Method for predicting fatigue strength of metal material under different average stresses based on damage parameters

Abstract

The invention discloses a method for predicting fatigue strength of a metal material under different average stresses based on damage parameters, and belongs to the technical field of fatigue performance testing of metal materials. According to the method, the fatigue damage increment of the material under the asymmetric load and the symmetric load is explored, and the quantitative relation between the fatigue damage of different average stress and the stress ratio is established. According to the method, the efficiency of obtaining the fatigue strength under different average stress is improved, the experimental quantity required by fatigue testing is reduced to a great extent, the method is effectively applicable to various types of steel including wind power steel, aluminum alloy, titanium alloy, nickel-based high-temperature alloy and other materials, and the method also has relatively good accuracy under various loading conditions and is suitable for popularization and application. The method is expected to be applied to the engineering field.

Description

Technical Field

[0001] The invention relates to the field of material science and engineering application technology, and in particular to a method for predicting fatigue strength of metal materials under different average stresses based on damage parameters. Background Art

[0002] In the field of engineering, metal fatigue fracture is the most common failure mode, which has attracted people's attention due to its suddenness and violent characteristics. In order to prevent the occurrence of fatigue failure, it is of great significance to predict the fatigue strength of metal component materials.

[0003] However, in the actual service environment of components, the working load is not constant. Accurately predicting the fatigue strength of materials under different load spectra is an important reference for component life estimation. Since the 1970s, many scholars such as Gerber, Goodman, Haigh and Soderberg have conducted research on the influence of average stress on material fatigue strength and made outstanding contributions to the evaluation of component safe service. However, in recent years, with the emergence of new materials and complex and changeable service conditions, previous models have certain limitations in representing fatigue strength under different average stresses. Therefore, it is urgent to establish a universal and accurate fatigue strength prediction method for materials under different average stress conditions. Summary of the invention

[0004] In order to overcome the limitations of traditional prediction methods, the present invention provides a method for predicting fatigue strength of metal materials under different average stresses based on damage parameters. This method analyzes the influence of average stress on material fatigue performance and damage mechanism, establishes the relationship between fatigue damage increase and stress ratio, and can accurately predict the fatigue strength of the same type of metal under different average stresses. This method has the advantages of high universality and high accuracy, and is simple and fast to apply, which can greatly reduce the demand for experimental volume.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for predicting fatigue strength of metal materials under different average stresses based on damage parameters, the method specifically comprising the following steps:

[0007] (1) preparing at least one set of symmetrical load test specimens and at least one set of asymmetrical load test specimens of a target metal material; the stress ratio of the symmetrical load test specimens is R=-1, and the stress ratio of the asymmetrical load test specimens is R;

[0008] (2) Fatigue strength test:

[0009] Symmetrical load test specimens and asymmetric load test specimens of the target metal material are subjected to symmetric load and asymmetric load fatigue strength tests respectively to obtain the fatigue strength σ under symmetric and asymmetric loads. -1 and σ w，max ;

[0010] (3) Parameter fitting:

[0011] Substitute the fatigue strength data obtained in step (2) into formula (1) to obtain the parameter value M at each stress ratio R in step (1): R , and M R The value and the corresponding R value are fitted by formula (2) to obtain the parameter value C;

[0012]

[0013] M R +1 = e C(R+1) (2);

[0015] (4) Fatigue strength prediction:

[0016] The obtained parameter value C is compared with the fatigue strength σ corresponding to the stress ratio R = -1. -1 Substituting into formula (3), the fatigue strength prediction value σ of the corresponding material at other stress ratios R can be obtained by calculation: w ;

[0017] σ w =σ -1 ·e C(R+1)

[0018] (3)

[0019] In the above step (1), fatigue tests with symmetric and asymmetric loads are performed under the same loading conditions (ambient temperature, stress concentration effect, number of rest cycles, etc.); and to ensure the accuracy of the prediction results, fatigue strength tests with no less than two sets of asymmetric loads may be performed.

[0020] In the above step (2) formula (1), σ w,max is the fatigue strength of the material expressed by the maximum stress ratio under various stress ratios; M for R = -1 R The value is 0, and it is fitted together with the fatigue strength data of asymmetric load through formula (2).

[0021] This method is applicable to steel, aluminum alloy, titanium alloy or nickel-based high-temperature alloy; applicable to various pre-deformation and heat treatment processes; applicable to conditions such as loading type, cycle number, test temperature and stress state.

[0022] The advantages and beneficial effects of the present invention are as follows:

[0023] 1. The prediction method of the present invention solves the problem that the fatigue strength prediction deviation of the traditional model at different stress ratios is large under conditions of high stress ratio and high temperature, high stress concentration effect, and ultra-high cycle number.

[0024] 2. The prediction method of the present invention has good prediction accuracy for the fatigue strength of various materials (including but not limited to steel, aluminum alloy, titanium alloy, high-temperature alloy) and process types (various heat treatment and surface treatment processes) under various stress ratio conditions.

[0025] 3. The prediction method of the present invention has good prediction accuracy for fatigue strength of different average stresses under different loading conditions such as loading type, cycle number, test temperature and stress state.

[0026] 4. The prediction method of the present invention has unique parameters, simple calculation and high accuracy. Only a small amount of fatigue tests can be used to effectively predict the fatigue strength of metal materials at different average stresses, which has the advantages of low cost and high efficiency. According to data statistics, the deviation between the predicted value and the experimental value of this method is mostly within ±15%, which reflects the accuracy and reliability of this method to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the fatigue strength prediction method for different stress ratios of metal materials.

[0028] Figure 2 R+1-M for different types of materials under various loading conditions R +1 relationship diagram; where: (a) R+1-M of steel R +1 relationship diagram; (b) R+1-M of aluminum alloy R +1 relationship diagram; (c) R+1-M of titanium alloy R +1 relationship diagram; (d) R+1-M of high temperature alloy R +1 for the relationship diagram.

[0029] Figure 3 To verify the accuracy of the prediction results for different types of materials under various loading conditions; among them: (a) steel; (b) aluminum alloy; (c) titanium alloy; (d) high-temperature alloy.

[0030] Figure 4 The fatigue strength prediction of Ti-6Al-4V titanium alloy under different mean stress conditions in Example 1; where: (a) R+1-M R +1 relationship diagram; (b) comparison between predicted values ​​and experimental values. Figure 5 is the fatigue strength prediction of GH3617M high temperature alloy at 700℃ under different average stress conditions in Example 2; where: (a) R+1-MR +1 relationship diagram; (b) comparison between predicted values ​​and experimental values.

[0031] Figure 6 The fatigue strength prediction of wind power steel Q500 under different average stress conditions in Example 3; where: (a) R+1-M R +1 relationship diagram; (b) comparison between predicted values ​​and experimental values. DETAILED DESCRIPTION

[0032] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0033] Figure 1 The operation flow of the method for predicting fatigue strength of metal materials under different average stresses of the present invention is as follows: (1) fatigue strength test; (2) parameter fitting; (3) fatigue strength prediction. The operation is simple and effective, and can be widely used in the prediction of fatigue strength of different metal materials under various loading conditions (such as Figure 2 ), the prediction deviation is mostly within ±15% (such as Figure 3 ).

[0034] The present invention is further described below in conjunction with embodiments.

[0035] Embodiment 1:

[0036] This embodiment predicts the fatigue strength of Ti-6Al-4V titanium alloy materials with different average stresses. High-cycle fatigue tests are carried out with symmetrical axial loads and stress ratios of 0 and 0.5 to measure the fatigue strength (experimental data) and use it to predict the fatigue strength of other untested stress ratios (verification data).

[0037] Step 1: Fatigue strength test of Ti-6Al-4V titanium alloy, fatigue strength σ under symmetrical load R = -1 -1 The fatigue strength σ of the asymmetric load R = 0 and 0.5 is 581 MPa. w，max They are 722MPa and 804MPa respectively, as shown in Table 1.

[0038] Step 2: Use the formula Calculate the stress ratios R = -1, 0 and 0.5 R The values ​​are 0, 0.243 and 0.384 respectively, and the corresponding stress ratio R is expressed by the formula M R +1 = e C(R+1) Fitting (such as Figure 4 (a) shows), the parameter C value is obtained to be 0.217.

[0039] Step 3: Obtain the fatigue strength σ when the parameter C value is 0.217 and the stress ratio R = -1 -1=581MPa into the formula σ w =σ -1 ·e C(R+1) The fatigue strength σ of the material under different average stresses can be obtained w Predicted values ​​(as shown in Table 1).

[0040] Step 4: To verify the accuracy of the predicted data, the predicted stress deviations are calculated. The deviation values ​​are shown in Table 1. The prediction accuracy is shown in Table 1. Figure 4 (b) (This step is for verification of the method and can be omitted in actual operation).

[0041] Table 1 Summary of prediction data of Ti-6Al-4V titanium alloy under different average stress conditions

[0042]

[0043] Embodiment 2:

[0044] This embodiment predicts the fatigue strength of GH3617M material at 700°C with different average stresses, conducts high cycle fatigue tests with symmetrical axial loads and stress ratios of -0.5 and 0.4, measures the fatigue strength (experimental data), and uses it to predict the fatigue strength of other untested stress ratios (verification data).

[0045] Step 1: Fatigue strength test of GH3617M material at 700℃, fatigue strength σ with symmetrical load R=-1 -1 The fatigue strength σ of 325 MPa, asymmetric load R = -0.5 and 0.4 w , max They are 348MPa and 488MPa respectively, as shown in Table 2.

[0046] Step 2: Use the formula The calculated stress ratios R = -1, -0.5 and 0.4 are M R The values ​​are 0, 0.072 and 0.502 respectively, and the corresponding stress ratio R is expressed by formula M R +1 = e C(R+1) Fitting (such as Figure 5 (a) shows), the parameter C value is obtained to be 0.280.

[0047] Step 3: Obtain the parameter C value 0.280 and the stress ratio R = -1 fatigue strength σ -1 =325MPa Substitute into the formula σ w =σ -1 ·e C(R+1) The fatigue strength σ of the material under different average stresses can be obtained w Predicted values ​​(as shown in Table 2).

[0048] Step 4: To verify the accuracy of the predicted data, the predicted stress deviations are calculated. The deviation values ​​are shown in Table 2. The prediction accuracy is shown in Table 2. Figure 5 (b) (This step is for verification of the method and can be omitted in actual operation).

[0049] Table 2 Summary of prediction data of GH3617M superalloy under different average stress conditions at 700℃

[0050]

[0051] Embodiment 3:

[0052] This embodiment predicts the fatigue strength of wind power steel Q500 with different average stresses, conducts high cycle fatigue tests with symmetrical axial loads and stress ratios of -1 and 0.1, measures the fatigue strength (experimental data), and uses it to predict the fatigue strength of other untested stress ratios (verification data).

[0053] Step 1: Perform fatigue strength test on Q500 steel for wind power generation. The fatigue strength σ of symmetrical load R = -1 -1 The fatigue strength σ of the asymmetric load R = 0.1 is 340MPa. w，max It is 575MPa, as shown in Table 3.

[0054] Step 2: Use the formula The calculated stress ratio R = -1 and M 0.1 R The values ​​are 0 and 0.691 respectively, and the corresponding stress ratio R is expressed by the formula M R +1 = e C(R+1) Fitting (such as Figure 6 (a) shows), the parameter C value is obtained to be 0.478.

[0055] Step 3: Obtain the fatigue strength σ when the parameter C value is 0.478 and the stress ratio R = -1 -1 =340MPa Substitute into the formula σ w =σ -1 ·e C(R+1) The fatigue strength σ of the material under different average stresses can be obtained w Predicted values ​​(as shown in Table 3).

[0056] Step 4: To verify the accuracy of the predicted data, the predicted stress deviations are calculated. The deviation values ​​are shown in Table 3. The prediction accuracy is shown in Table 3. Figure 6 (b) (This step is for verification of the method and can be omitted in actual operation).

[0057] Table 3 Summary of prediction data of Q500 steel for wind power under different average stress conditions

[0058]

[0059] The above embodiments are merely illustrative of the principles and performances of the present invention and are not exhaustive. Other embodiments may be obtained based on the present embodiments without creative work, and all of these embodiments fall within the protection scope of the present invention.

Claims

1. A method for predicting fatigue strength of metal materials under different average stresses based on damage parameters, characterized in that: The method specifically comprises the following steps: (1) preparing at least one set of symmetrical load test specimens and at least one set of asymmetrical load test specimens of a target metal material; the stress ratio of the symmetrical load test specimens is R=-1, and the stress ratio of the asymmetrical load test specimens is R; (2) Fatigue strength test: Symmetrical load test specimens and asymmetric load test specimens of the target metal material are subjected to symmetric load and asymmetric load fatigue strength tests respectively to obtain the fatigue strength σ under symmetric and asymmetric loads. -1 and σ w，max ; (3) Parameter fitting: Substitute the fatigue strength data obtained in step (2) into formula (1) to obtain the parameter value M at each stress ratio R in step (1): R , and M R The value and the corresponding R value are fitted by formula (2) to obtain the parameter value C; M R +1=e C(R+1) (2); (4) Fatigue strength prediction: The obtained parameter value C is compared with the fatigue strength σ corresponding to the stress ratio R = -1. -1 Substituting into formula (3), the fatigue strength prediction value σ of the corresponding material at other stress ratios R can be obtained by calculation: w ; s w =s -1 ·e C(R+1) (3)。 2. The fatigue strength prediction method of metal materials under different average stresses based on damage parameters according to claim 1 is characterized in that: In step (1), fatigue tests with symmetric and asymmetric loads are performed under the same loading conditions; and to ensure the accuracy of the prediction results, no less than two sets of fatigue strength tests with asymmetric loads may be performed.

3. The fatigue strength prediction method of metal materials under different average stresses based on damage parameters according to claim 2 is characterized in that: The loading conditions include loading type, cycle number, test temperature and stress state.

4. The fatigue strength prediction method of metal materials under different average stresses based on damage parameters according to claim 1 is characterized in that: Step (3) In formula (1), σ w,max is the fatigue strength of the material expressed by the maximum stress ratio under various stress ratios; M for R = -1 R The value is 0, and it is fitted together with the fatigue strength data of asymmetric load through formula (2).

5. The fatigue strength prediction method of metal materials under different average stresses based on damage parameters according to claim 1 is characterized in that: This method is applicable to steel, aluminum alloy, titanium alloy or nickel-based high-temperature alloy; Suitable for various pre-deformation and heat treatment processes; Applicable to different loading types, cycles, test temperatures, stress states and other conditions.

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