Prediction Method for Rotating Bending Fatigue Life of Alloy Steel Considering Machined Surface Integrity

Abstract

The present invention discloses a method for predicting the rotating bending fatigue life of alloy steel considering machining surface integrity, which relates to the technical field of estimating the rotating bending fatigue life of alloy steel. It includes S1, obtaining surface layer characteristic parameters; S2, calculating and obtaining the initial microcrack growth coefficient and the critical microcrack growth threshold; S3, judging the quantitative relationship between the initial microcrack growth coefficient and the critical microcrack growth threshold; S4, obtaining the fixed coefficient in the Gibbs energy model; S5, obtaining the revised crack initiation life prediction model; S6, the crack growth coefficient and the crack growth exponent; S7, obtaining the revised crack growth life prediction model; S8, establishing a rotating bending fatigue life prediction model of low alloy steel considering machining surface integrity. This method can effectively improve the prediction accuracy of the rotating bending fatigue life of low alloy steel and avoid errors caused by the uncertainty of surface characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of estimating the rotating bending fatigue life of alloy steel, and particularly relates to a method for predicting the rotating bending fatigue life of alloy steel considering the machining surface integrity. Background Art

[0002] With the wide application of low-alloy steel in the fields of vehicles, aerospace, and ordnance, its complex service environment poses higher requirements for the rotating bending fatigue life of large mechanical structures such as vehicles, aerospace, and ordnance. Once fatigue fracture occurs, it will cause major accidents and losses.

[0003] After machining, the free surface bears large loads and is affected by the external environment, such as thermal stress, oxidation, wear, etc. The initiation and propagation of fatigue cracks are attributed to the machining surface integrity. However, traditional fatigue failure prediction models, such as the microcrack propagation model, the critical plane method, etc., all fail to consider the influence of surface integrity. In recent years, among the prediction models in the literature, only the single-factor influence of surface roughness, surface axial residual stress, and microhardness is often considered, and the multi-factors of surface integrity are not taken into account. Therefore, these models are difficult to accurately predict the fatigue resistance of the surface layer material. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for predicting the rotating bending fatigue life of alloy steel considering the machining surface integrity, which can effectively improve the prediction accuracy of the rotating bending fatigue life of low-alloy steel and avoid errors caused by the uncertainty of surface characteristics.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for predicting the rotating bending fatigue life of alloy steel considering the machining surface integrity includes the following steps,

[0007] S1: Obtain the surface layer characteristic parameters through experimental measurement or calculation. The surface layer characteristic parameters include the arithmetic mean deviation of the surface profile R a , the average spacing of the profile micro-irregularities R sm , the microhardness HV , the surface axial residual stress σ res and the grain size G ( h );

[0008] S2: Based on the microcrack propagation theory, calculate the initial microcrack propagation coefficient Δ K sur,max and the critical microcrack propagation threshold Δ K th;

[0009] S3: Determine whether the initial microcrack growth coefficient Δ K sur,max satisfies 2Δ K sur,max ≤Δ K th . If it is satisfied, execute S4; otherwise, execute S6;

[0010] S4: Obtain the fixed coefficient in the Gibbs energy model through micro-column compression test;

[0011] S5: Substitute the arithmetic mean deviation of the surface profile R a , microhardness HV and grain size G ( h ) into the Gibbs energy model, and at the same time input the fixed coefficient obtained in S4 to obtain a revised crack initiation life prediction model;

[0012] S6: Obtain the crack growth coefficient C and crack growth exponent m in the Paris formula through fatigue crack growth test;

[0013] S7: Substitute the surface axial residual stress σ res obtained in S1 into the Paris formula, and at the same time input material parameters to obtain a revised crack growth life prediction model;

[0014] S8: Combine the crack initiation life prediction model obtained in S5 and the crack growth life prediction model obtained in S7 to establish a low-alloy steel rotating bending fatigue life prediction model considering machining surface integrity.

[0015] Preferably, step S1 specifically includes:

[0016] S11: Measure the arithmetic mean deviation of the surface profile R a , the average spacing of the profile micro-irregularities R sm , and calculate the surface topography characteristic parameter as:

[0017] , when ;

[0018] , when ;

[0019] S12: Calculate the grain size G ( h) is:

[0020] Wherein, h 0 is the layer depth when the surface axial residual stress begins to transform; h is the distance from the starting point of the surface axial residual stress transformation; is the weight function considering the surface axial residual stress at different depths on the fatigue life; is the surface axial residual stress with respect to h the fitting function of the change;

[0021] S13: Measure the microhardness of the surface HV , measure the surface axial residual stress σ res .

[0022] Preferably, step S2 specifically includes:

[0023] S21: Use the surface topography characteristic parameter and the surface axial residual stress σ res to calculate the microcrack propagation factor K sur,max , and the calculation formula is:

[0024]

[0025] Wherein, σ max the maximum stress value on the surface during the rotating bending-fatigue test;

[0026] S22: From the rotating bending fatigue test, the stress ratio is R = -1, the microcrack propagation coefficient Δ K sur,max = K sur,max - K sur,min = 2 K sur,max ;

[0027] S23: Use the microhardness HV to calculate the critical microcrack propagation threshold Δ K th , and the calculation formula is:

[0028]

[0029] Wherein, is the critical crack propagation size, obtained through the staircase method fatigue test, the crack size corresponding to 1×10 7 cycles.

[0030] Preferably, step S5 is specifically as follows:

[0031] A fatigue crack initiation model considering surface layer characteristic parameters:

[0032]

[0033] Wherein, Mk represents the fatigue limit stress; α is the stacking fault energy and the coefficient of slip irreversibility, 0 < α ≤ 1; N i is the crack initiation life; D is the slip band width; ν is the Poisson's ratio, λ is a material constant, taking 0.005.

[0034] Preferably, the Paris formula is:

[0035]

[0036] Step S7 is specifically as follows:

[0037] ,

[0038] Wherein, N p is the crack propagation life, C is the crack propagation coefficient, m is the crack propagation exponent, C and m are both obtained from crack propagation tests; l 0 is the initial crack length; l th is the crack propagation limit size at the time of instantaneous fracture.

[0039] Preferably, step S8 is specifically as follows:

[0040] A fatigue life prediction model considering surface layer characteristic parameters is:

[0041] , when Δ K sur,max ≤ Δ K th ;

[0042] , when Δ K sur,max＞ Δ K th ;

[0043] In the present invention, by relating the surface layer grain size, microhardness, surface axial residual stress, and surface topography characteristic parameters that affect the rotating bending fatigue performance of low alloy steel, a mapping relationship corresponding to surface integrity and fatigue life is established, thereby better understanding the close relationship between fatigue life and the surface integrity of parts from the perspective of fatigue performance. At the same time, it is possible to more accurately study the influence mechanism and law of the surface integrity of parts formed under different process conditions on fatigue performance, and also provide an accurate quantitative index for evaluating the influence degree of the machining surface integrity of low alloy steel on the rotating bending fatigue life.

[0044] Using the surface integrity index parameters for fatigue life prediction can avoid errors caused by the uncertainty of surface characteristics, improve the stability and accuracy of prediction, and can more conveniently and quickly predict the rotating bending fatigue life of low alloy steel. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of the prediction method of the present invention;

[0046] Figure 2 is a schematic diagram of the specimen used in the fatigue test of the present invention;

[0047] Figure 3 is a schematic diagram of laser shock peening for changing the surface characteristic parameters of the specimen in the present invention;

[0048] Figure 4 is a comparison chart of the prediction results of the rotating bending fatigue life of low alloy steel corresponding to a 32CrNi low alloy steel material in the present invention, the prediction results of the model before revision, and the test data; DETAILED DESCRIPTION OF THE INVENTION

[0049] The present invention will be further described below with reference to the accompanying drawings:

[0050] As Figures 1 - 4 shown, a method for predicting the rotating bending fatigue life of alloy steel considering the machining surface integrity includes the following steps:

[0051] S1: Obtain the surface layer characteristic parameters through experimental measurement or calculation. The surface layer characteristic parameters include the arithmetic mean deviation of the surface profile R a , the average spacing of the profile micro-irregularities R sm , the microhardness HV , the surface axial residual stress σ res and the grain size G ( h );

[0052] S2: Based on the microcrack propagation theory, calculate the initial microcrack propagation coefficient ΔK sur,max and the critical microcrack propagation threshold Δ K th ;

[0053] S3: Determine whether the initial microcrack propagation coefficient Δ K sur,max satisfies 2Δ K sur,max ≤Δ K th . If it is satisfied, execute S4; otherwise, execute S6;

[0054] S4: Obtain the fixed coefficient in the Gibbs energy model through micro-column compression tests;

[0055] S5: Substitute the arithmetic mean deviation of the surface profile R a , microhardness HV and grain size G ( h ) into the Gibbs energy model, and at the same time input the fixed coefficient obtained in S4 to obtain a revised crack initiation life prediction model;

[0056] S6: Obtain the crack propagation coefficient C and crack propagation exponent m in the Paris formula through fatigue propagation tests;

[0057] S7: Substitute the surface axial residual stress σ res obtained in S1 into the Paris formula, and at the same time input material parameters to obtain a revised crack propagation life prediction model;

[0058] S8: Combine the crack initiation life prediction model obtained in S5 and the crack propagation life prediction model obtained in S7 to establish a low-alloy steel rotating bending fatigue life prediction model considering machining surface integrity.

[0059] Step S1 specifically includes:

[0060] S11: Measure the arithmetic mean deviation of the surface profile R a , the average spacing of profile micro-irregularities R sm , and calculate the surface topography characteristic parameter as:

[0061] , when ;

[0062] , when ;

[0063] S12: Calculate the grain size G ( h ) is:

[0064] Wherein, h 0 is the layer depth at which the surface axial residual stress begins to transform; h is the distance from the location where the surface axial residual stress begins to transform; is the weight function considering the surface axial residual stress at different depths on the fatigue life; is the surface axial residual stress as h changes in the fitting function;

[0065] S13: Measure the microhardness of the surface HV , measure the surface axial residual stress σ res .

[0066] Step S2 specifically includes:

[0067] S21: Use the surface topography characteristic parameter and the surface axial residual stress σ res to calculate the microcrack propagation factor K sur,max , and the calculation formula is:

[0068]

[0069] Wherein, σ max The maximum stress value on the surface during the rotating bending-fatigue test;

[0070] S22: From the rotating bending fatigue test, the stress ratio is R =-1, the microcrack propagation coefficient Δ K sur,max = K sur,max - K sur,min =2 K sur,max ;

[0071] S23: Use the microhardness HV to calculate the critical microcrack propagation threshold Δ K th , and the calculation formula is:

[0072]

[0073] Wherein, is the critical crack propagation size, obtained through the fatigue test by the up-and-down method, and is the crack size corresponding to 1×10 7 cycles.

[0074] Step S5 is specifically as follows:

[0075] Fatigue crack initiation model considering surface layer characteristic parameters:

[0076]

[0077] Among them, Mk represents the fatigue limit stress; α is the stacking fault energy and the coefficient of slip irreversibility, 0 < α ≤ 1, α Taking 0.5 can better conform to the experimental data; N i is the crack initiation life; D is the slip band width; ν is the Poisson's ratio, λ is a material constant, taking 0.005.

[0078] The Paris formula is:

[0079]

[0080] Step S7 is specifically as follows:

[0081] ,

[0082] Among them, N p is the crack propagation life, C is the crack propagation coefficient, m is the crack propagation exponent, C and m are both obtained from the crack propagation test; l th is the crack propagation limit size at the time of sudden fracture; l 0 is the initial crack length; l is the crack propagation size.

[0083] Step S8 is specifically as follows:

[0084] The fatigue life prediction model considering surface layer characteristic parameters is:

[0085] , when Δ K sur,max ≤ Δ K th ;

[0086] , when ΔK sur,max＞ Δ K th When.

[0087] Among them, N surf is the total fatigue life.

[0088] Taking 32CrNi low alloy steel material as an example below, the tensile strength σ b is 1200 MPa, and the yield strength σ 0.2 is 1143 MPa. The specimen dimensions used in the fatigue test are as Figure 2 shown. By laser shock peening, the surface layer characteristic parameters of the specimen are changed. The schematic of laser shock peening is as Figure 3 shown. The specimen dimensions used in the fatigue test are as Figure 2 shown. The fatigue test frequency is 100 Hz, rotational bending fatigue, the stress ratio is -1, and the maximum surface stress load σ max is 800 MPa. The implementation steps of the present invention are introduced in detail:

[0089] S1: First, perform laser shock peening treatment on the fatigue specimen with different energy values. Taking the laser shock peening process energy of 15 J, the spot diameter of 3 mm, and the overlap rate of 50% as an example, measure the arithmetic mean deviation of the surface profile R a and the average spacing of the profile micro-irregularities R sm after the 32CrNi low alloy steel is strengthened by a laser interferometer, and obtain the surface topography characteristic parameter using the formula in S11; obtain the microhardness HV through a microhardness tester; obtain the surface axial residual stress X through an σ res X-ray surface axial residual stress instrument; obtain the curve of grain size varying with depth through EBSD software, and obtain the grain size G ( h ) = 1.2 μm using the formula in S12.

[0090] S2: Substitute the surface topography characteristic parameter and the maximum stress value σ max on the surface during the rotational bending-fatigue test into the formulas in S21 and S22 to obtain the initial microcrack growth coefficient Δ K sur,max = 0.61 MPa∙m 1 / 2 , and substitute the microhardness HVSubstitute into the formula of S23 to obtain the calculated critical microcrack growth threshold Δ K th = 2.37 MPa∙m 1 / 2 .

[0091] S3: Compare Δ K sur,max and Δ K th , 2Δ K sur,max <Δ K th , and then directly run S4.

[0092] S4: Through the micro-column compression test, obtain the Taylor factor in the Gibbs energy model M , the dislocation friction resistance k , Mk represents the fatigue limit stress, the slip band width D , the Poisson's ratio ν , the stacking fault energy and the coefficient of slip irreversibility α , the material constant λ , in this embodiment α take 0.5, λ take 0.005.

[0093] S5: Substitute the surface topography characteristic parameters , the microhardness HV and the grain size G ( h ), the slip band width D , the Poisson's ratio ν , the stacking fault energy and the coefficient of slip irreversibility α , the material constant λ into the formula in S5 to obtain the predicted crack initiation life of 7×10 5 cycles.

[0094] S6: Through the fatigue crack growth test, obtain the critical crack size at the instant of sudden fracture in the Paris formula l th , the crack growth coefficient C and the crack growth exponent m .

[0095] S7: Substitute the maximum stress value on the surface during the externally applied rotating bending-fatigue test σ max , the surface axial residual stress obtained in S1 σ res , the critical crack size at the instant of sudden fracture obtained in S6 l th , the crack growth coefficientC and crack growth index m Substitute into the formula in S7 to obtain the predicted fatigue crack growth life of 2×10 5 cycles.

[0096] S8: By combining the crack initiation life prediction model obtained in S5 and the crack growth life prediction model obtained in S7, a rotating bending fatigue life prediction model of 32CrNi low alloy steel considering machining surface integrity can be obtained.

[0097] Figure 4 The schematic diagram of the prediction accuracy of the rotating bending fatigue life prediction results of the 32CrNi low alloy steel before and after revision and the fatigue test data is given. It can be seen from the figure that the rotating bending fatigue life prediction method of low alloy steel considering machining surface integrity in the embodiment of the present invention has an average prediction error of 9%, which is much larger than the 32% prediction error of the model before revision. Thus, it can be known that the method of the present invention can accurately predict the rotating bending fatigue life of low alloy materials.

[0098] The prediction of the tensile-torsional fatigue life of low alloy steel is similar to the prediction method of the rotating bending fatigue life. The prediction of the tensile-torsional fatigue life changes the material constants at the time of tensile-torsional failure, resulting in a change in life. The prediction of the rotating bending fatigue life obtains the corresponding fatigue failure material constants through a rotating bending fatigue test, thereby changing the fatigue life.

[0099] The above embodiments are only several illustrations of the concept and implementation of the present invention, and do not limit it. Under the concept of the present invention, the technical solutions without substantial transformation are still within the protection scope.

Claims

1. A method for predicting the fatigue life of alloy steel rotating bending considering the integrity of the processed surface, characterized in that: The following steps are included: S1: Obtaining surface layer characteristic parameters by experimental measurement or calculation, wherein the surface layer characteristic parameters include the arithmetic mean deviation of the surface profile R a , the average spacing of the contour micro-roughness R sm , Microhardness HV , surface axial residual stress σ res and grain size G ( h ); S2: Based on the microcrack extension theory, the initial microcrack extension coefficient Δ is calculated K sur,max and the critical microcrack growth threshold Δ K th ; S3: Determine the initial microcrack expansion coefficient Δ K sur,max Does it satisfy 2Δ? K sur,max ≤Δ K th , if satisfied, execute S4, otherwise execute S6; S4: Obtain the fixed coefficients in the Gibbs energy model through micro-column compression tests; S5: Calculate the average deviation of the surface profile in S1 R a , Microhardness HV and grain size G ( h ) is brought into the Gibbs energy model, and the fixed coefficients obtained in S4 are input to obtain the revised crack initiation life prediction model; S6: Obtain the crack growth coefficient in the Paris formula through fatigue growth test C and crack extension index m ; S7: The surface axial residual stress obtained in S1 σ res Substitute it into the Paris formula and input the material parameters to obtain the revised crack growth life prediction model; S8: Combining the crack initiation life prediction model obtained in S5 and the crack propagation life prediction model obtained in S7, a low alloy steel rotary bending fatigue life prediction model considering the integrity of the machined surface is established.

2. The method for predicting the fatigue life of alloy steel rotating bending considering the integrity of the processed surface according to claim 1 is characterized in that: Step S1 specifically includes: S11: Measuring the arithmetic mean deviation of the surface profile R a , the average spacing of the contour micro-roughness R sm , calculate the surface morphology characteristic parameters for: ,when hour; ,when hour; S12: Calculate grain size G ( h )for: ， in, h 0 is the layer depth when the surface axial residual stress begins to transform; h is the distance from the surface where the axial residual stress begins to transform; is the weighting function of the surface axial residual stress at different depths on fatigue life; is the surface axial residual stress h Variation of the fitting function; S13: Measuring surface microhardness HV , measuring the surface axial residual stress σ res .

3. The method for predicting the fatigue life of alloy steel rotating bending considering the integrity of the processed surface according to claim 2 is characterized in that: Step S2 specifically includes: S21: Using surface morphology parameters and surface axial residual stress σ res Calculation of microcrack growth factor K sur,max , the calculation formula is: ， in, σ max Maximum stress value on the surface during the rotational bending-fatigue test; S22: By rotating bending fatigue test, the stress ratio is R =-1, the microcrack extension coefficient Δ K sur,max = K sur,max - K sur,min =2 K sur,max ; S23: Using microhardness HV Calculate the critical microcrack growth threshold Δ K th , the calculation formula is: ， in, is the critical crack extension size, obtained by the lifting method fatigue test, 1×10 7 The corresponding crack size at the cycle.

4. The method for predicting the alloy steel rotary bending fatigue life considering the integrity of the processed surface according to claim 3 is characterized in that: Step S5 is specifically as follows: Fatigue crack initiation model considering characteristic parameters of surface layer: ， in, Mk represents the fatigue limit stress; α is the stacking fault energy and slip irreversibility coefficient, 0< α ≤1; N i is the crack initiation life; D is the slip band width; ν is Poisson's ratio, λ is the material constant and is taken as 0.

005.

5. The method for predicting the alloy steel rotary bending fatigue life considering the integrity of the processed surface according to claim 4 is characterized in that: The Paris formula is: Step S7 is specifically as follows: ， in, N p is the crack growth life, C is the crack growth coefficient, m is the crack extension index, C and m All obtained from crack extension tests; l 0 is the initial crack length; l th It is the limit size of crack extension when instantaneous fracture occurs; l is the crack extension size.

6. The method for predicting the fatigue life of alloy steel rotating bending considering the integrity of the processed surface according to claim 5, characterized in that: Step S8 is specifically as follows: The fatigue life prediction model considering the characteristic parameters of the surface layer is: , when Δ K sur,max ≤Δ K th hour; , when Δ K sur,max＞ Δ K th ; when in, is the total fatigue life.

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