue test bars. In the embodiment, only one rolling strengthening parameter is changed, and two or three rolling strengthening parameters can also be changed.

[0048] Table 1 Preparation parameters of samples with different surface conditions

[0049]

[0050] S02: The surface roughness profile of the fatigue test bar is tested by a roughness profile measuring instrument, and the residual stress of the fatigue test bar and the gradient distribution of microhardness along the depth direction are tested by an X-ray diffraction residual stress tester and a Vickers microhardness tester. Specifically: the surface roughness profile of each test bar is measured on a German MarSurf GD 25 contact surface profiler. The sampling length is 0.8mm and the evaluation length is 4mm. The test results are as follows: Figure 2 The residual stress was measured using a PROTO LXRD MG2000 residual stress tester. When testing the residual stress at different depths, the layer was peeled off using chemical corrosion. The solution used was a mixed solution of nitric acid (25 ml), hydrofluoric acid (30 ml), hydrochloric acid (30 ml) and H2O (15 ml). The test results are shown in Figure 3 The microhardness test was conducted on a Japanese FUTURE-TECH FM 800 micro Vickers hardness tester with a test load of 25 gf and a dwell time of 10 s. The test results are shown in Figure 4 shown.

[0051] S03: The process of calculating the stress concentration factor using the finite element method is as follows Figure 5 As shown. According to the measured roughness profile data, the surface roughness profile curve is redrawn using Solidworks. Then the surface roughness profile curve is imported into Abaqus, and the model is established for finite element analysis. The degrees of freedom of the left and lower boundaries of the model are restricted respectively, and a certain load is applied to the right end. The load in this study is 200MPa, and other values ​​can also be used, which has no effect on the calculation results. The mesh size is about 0.5μm, and the unit type is CPS4R. The material constitutive model used is JC constitutive, and the specific parameters are shown in Table 2. The calculated surface stress concentration coefficients Kst of #1 to #5 samples are 1.1296, 1.0614, 1.0477, 1.035 and 1.0344, respectively, and the difference ΔKst between the surface stress concentration coefficient after strengthening and the stress concentration free are 0.1296, 0.0614, 0.0477, 0.035 and 0.0344, respectively.

[0052] Table 2 Constitutive parameters of Ti60 JC

[0053] A(MPa) B(MPa) C n m Poisson's ratio Young's modulus (MPa) 900 775.81 0.0256 0.6465 0.8353 0.31 114000

[0054] The residual stress gradient distributions in five different states were fitted using the cosine attenuation function shown in formula (1). The fitting results are shown in Figure 3 The obtained parameters are shown in Table 3. The fitting adjustment determination coefficients are all above 0.9, and the fitting effect is good. The average area enclosed by the fitting curve and the horizontal line of the matrix residual stress value is calculated by formula (2). The matrix residual stress value is 0, that is, the horizontal coordinate line. The results are shown in Figure 3 As shown in Table 3, it should be noted that when solving the life model, the area used is a negative value.

[0055]

[0056] Where: r (h) is the residual stress; σ r0 is the matrix residual stress; h is the depth below the surface; A σ is the first control factor of the residual stress field gradient distribution curve; σ is the second control factor of the residual stress field gradient distribution curve; ω σ is the third control factor of the residual stress field gradient distribution curve; θ σ It is the fourth control factor of the residual stress field gradient distribution curve.

[0057]

[0058] Where: is the average envelope area of ​​residual stress; hσ is the depth of residual stress variation layer; σ' r0 is the measured value of residual stress (0MPa).

[0059] Table 3 Calculation results of the average envelope area of ​​the residual stress distribution curve envelope

[0060] serial number <![CDATA[A σ ]]> <![CDATA[λ σ ]]> <![CDATA[ω σ ]]> <![CDATA[θ σ ]]> <![CDATA[Adj-R 2 ]]> <![CDATA[h σ (μm)]]> Average area #1 -1307.35944 0.00639 0.00728 -1.23851 0.95335 390 342.403 #2 -1340.49264 0.00562 0.00644 -1.15361 0.94985 425 378.287 #3 -1381.75136 0.00468 0.00556 -1.07536 0.94562 460 438.585 #4 -1466.46475 0.00423 0.00487 -1.02523 0.94388 490 492.443 #5 -2178.38633 0.00552 0.00395 -1.17441 0.93979 525 522.701

[0061] The microhardness gradient distributions in five different states were fitted using the cosine attenuation function shown in formula (3). The fitting results are shown in Figure 4 The obtained parameters are shown in Table 4. The fitting adjustment determination coefficients are all above 0.88, and the fitting effect is good.

[0062] The average area enclosed by the fitting curve and the matrix hardness is calculated by formula (4), and the results are as follows: Figure 4 And as shown in Table 4.

[0063]

[0064] Where HV(h) is the microhardness; HV0 is the microhardness value of the matrix; A HV is the first control factor of the microhardness field gradient distribution curve; HV is the second control factor of the microhardness field gradient distribution curve; ω HV is the third control factor of the microhardness field gradient distribution curve; θ HV It is the fourth control factor of the microhardness field gradient distribution curve.

[0065]

[0066] In the formula, δ HV is the average envelope area of ​​microhardness; h HV is the depth of the microhardness variation layer; HV'0 is the average measured microhardness of the matrix (371HV).

[0067] Table 4 Calculation results of average envelope area of ​​microhardness distribution curve

[0068] serial number <![CDATA[A HV ]]> <![CDATA[λ HV ]]> <![CDATA[ω HV ]]> <![CDATA[θ HV ]]> <![CDATA[HV0]]> <![CDATA[Adj-R 2 ]]> <![CDATA[h HV (μm)]]> Average area #1 69.04653 0.02101 0.01783 -1.94269 370.4604 0.91602 185 3.314 #2 102.04444 0.01871 0.01656 -1.84382 370.74871 0.91341 205 8.224 #3 140.98535 0.01761 0.01512 -1.82054 371.74258 0.88839 240 12.396 #4 209.64939 0.01665 0.01231 -1.74113 372.77519 0.91299 285 18.372 #5 224.50718 0.01474 0.01246 -1.75085 375.48919 0.88923 305 24.659

[0069] S04: The round bar specimens after rolling strengthening were subjected to tension and compression cycle fatigue tests at room temperature and 450℃ on INSTRON 8872 and QBG-50 electro-hydraulic servo fatigue testing machines, respectively. The load range of the fatigue test at room temperature was -286~607MPa, and the load range of the fatigue test at high temperature was -229~485MPa. The life data of fatigue specimens with different surface conditions were recorded, and 3 specimens were collected for each surface condition, and the average value was taken as the fatigue life of the condition. The corresponding average fatigue life (accurate to hundreds) under room temperature fatigue conditions were 203600 cycles, 214800 cycles, 227100 cycles, 230900 cycles and 268500 cycles, respectively, and the corresponding average fatigue life under high temperature fatigue conditions were 187400 cycles, 198800 cycles, 206600 cycles, 224800 cycles and 237000 cycles, respectively.

[0070] S05: The surface stress concentration factor Kst and the average envelope area of ​​microhardness δ calculated in S03 are HV , the average area enclosed by residual stress The fatigue life data in S04 are substituted into the fatigue life prediction model shown in fatigue life equation (5), the equation is transformed into a linear equation system, and the hyperstatic equation system is solved using Matlab to obtain the parameters A, α1, α2, and α3 in the fatigue life model. The results are shown in Table 5.

[0071]

[0072] In the formula, ΔK st is the difference between the surface stress concentration coefficient after strengthening and the stress concentration without; δ HV is the average value of the area enclosed by the microhardness distribution curve and the matrix hardness, is the average value of the area enclosed by the residual stress distribution curve; A is parameter one in the fatigue life model; α1 is parameter two in the fatigue life model; α2 is parameter three in the fatigue life model; α4 is parameter four in the fatigue life model; α1<0, α2>0, α3>0.

[0073] Table 5 Fatigue life prediction model parameters

[0074] temperature A <![CDATA[α1]]> <![CDATA[α2]]> <![CDATA[α3]]> normal temperature 189458.58195 -0.21187 2.58652 0.30167 high temperature 167989.24420 -0.01798 3.63109 0.27566

[0075] S06: Predict the fatigue life of the test sample using the fatigue life prediction model.

[0076] In order to verify the accuracy of the model, an additional set of room temperature and high temperature fatigue life tests were carried out for verification. The rolling process parameters are shown in Table 6. The measured roughness profiles are shown in Figure 6 As shown, the calculated stress concentration factor is 1.037 and ΔKst is 0.037. Figure 7(a) To verify the residual stress distribution of the sample surface, the cosine decay function is used to fit the residual stress distribution. The fitting parameters are shown in Table 7. The adjustment coefficient of determination is 0.96075, and the fitting result is good. According to the cosine decay curve obtained by fitting, the average envelope area of ​​the surface residual stress is calculated by formula (2) to be 521.26. Figure 7 (b) is the distribution of surface microhardness of the validation group samples. The cosine attenuation function is used to fit the microhardness distribution. The fitting results are shown in Table 8. The adjusted determination coefficient is 0.91426. The average envelope area of ​​the surface microhardness is calculated to be 19.68 by formula (4). Substituting the above parameters into the fatigue life model in S05, the calculated fatigue life at room temperature and high temperature is 243275 and 226838 respectively. Three groups of fatigue tests at room temperature and high temperature are carried out and compared with the predicted values. Figure 8 As shown in the figure, the prediction results are all within the 1.5-fold dispersion band, achieving a high-precision prediction of fatigue life.

[0077] Table 6 Rolling processing parameters for model verification

[0078] serial number Rolling pressure P(MPa) <![CDATA[Rolling feed f B (mm / r)]]> <![CDATA[Rolling speed v B (r / min)]]> Rolling times n #6 18 0.1 600 1

[0079] Table 7 Calculation results of average area of ​​residual stress distribution in the verification group

[0080] serial number <![CDATA[A σ ]]> <![CDATA[λ σ ]]> <![CDATA[ω σ ]]> <![CDATA[θ σ ]]> <![CDATA[Adj-R 2 ]]> <![CDATA[h σ (μm)]]> Average area #6 -1333.14605 0.00358 0.00481 -0.91494 0.96075 480 521.26

[0081] Table 8 Calculation results of average area of ​​microhardness distribution of verification group

[0082] serial number <![CDATA[A HV ]]> <![CDATA[λ HV ]]> <![CDATA[ω HV ]]> <![CDATA[θ HV ]]> <![CDATA[HV0]]> <![CDATA[Adj-R 2 ]]> <![CDATA[h HV (μm)]]> Average area #6 174.62553 0.01472 0.01654 -1.84942 374.47471 0.91426 280 19.68

[0083] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0084] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. All equivalent embodiments or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution, characterized in that: The steps include: Fatigue specimens with different surface conditions were prepared using different strengthening parameters; The surface roughness profile, the residual stress gradient distribution along the depth direction and the microhardness gradient distribution along the depth direction of fatigue specimens with different surface conditions were tested respectively; The stress concentration factor Kst of fatigue specimens is determined by finite element simulation according to the surface roughness profiles of fatigue specimens with different surface conditions; The residual stress gradient distribution diagram is drawn with the depth as the abscissa and the residual stress gradient distribution along the depth direction as the ordinate; the residual stress of fatigue specimens with different surface conditions is fitted by the cosine decay function to obtain the fitted residual stress fitting curve; the average area enclosed by the residual stress distribution curve and the horizontal line of the matrix residual stress value is calculated; A microhardness gradient distribution diagram is drawn with depth as the horizontal coordinate and the microhardness value of the gradient distribution along the depth direction as the vertical coordinate; the microhardness of fatigue specimens with different surface conditions is fitted by a cosine attenuation function to obtain a fitted microhardness fitting curve; the average area enclosed by the microhardness distribution curve and the horizontal line where the matrix hardness value is located is calculated; The fatigue life of fatigue specimens with different surface conditions is obtained through fatigue tests; According to the surface stress concentration factor Kst, the average envelope area of ​​microhardness δ HV , the average area enclosed by residual stress The fatigue life prediction model is established, which is specifically expressed as: In the formula, ΔK st is the difference between the surface stress concentration coefficient after strengthening and the stress concentration without; δ HV is the average value of the area enclosed by the microhardness distribution curve and the matrix hardness, is the average value of the area enclosed by the residual stress distribution curve; A is parameter 1 in the fatigue life model; α1 is parameter 2 in the fatigue life model; α2 is parameter 3 in the fatigue life model; α4 is parameter 4 in the fatigue life model; α1<0, α2>0, α3>0; And determine the parameters in the fatigue life prediction model according to the fatigue life of fatigue specimens with different surface conditions; The fatigue life of the test sample is predicted by the fatigue life prediction model.

2. The fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution according to claim 1 is characterized in that: The residual stress of fatigue specimens with different surface conditions is fitted by the cosine decay function to obtain the residual stress fitting curve, which is determined according to the following formula: Where: r (h) is the residual stress; σ r0 is the matrix residual stress; h is the depth below the surface; A σ is the first control factor of the residual stress field gradient distribution curve; σ is the second control factor of the residual stress field gradient distribution curve; ω σ is the third control factor of the residual stress field gradient distribution curve; θ σ It is the fourth control factor of the residual stress field gradient distribution curve.

3. The fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution according to claim 2 is characterized in that: Calculate the average area enclosed by the residual stress distribution curve and the horizontal line of the matrix residual stress value, which is determined according to the following formula: Where: is the average envelope area of ​​residual stress; h σ is the depth of residual stress variation layer; σ r ′0 is the measured residual stress value.

4. The fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution according to claim 1 is characterized in that: The microhardness of fatigue specimens with different surface conditions is fitted by the cosine attenuation function to obtain the fitted microhardness fitting curve, which is specifically determined according to the following formula: Where HV(h) is the microhardness; HV0 is the microhardness value of the matrix; A HV is the first control factor of the microhardness field gradient distribution curve; HV is the second control factor of the microhardness field gradient distribution curve; ω HV is the third control factor of the microhardness field gradient distribution curve; θ HV It is the fourth control factor of the microhardness field gradient distribution curve.

5. The fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution according to claim 4 is characterized in that: Calculate the average area enclosed by the microhardness distribution curve and the horizontal line where the matrix hardness value is located, which is determined according to the following formula: In the formula, δ HV is the average envelope area of ​​microhardness; h HV is the depth of the microhardness variation layer; HV'0 is the average measured microhardness of the matrix.

6. The fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution according to claim 1 is characterized in that: At least one rolling strengthening parameter is changed to prepare rolling fatigue specimens with no less than 4 different surface conditions; the rolling strengthening parameters include rolling pressure, rolling feed, rolling speed and rolling times.

7. The fatigue life prediction method considering the modified and strengthened surface state characteristics and its gradient distribution according to claim 1 is characterized in that: The surface roughness profile of fatigue specimens with different surface conditions is detected by a roughness profile measuring instrument; the residual stress gradient distribution along the depth direction of fatigue specimens with different surface conditions is detected by an X-ray diffraction residual stress tester; the microhardness gradient distribution along the depth direction of fatigue specimens with different surface conditions is detected by a Vickers microhardness tester.

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