Alloy steel rotating bending fatigue life prediction method considering machining surface integrity

Abstract

The invention discloses an alloy steel rotating bending fatigue life prediction method considering machining surface integrity, and relates to the technical field of alloy steel rotating bending fatigue life estimation methods, and the method comprises the steps: S1, obtaining surface layer characteristic parameters; s2, calculating to obtain an initial microcrack propagation coefficient and a critical microcrack propagation threshold value; s3, judging a quantitative relationship between the initial microcrack propagation coefficient and a critical microcrack propagation threshold value; s4, obtaining a fixed coefficient in the Gibbs energy model; s5, obtaining a revised crack initiation life prediction model; s6, calculating a crack propagation coefficient and a crack propagation index; s7, obtaining a revised crack propagation life prediction model; and S8, establishing a low-alloy steel rotating bending fatigue life prediction model considering the integrity of the machined surface. According to the method, the prediction precision of the rotating bending fatigue life of the low alloy steel can be effectively improved, and errors caused by uncertainty of surface characteristics are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of the estimation method for 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 the prediction models in the literature in recent years, 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: A method for predicting the rotating bending fatigue life of alloy steel considering the machining surface integrity, comprising the following steps, S1: Obtain the surface layer characteristic parameters through experimental measurement or calculation, and 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 ); S2: Based on the microcrack propagation theory, calculate and obtain the initial microcrack propagation coefficient Δ K sur,max and the critical microcrack propagation threshold Δ K th ; S3: Judge the initial microcrack propagation coefficient ΔK sur,max Whether it satisfies 2Δ K sur,max ≤Δ K th , if it is satisfied, execute S4, otherwise execute S6; S4: Through the micro-column compression test, obtain the fixed coefficient in the Gibbs energy model; S5: Substitute the arithmetic mean deviation of the surface profile in S1 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; S6: Through the fatigue crack growth test, obtain the crack growth coefficient C and crack growth exponent m in the Paris formula; S7: Substitute the surface axial residual stress obtained in S1 σ res into the Paris formula, and at the same time input the material parameters to obtain a revised crack growth life prediction model; 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 the machining surface integrity.

[0006] Preferably, step S1 specifically includes: S11: Measure the arithmetic mean deviation of the surface profile R a , the average spacing of the profile irregularities R sm , and calculate the surface topography characteristic parameter as: , when ; , when ; S12: Calculate the grain size G ( h ) as: wherein, h 0 is the layer depth when the surface axial residual stress starts 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 fitting function of the surface axial residual stress varying with h ; S13: Measure the microhardness of the surface HV , and measure the surface axial residual stress σ res .

[0007] Preferably, step S2 specifically includes: S21: Use the surface topography characteristic parameters and the surface axial residual stress σ res to calculate the microcrack propagation factor K sur,max , and the calculation formula is: where σ max is the maximum stress value on the surface during the rotating bending-fatigue test; S22: From the rotating bending fatigue test, with the stress ratio R =-1, the microcrack propagation coefficient Δ K sur,max = K sur,max - K sur,min =2 K sur,max ; S23: Use the microhardness HV to calculate the critical microcrack propagation threshold Δ K th , and the calculation formula is: where is the critical crack propagation size, obtained through the staircase method fatigue test, and is the crack size corresponding to 1×10 7 cycle times.

[0008] Preferably, step S5 is specifically: Consider the fatigue crack initiation model of the surface layer characteristic parameters:

[0009] where Mk represents the fatigue limit stress; α is the stacking fault energy and the coefficient of slip irreversibility degree, 0< α ≤1; N i is the crack initiation life; D is the slip band width; ν is the Poisson's ratio, λis a material constant, taking 0.005.

[0010] Preferably, the Paris formula is: Step S7 is specifically: , 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 the crack propagation test; l 0 is the initial crack length; l th is the crack propagation limit size at the occurrence of instantaneous fracture.

[0011] Preferably, step S8 is specifically: The fatigue life prediction model considering the surface layer characteristic parameters is: , when Δ K sur,max ≤Δ K th ; , when Δ K sur,max＞ Δ K th ;

[0012] In the present invention, by relating the surface layer grains, 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 the surface integrity and fatigue life is established, thereby better understanding the close relationship between the fatigue life and the surface integrity of the part from the perspective of fatigue performance. At the same time, it can more accurately study the influence mechanism and law of the surface integrity of parts formed under different process conditions on the 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.

[0013] 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

[0014] Figure 1 is the flow chart of the prediction method of the present invention; Figure 2Schematic diagram of the specimen used in the fatigue test of the present invention; Figure 3 Schematic diagram of laser shock peening for changing the surface characteristic parameters of the specimen of the present invention; Figure 4 Comparison diagram of the predicted results of the low alloy steel rotating bending fatigue life, the predicted results of the model before revision and the test data corresponding to a 32CrNi low alloy steel material of the present invention; Specific implementation manners

[0015] The present invention will be further described below with reference to the accompanying drawings: 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 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 ); 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 ; S3: Judge 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; S4: Obtain the fixed coefficient in the Gibbs energy model through micro-column compression test; S5: Substitute the arithmetic mean deviation of the surface profile R a , the microhardness HV and the grain size G ( h ) in S1 into the Gibbs energy model, and at the same time input the fixed coefficient obtained in S4 to obtain the revised crack initiation life prediction model; S6: Obtain the crack propagation coefficient C and the crack propagation exponent m in the Paris formula through fatigue propagation test; S7: Bring the surface axial residual stress obtained in S1 σ res into the Paris formula, and at the same time input material parameters to obtain a revised crack growth life prediction model; 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.

[0016] Step S1 specifically includes: S11: Measure the arithmetic mean deviation of the surface profile R a and the mean spacing of the surface micro-irregularities R sm to calculate the surface topography characteristic parameters as: when ; when ; S12: Calculate the grain size G ( h ) as: where 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 transformation of the surface axial residual stress; 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; S13: Measure the microhardness of the surface HV , and measure the surface axial residual stress σ res .

[0017] Step S2 specifically includes: S21: Use the surface topography characteristic parameters and the surface axial residual stress σ res to calculate the microcrack growth factor K sur,max , and the calculation formula is: where σ max is the maximum stress value on the surface during the rotating bending-fatigue test; S22: From the rotating bending fatigue test with a stress ratio of R = -1, the microcrack growth coefficient Δ K sur,max = K sur,max - K sur,min = 2 K sur,max ; S23: Calculate the critical microcrack growth threshold Δ HV using microhardness K th . The calculation formula is: where is the critical crack growth size, obtained through the staircase fatigue test, and is the crack size corresponding to 1×10 7 cycle times.

[0018] Step S5 is specifically as follows: A fatigue crack initiation model considering surface layer characteristic parameters:

[0019] where 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 match 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.

[0020] The Paris formula is: Step S7 is specifically as follows: , where 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 critical crack propagation limit size at the time of instantaneous fracture; l 0 is the initial crack length; l is the crack propagation size.

[0021] Step S8 is specifically as follows: The fatigue life prediction model considering the surface layer characteristic parameters is: , when Δ K sur,max ≤Δ K th ; , when Δ K sur,max＞ Δ K th .

[0022] Among them, N surf is the total fatigue life.

[0023] Taking the 32CrNi low alloy steel material as an example, the tensile strength σ b is 1200 MPa, the yield strength σ 0.2 is 1143 MPa, the dimensions of the specimens used in the fatigue test are as Figure 2 shown, the surface layer characteristic parameters of the specimens are changed by laser shock peening, and the schematic diagram of laser shock peening is as Figure 3 shown, the dimensions of the specimens 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: S1: First, the fatigue specimens are subjected to laser shock peening treatment with different energy values. Taking the laser shock peening process energy of 15 J, the spot diameter of 3 mm, and the overlapping rate of 50% as an example, the arithmetic mean deviation of the surface profile R a and the average spacing of the profile micro-irregularities R sm of the 32CrNi low alloy steel after strengthening are measured by a laser interferometer, and the surface topography characteristic parameter is obtained by using the formula in S11; the microhardness HV is obtained by a microhardness tester; the surface axial residual stress X is obtained by an X-ray surface axial residual stress instrument σ res ; the curve of the grain size varying with the depth is obtained by EBSD software, and the grain size G ( h ) = 1.2 μm is obtained by using the formula in S12.

[0024] S2: Substitute the surface topography characteristic parameters and the maximum stress value on the surface during the rotating bending-fatigue test σ max into the formulas in S21 and S22 to obtain the initial microcrack growth coefficient Δ K sur,max = 0.61 MPa∙m 1 / 2 , substitute the microhardness HV into the formula in S23 to obtain the calculated critical microcrack growth threshold Δ K th = 2.37 MPa∙m 1 / 2 .

[0025] S3: Compare Δ K sur,max and Δ K th , 2Δ K sur,max < Δ K th , then directly run S4.

[0026] S4: Through the micro-column compression test, obtain the Taylor factor M in the Gibbs energy model, 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 slip irreversibility degree coefficient α , the material constant λ , in this embodiment α take 0.5, λ take 0.005.

[0027] 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 slip irreversibility degree coefficient α , the material constant λ into the formula in S5 to obtain the predicted crack initiation life of 7×10 5 cycles.

[0028] S6: Through the fatigue crack growth test, obtain the crack growth limit size l th at the moment of sudden fracture, the crack growth coefficient C and the crack growth exponent m .

[0029] 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 crack propagation limit size at the moment of instantaneous fracture obtained in S6 l th , the crack propagation coefficient C and the crack propagation exponent m into the formula in S7 to obtain the predicted fatigue crack propagation life of 2×10 5 cycles.

[0030] S8: Combining the crack initiation life prediction model obtained in S5 and the crack propagation life prediction model obtained in S7, a rotating bending fatigue life prediction model of 32CrNi low alloy steel considering the machining surface integrity can be obtained.

[0031] 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 the 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. Therefore, it can be known that the method of the present invention can accurately predict the rotating bending fatigue life of low alloy materials.

[0032] The prediction of the tensile-torsion fatigue life of low alloy steel is similar to the method of predicting the rotating bending fatigue life. The prediction of the tensile-torsion fatigue life changes the material constants at the time of tensile-torsion failure, so that the life changes. The prediction of the rotating bending fatigue life obtains the corresponding fatigue failure material constants through the rotating bending fatigue test, and then the fatigue life changes.

[0033] The above embodiments are only several descriptions 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 growth 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 2 or 3, 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.

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.

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