A method for predicting fatigue strength of metallic materials under different stress ratios

Abstract

This invention discloses a method for predicting the fatigue strength of metallic materials under different stress ratios, belonging to the field of materials science and engineering application technology. This method establishes the relationship between the tensile strength, symmetrical load fatigue strength, and stress ratio of a material. Only tensile tests and a small number of fatigue tests are required to effectively predict the fatigue strength of the material under various stress ratios. This method effectively reduces the amount of experimentation, greatly saving time, money, and manpower costs. It provides accurate predictions for materials such as steel, aluminum alloys, magnesium alloys, and high-temperature alloys under different loading conditions (loading method, number of cycles, experimental temperature, and stress state).

Description

Technical Field

[0001] This invention relates to the field of materials science and engineering application technology, specifically a method for predicting the fatigue strength of metallic materials under different stress ratios. Background Technology

[0002] Metal fatigue is the most common failure mode of components. Statistics show that over 80% of mechanical part failures are fatigue failures. Since components do not exhibit obvious macroscopic deformation before fatigue failure, and the fracture is sudden, fatigue failure is difficult to detect and accidents often occur unexpectedly. Therefore, predicting the fatigue strength of component materials is of great significance for industrial production.

[0003] In engineering, the operating loads on most components are not constant. Effectively predicting fatigue strength under different load spectra is crucial for evaluating the safe service life of these components. Traditional models have limitations in representing the fatigue strength of materials at different stress ratios. For example, the Goodman model is conservative for ductile materials, the Gerber model is inaccurate for brittle materials, and the Soderberg model is conservative for most metallic materials. Furthermore, under high stress ratios, traditional models exhibit significant prediction biases, greatly complicating the prediction of fatigue strength in components operating at high stress ratios (such as bolts). Therefore, establishing a universally applicable and accurate fatigue strength prediction relationship for materials under various stress ratios is of paramount importance. Summary of the Invention

[0004] To reduce the cost of obtaining the fatigue limit of materials under different stress ratios, this invention provides a method for predicting the fatigue strength of metallic materials under different stress ratios. This method establishes the relationship between tensile strength, fatigue strength, and stress ratio, requiring only tensile testing and a small number of fatigue strength tests to determine the fatigue strength of similar metals under different stress ratios. This method uses only a single material parameter, significantly reducing the amount of experimentation required, and is more accurate than traditional formula-based predictions.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for predicting fatigue strength of metallic materials under different stress ratios, the method specifically includes the following steps:

[0007] (1) Perform an axial tensile test on the target metallic material to obtain its tensile properties, including tensile strength σ. b ;

[0008] (2) Perform fatigue strength tests on the target metallic material to obtain the fatigue strength under symmetrical axial load (stress ratio R=-1) and asymmetrical axial load (R≠-1) conditions, respectively, and use σ -1 and σ w express;

[0009] (3) Calculate the average stress σ of the material under asymmetric axial load. m ; Obtain σ under the load condition in step (2) w / σ -1 With σ m / σ b The value is plotted in a Cartesian coordinate system (σ). m / σ b , σ w / σ -1 The coordinates are used to linearly fit the data points of the asymmetric axial load through the coordinates (0, 1). The slope of the straight line is parameter C. Parameter C is directly substituted into formula (1).

[0010] (1);

[0011] (4) The predicted fatigue strength σ of the material under the predicted stress ratio R can be obtained by calculation according to formula (1). w .

[0012] In step (2) above, in order to improve the accuracy of the prediction results, 2 to 3 asymmetric axial loads can be selected for fatigue strength testing.

[0013] In step (3) above, if two or more other stress ratios are selected for the test, or if two or more asymmetric axial load conditions are selected for the test, the parameter C is obtained by linear fitting of the above coordinates through the (0, 1) coordinates.

[0014] This method is applicable to steel, aluminum alloys, magnesium alloys, or nickel-based high-temperature alloys; it is applicable to various pre-deformation and heat treatment processes; and it is applicable to different loading methods, cycle numbers, test temperatures, and stress states.

[0015] The advantages and beneficial effects of this invention are as follows:

[0016] 1. The prediction method of this invention solves the problem of large prediction deviation of traditional models under high stress ratios.

[0017] 2. The prediction method of this invention has excellent prediction accuracy for the fatigue strength of various materials (including but not limited to steel, aluminum alloys, titanium alloys, and high-temperature alloys) and process types (various pre-deformation, heat treatment, and surface treatment processes) under various stress ratio conditions. As the material and process change, the value of parameter C varies, but the data remains within the σ range. m / σb - σ w / σ -1 There is a good linear relationship in the coordinate system.

[0018] 3. The prediction method of this invention has excellent prediction accuracy for fatigue strength of various stress ratios under different loading conditions. The value of parameter C changes with variations in cycle number, loading type, and ambient temperature, but the data remains within the range of σ. m / σ b -σ w / σ -1 There is a good linear relationship in the coordinate system.

[0019] 4. The prediction method of this invention has unique parameters, is simple to calculate, and has high accuracy. It can effectively predict the fatigue strength of various stress ratios using only tensile tests and a small number of fatigue strength tests, offering advantages of low cost and high efficiency. Through extensive data analysis, the deviation between the predicted and experimental values ​​of this method is mostly within 10%, demonstrating the method's accuracy and reliability to a certain extent. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for predicting fatigue strength of metallic materials under different stress ratios.

[0021] Figure 2 For different types of materials σ m / σ b - σ w / σ -1 Relationship diagram; where: (a) σ of steel m / σ b - σ w / σ -1 Relationship diagram; (b) σ of aluminum alloy m / σ b - σ w / σ -1 Relationship diagram; (c) σ of titanium alloy m / σ b - σ w / σ -1 Relationship diagram; (d) σ of high-temperature alloys m / σ b -σ w / σ -1 Relationship diagram.

[0022] Figure 3 For different loading conditions and test environments, σ m / σ b - σ w / σ -1 Relationship diagram; where: (a) σ for different cycle numbers m / σb - σ w / σ -1 Relationship diagram; (b) σ for different loading methods m / σ b - σ w / σ -1 Relationship diagram; (c) σ at different test temperatures m / σ b -σ w / σ -1 Relationship diagram; (d) σ under different stress concentration states m / σ b - σ w / σ -1 Relationship diagram.

[0023] Figure 4 To verify the accuracy of prediction results for metallic materials under different stress ratios; among which: (a) steel; (b) aluminum alloy; (c) titanium alloy; (d) high-temperature alloy.

[0024] Figure 5 The fatigue strength prediction of Ti-6Al-4V titanium alloy in Example 1 under various stress ratio conditions.

[0025] Figure 6 This is a prediction of the fatigue strength of GH3617M high-temperature alloy at 700℃ under various stress ratios in Example 2. Detailed Implementation

[0026] The present invention will be further described below with reference to the embodiments and accompanying drawings.

[0027] Example 1:

[0028] This embodiment predicts the fatigue strength of Ti-6Al-4V titanium alloy with different stress ratios. High-cycle fatigue tests were conducted on symmetrical axial loads with stress ratios of 0.1 and 0.6 to determine the fatigue strength (experimental data), which was then used to predict the fatigue strength of other untested stress ratios (validation data). The prediction process is referenced below. Figure 1 The specific process is as follows:

[0029] Step 1: An axial tensile test was performed on the tensile specimen of Ti-6Al-4V titanium alloy to obtain a tensile strength of 1035 MPa.

[0030] Step 2: Fatigue tests were conducted on Ti-6Al-4V titanium alloy under symmetrical axial load (stress ratio R=-1) and stress ratios of 0.1 and 0.6. The fatigue strengths were 581 MPa, 711 MPa and 870 MPa, respectively, as shown in Table 1.

[0031] Step 3: Calculate the average stress σ of the material under asymmetric axial loading conditions. m Using the measured tensile and fatigue data, the stress σ of Ti-6Al-4V titanium alloy under symmetrical loading (stress ratio R=-1) and stress ratios of 0.1 and 0.6 was calculated. m / σ b and σ w / σ -1 The values ​​are plotted on σ as the x and y axes, respectively. m / σ b - σ w / σ -1 Relationship diagram (e.g.) Figure 2 (c)), and perform linear fitting on the above data points using the point (0, 1), as follows: Figure 5 As shown in (a), the parameter C of the slope of the fitted line is obtained, and the value of C is 0.709.

[0032] Step 4: Combine the parameters C, the stress ratio to be predicted R, and the tensile strength σ. b and symmetrical load fatigue strength σ -1 Substitute into the formula The fatigue strength σ is obtained. w The predicted values ​​are shown in Table 1.

[0033] Step 5: To verify the accuracy of the predicted data, calculate the deviations of each predicted stress. The deviation values ​​are shown in Table 1. The accuracy of the prediction is as follows: Figure 5 (b) shows the method (this step is for verification and can be omitted in actual operation).

[0034] Table 1. Summary of Predicted Relevant Data for Ti-6Al-4V Titanium Alloy under Various Stress Ratios

[0035]

[0036] Example 2:

[0037] This embodiment predicts the fatigue strength of GH3617M high-temperature alloy under different stress ratios at 700℃. High-cycle fatigue tests were conducted on symmetrical axial loads (stress ratio R=-1), stress ratios of -0.5 and 0, and the fatigue strength was measured (experimental data). This data was then used to predict the fatigue strength of other untested stress ratios (verification data).

[0038] Step 1: An axial tensile test was conducted on the tensile specimen of GH3617M high-temperature alloy at 700℃ to obtain a tensile strength of 786 MPa.

[0039] Step 2: Fatigue tests were conducted on GH3617M high-temperature alloy at 700℃ under symmetrical axial load (stress ratio R=-1) and stress ratios of -0.5 and 0. The fatigue strengths were 325 MPa, 348 MPa and 405 MPa, respectively, as shown in Table 2.

[0040] Step 3: Calculate the average stress σ of the material under asymmetric axial loading conditions. m Using the measured tensile and fatigue data, the σ values ​​of GH3617M superalloy under symmetrical loading (stress ratio R = -1), stress ratios of -0.5, and 0 were calculated. m / σ b and σ w / σ -1 The values ​​are plotted on σ as the x and y axes, respectively. m / σ b - σ w / σ -1 In the relationship diagram, a linear fit is performed on the above data points through the point (0, 1) (e.g.) Figure 6 As shown in (a), the parameter C of the slope of the fitted line is obtained, and the value of C is 0.908.

[0041] Step 4: Combine the parameters C, the stress ratio to be predicted R, and the tensile strength σ. b and symmetrical load fatigue strength σ -1 Substitute into the formula The fatigue strength σ is obtained. w Predicted values ​​(as shown in Table 2).

[0042] Step 5: To verify the accuracy of the predicted data, calculate the deviations of each predicted stress. The deviation values ​​are shown in Table 2. The accuracy of the prediction is as follows: Figure 6 (b) shows the method (this step is for verification and can be omitted in actual operation).

[0043] Table 2. Summary of prediction data for GH3617M high-temperature alloy at 700℃ under various stress ratio conditions.

[0044]

[0045] The method of this invention is applicable to steel, aluminum alloys, magnesium alloys, or nickel-based high-temperature alloys (such as... Figure 2 It is applicable to various pre-deformation and heat treatment processes, and suitable for different loading methods, cycle numbers, test temperatures, and stress states (such as...). Figure 3 The accuracy of prediction results for metallic materials under different stress ratios is verified as follows: Figure 4 .

[0046] The above embodiments are merely illustrative of the principles and performance of the present invention and are not exhaustive. People can obtain other embodiments based on these embodiments without creative effort, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for predicting fatigue strength of metallic materials under different stress ratios, characterized in that: The method specifically includes the following steps: (1) Perform axial tensile testing on the target metallic material to obtain its tensile properties; the tensile properties include tensile strength σ b ; (2) Perform fatigue strength tests on the target metallic material to obtain the stress ratio R Fatigue strength σ under symmetrical axial load condition = -1 -1 and stress ratio R Fatigue strength σ under asymmetric axial loading conditions ≠-1 w ; (3) Calculate the average stress σ of the material under asymmetric axial load conditions. m ; Obtain σ under each load condition in step (2) w / σ -1 With σ m / σ b The value is plotted in a Cartesian coordinate system (σ). m / σ b , σ w / σ -1 The data points are linearly fitted through the coordinates (0, 1) under the asymmetric axial load condition. The slope of the straight line is parameter C. Parameter C is directly substituted into formula (1). （1） ； (4) The predicted fatigue strength σ of the material under the predicted stress ratio R can be obtained by calculation according to formula (1). w .

2. The method for predicting fatigue strength of metallic materials under different stress ratios according to claim 1, characterized in that: In step (2), in order to improve the accuracy of the prediction results, 2 to 3 asymmetric axial loads can be selected for fatigue strength testing; 3. The method for predicting fatigue strength of metallic materials under different stress ratios according to claim 1, characterized in that: In step (3), if two or more asymmetric axial load conditions are selected for the test, the parameter C is obtained by linear fitting of the above coordinates through the (0, 1) coordinates.

4. The method for predicting fatigue strength of metallic materials under different stress ratios according to claim 1, characterized in that: This method is applicable to steel, aluminum alloys, magnesium alloys, or nickel-based high-temperature alloys; Suitable for various pre-deformation and heat treatment processes; It is suitable for different loading methods, cycle numbers, test temperatures, and stress states.

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