Method for rapidly predicting thermal mechanical fatigue life of metal material based on hysteresis energy density

Abstract

The invention relates to the technical field of material science and engineering application, in particular to a method for rapidly predicting the thermal mechanical fatigue life of a metal material based on hysteresis energy density. Firstly, the hysteresis energy density is calculated by utilizing a hysteresis curve, and then the aim of predicting the hysteresis energy density of the thermal mechanical fatigue under the same mechanical strain amplitude through the low-cycle fatigue is achieved by utilizing a quantitative relation among a shape coefficient between the low-cycle fatigue and the thermal mechanical fatigue, a stress change range and a plastic strain range. And finally, predicting the thermal mechanical fatigue life by using the energy accumulation damage model. According to the method, two groups of experiments are respectively carried out on the low-cycle fatigue and the thermal mechanical fatigue, the thermal mechanical fatigue life is predicted under different mechanical strain amplitudes through the low-cycle fatigue, and the low-cycle fatigue and the thermal mechanical fatigue are associated by utilizing the similarity of the low-cycle fatigue and the thermal mechanical fatigue in a cyclic loading process; the method effectively reduces the experimental quantity required for thermal mechanical fatigue life evaluation, and is applied to thermal mechanical fatigue life prediction of metal materials such as high-temperature alloy and vermicular graphite cast iron.

Description

Technical Field

[0001] The invention relates to the field of material science and engineering application technology, and in particular to a method for quickly predicting the thermomechanical fatigue life of metal materials based on hysteresis energy density. Background Art

[0002] Low-cycle fatigue and thermomechanical fatigue are both common failure forms of engineering components during service. The key difference between the two is that thermomechanical fatigue requires a temperature cycle with a specific interval to be applied on the basis of the load cycle. In the actual test process, in order to ensure the accuracy of the temperature cycle, the loading frequency must be reduced and a highly sensitive temperature control element must be introduced into the test platform, which greatly increases the time and economic cost. Therefore, the use of isothermal low-cycle fatigue data to quickly evaluate and predict the thermomechanical fatigue life of materials has great application potential.

[0003] At present, the classic fatigue life prediction models can be divided into two categories. One is the traditional Basquin model and the Coffin-Manson model. These models believe that the fatigue life of a material is mainly related to the stress amplitude or strain amplitude it bears. However, since there is usually an inverted relationship between the strength and plasticity of metal materials, the above models are not comprehensive when evaluating the fatigue performance of different materials. Due to the above problems, researchers have proposed an energy accumulation damage model that can simultaneously consider strength and plasticity, so that the fatigue damage that occurs to the material during cyclic loading can be comprehensively considered. However, how to establish a correlation between low-cycle fatigue and thermomechanical fatigue through the energy accumulation damage model and achieve the purpose of quickly predicting thermomechanical fatigue life requires further research. Summary of the invention

[0004] In order to solve the problem of high testing costs for thermomechanical fatigue performance evaluation, the purpose of the present invention is to provide a method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density. By obtaining the common influencing parameters between low-cycle fatigue and thermomechanical fatigue, the purpose of using low-cycle fatigue to predict thermomechanical fatigue life can be achieved. This method comprehensively considers the influence of stress and plastic deformation on material fatigue damage, effectively reducing the time and economic cost of traditional thermomechanical fatigue life testing, and also provides an optimization direction for the fatigue resistance design of metal materials.

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

[0006] A method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, the method specifically comprising the following steps:

[0007] (1) Select two different mechanical strain amplitudes to perform strain-controlled thermomechanical fatigue and isothermal low-cycle fatigue performance tests on metal materials at the corresponding peak temperature of thermomechanical fatigue, and obtain the corresponding hysteresis curves and fatigue life values;

[0008] (2) Select the mid-life hysteresis curve to obtain the corresponding stress variation range and plastic strain range, and obtain the hysteresis energy density and shape factor data by calculating the hysteresis curve area;

[0009] (3) The mid-life hysteresis energy density W s and fatigue life N f Perform linear fitting under double logarithmic coordinates to obtain corresponding fitting parameters;

[0010] (4) Using the low-cycle fatigue and thermomechanical fatigue plastic strain range Δε obtained in step (2) p Perform linear fitting to obtain corresponding fitting parameters m and n;

[0011] (5) Using the low-cycle fatigue plastic strain range Δε obtained in step (2) p , the stress variation range Δσ, the shape factor k and the fitting parameters m and n obtained in step (4) are used to calculate the thermomechanical fatigue hysteresis energy density under the same mechanical strain amplitude;

[0012] (6) Substitute the thermomechanical fatigue hysteresis energy density obtained in step (5) into step (3) to calculate the corresponding thermomechanical fatigue life.

[0013] In the method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, in step (1), strain-controlled thermomechanical fatigue includes low-cycle fatigue and thermomechanical fatigue, and the selected mechanical strain amplitude is not lower than the yield strain of the material under corresponding experimental conditions.

[0014] In the method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, in step (2), the area of ​​the hysteresis curve is not 0, the stress variation range is the difference between the maximum stress and the minimum stress, the plastic strain range is the distance between the intersection of the hysteresis loop and the strain axis when the stress is 0, and the shape factor is the ratio of the hysteresis energy density to the product of the stress variation range and the plastic strain range.

[0015] In the method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, in step (3), the linear fitting parameters under double logarithmic coordinates are W0 and β, respectively, and their expressions are:

[0016] W s =W0·N f -1 / β

[0017] Where W sRepresents the hysteresis energy density (MJ / m 3 ), W0 represents intrinsic fatigue toughness (MJ / m 3 ), β represents the fatigue damage conversion index (dimensionless parameter), N f Represents fatigue life (cycles).

[0018] In the method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, in step (4), the plastic strain range of low cycle fatigue and thermomechanical fatigue is linearly related, and the expression is:

[0019] Δε p-TMF =mΔε p-LCF +n

[0020] In the formula, Δε p-TMF represents the thermomechanical fatigue plastic strain range (%), Δε p-LCF represents the low cycle fatigue plastic strain range (%), and m and n are fitting constants.

[0021] In the method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, in step (5), the shape coefficient k and stress variation range Δσ obtained by the same metal material during high-temperature low-cycle fatigue (the experimental temperature is the thermomechanical fatigue peak temperature) and thermomechanical fatigue are basically the same, and an equivalent substitution is performed.

[0022] In the method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, in step (6), the thermomechanical fatigue hysteresis energy density W s-TMF The calculation formula is as follows:

[0023] W s-TMF =k LCF ·Δσ LCF ·(mΔε p-LCF +n)

[0024] Where W s-TMF is the thermomechanical fatigue hysteresis energy density (MJ / m 3 ), Δε p-LCF is the low cycle fatigue plastic strain range (%), k LCF Represents the shape factor of low cycle fatigue, Δσ LCF Represents the stress variation range of low cycle fatigue, parameter k LCF and Δσ LCF The parameters m and n are determined by linear fitting of the plastic strain range obtained from the thermomechanical fatigue and low-cycle fatigue tests.

[0025] The design idea of ​​the present invention is:

[0026] Traditional fatigue life prediction models are usually only applied to a single loading mode, and cannot establish a correlation between fatigue performance under different loading modes. The present invention uses the similarities between low-cycle fatigue and thermomechanical fatigue in the cyclic loading process to clarify the mutual transformation relationship between related parameters in the life prediction model, and finally establishes a correlation between low-cycle fatigue and thermomechanical fatigue life in combination with the energy accumulation damage model. This method only requires two sets of experiments for low-cycle fatigue and thermomechanical fatigue, and can predict thermomechanical fatigue life under different mechanical strain amplitudes through low-cycle fatigue.

[0027] The advantages and beneficial effects of the present invention are as follows:

[0028] 1. The present invention uses the similarity between low-cycle fatigue and thermo-mechanical fatigue in the cyclic loading process to accurately find three sets of mutual conversion parameters, and establishes the relationship between low-cycle fatigue and thermo-mechanical fatigue in combination with the energy accumulation damage model.

[0029] 2. The relevant fatigue life prediction parameters of the present invention can be obtained through low-cycle fatigue testing, which effectively reduces the amount of experiments required for thermomechanical fatigue performance evaluation and greatly reduces testing costs.

[0030] 3. The model of the present invention has high accuracy and universality, and is suitable for predicting the thermomechanical fatigue life of metal materials of different types and in different test environments under strain control. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of the material fatigue life prediction method.

[0032] Figure 2 It is a schematic diagram of the relevant model parameters in the hysteresis loop. In the figure, the horizontal axis ε is the strain and the vertical axis σ is the stress.

[0033] Figure 3 The linear relationship between the thermomechanical fatigue hysteresis energy and fatigue life of laser-selected melt-molded high-temperature alloy materials in double logarithmic coordinates. In the figure, the horizontal axis Number of cycles to failure is the fatigue life (N f ), the vertical axis Hysteretic energy is the hysteresis energy density (MJ / m 3 ), TMF-OP is the reverse phase thermomechanical fatigue test.

[0034] Figure 4 The equivalent transformation relationship between the shape factor k in low cycle fatigue and thermomechanical fatigue of laser selected melt-molded high temperature alloy materials. p is the plastic strain range, Δσ is the stress variation range; the ordinate Hystereticenergy is the hysteretic energy density (MJ / m 3 ).

[0035] Figure 5 The low-cycle fatigue stress variation range of the laser-selected melt-molded high-temperature alloy material gradually approaches the thermomechanical fatigue stress variation range as the experimental temperature increases. m / 2 is the mechanical strain amplitude (%), Δσ LCF is the low cycle fatigue stress variation range, Δσ TMF is the range of thermomechanical fatigue stress variation.

[0036] Figure 6 The linear relationship between the plastic strain range in low-cycle fatigue and thermomechanical fatigue of laser-selected melt-molded high-temperature alloy materials. p-TMF is the thermomechanical fatigue plastic strain range (%), the ordinate Δε p-LCF is the low cycle fatigue plastic strain range (%).

[0037] Figure 7 This is the result of predicting the thermomechanical fatigue life of low-cycle fatigue of high-temperature alloy materials by laser selection and melting. f Represents the true fatigue life (Exp), the ordinate N f Represents the predicted fatigue life (Cal).

[0038] Figure 8 The linear relationship between the thermomechanical fatigue hysteresis energy and life of the compacted graphite cast iron material in double logarithmic coordinates. In the figure, the horizontal axis Number of cycles to failure is the fatigue life (N f ), the vertical axis Hysteretic energy is the hysteresis energy density (MJ / m 3 ).

[0039] Fig. 9 is the equivalent conversion relationship between the shape factor k in low cycle fatigue and thermomechanical fatigue of vermicular graphite cast iron. p is the plastic strain range, Δσ is the stress variation range; the vertical coordinate Hysteretic energy is the hysteretic energy density (MJ / m 3 ).

[0040] Fig.10 is the equivalent transformation relationship between the low cycle fatigue stress variation range and the thermomechanical fatigue stress variation range of the vermicular graphite cast iron material. m / 2 is the mechanical strain amplitude, Δσ LCF is the low cycle fatigue stress variation range, Δσ TMF is the range of thermomechanical fatigue stress variation.

[0041] Fig.11is the linear relationship between the plastic strain range in low cycle fatigue and thermomechanical fatigue of compacted graphite cast iron. p-TMF is the thermomechanical fatigue plastic strain range, the ordinate Δε p-LCF It is the low cycle fatigue plastic strain range.

[0042] Fig.12 This is the result of predicting the thermomechanical fatigue life through low cycle fatigue of compacted graphite cast iron. f Represents the true fatigue life (Exp), the ordinate N f Represents the predicted fatigue life (Cal). DETAILED DESCRIPTION

[0043] In the specific implementation process, the present invention first uses the hysteresis curve to calculate the hysteresis energy density, and then uses the equivalent relationship of the shape coefficient and the equivalent conversion relationship between the stress change range and the plastic strain range between low-cycle fatigue and thermomechanical fatigue, so as to achieve the purpose of predicting the hysteresis energy density of thermomechanical fatigue by low-cycle fatigue. Finally, the linear relationship between the hysteresis energy density and the fatigue life in double logarithmic coordinates is combined to predict the thermomechanical fatigue life.

[0044] like Figure 1 As shown, the present invention proposes a method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, and the specific steps are as follows:

[0045] Step (1): Analyze the service conditions of the service component and determine the mechanical strain range of the component during service as well as parameters such as service temperature and strain rate.

[0046] Step (2): Select a metal material and perform strain-controlled isothermal low-cycle fatigue and thermomechanical fatigue performance tests at two mechanical strain amplitudes to obtain mid-life hysteresis loop data. The isothermal low-cycle fatigue test temperature is the peak temperature of the thermomechanical fatigue test, and the mid-life means half of the fatigue life.

[0047] Step (3): Calculate the stress variation range and plastic strain range through the obtained hysteresis curve, and obtain the hysteresis energy density and shape factor by calculating the area of ​​the hysteresis curve. Figure 2 shown.

[0048] Step (4): The hysteresis energy density of thermomechanical fatigue and fatigue life are linearly fitted in double logarithmic coordinates according to formula (1) to obtain linear fitting parameters W0 and β.

[0049] W s =W0·N f -1 / β (1)

[0050] Where Ws Represents the hysteresis energy density (MJ / m 3 ), W0 represents intrinsic fatigue toughness (MJ / m 3 ), β represents the fatigue damage conversion index (dimensionless parameter), N f Represents fatigue life (cycles).

[0051] Step (5): Linearly fit the hysteresis energy density with the stress variation range and the plastic strain range according to formula (2) to obtain the shape coefficient k.

[0052] W s = k·Δσ·Δε p (2)

[0053] Where W s Represents the hysteresis energy density (MJ / m 3 ), Δε p represents the plastic strain range, and Δσ represents the stress variation range.

[0054] Step (6): Linearly fit the plastic strain range of low cycle fatigue and thermomechanical fatigue according to formula (3):

[0055] Δε p-TMF =mΔε p-LCF +n (3)

[0056] In the formula, Δε p-TMF represents the thermomechanical fatigue plastic strain range; Δε p-LCF Represents the low cycle fatigue plastic strain range. m and n are fitting constants.

[0057] Step (7): Convert the thermomechanical fatigue hysteresis energy density W s-TMF Calculate according to formula (4):

[0058] W s-TMF =k LCF ·Δσ LCF ·(mΔε p-LCF +n) (4)

[0059] Where W s-TMF is the thermomechanical fatigue hysteresis energy density (MJ / m 3 ), Δε p-LCF is the low cycle fatigue plastic strain range (%), k LCF Represents the shape factor of low cycle fatigue, Δσ LCF Represents the stress variation range of low cycle fatigue (MPa), parameter k LCF and Δσ LCF The parameters m and n are determined by linear fitting of the plastic strain range obtained from the thermomechanical fatigue and low-cycle fatigue tests.

[0060] Step (8): The thermomechanical fatigue hysteresis energy W obtained in step (7) is s-TMF Substitute the data into formula (1) to calculate the thermomechanical fatigue life.

[0061] The present invention is further described below in conjunction with embodiments and drawings.

[0062] Embodiment 1:

[0063] like Figure 1 As shown, this embodiment predicts the inverse phase thermomechanical fatigue life of a high temperature alloy material, and includes the following steps:

[0064] Step 1: Use laser selected melting molding technology to prepare high-temperature alloy material (GH4169). According to the service conditions, isothermal low-cycle fatigue is carried out at 650°C, and the mechanical strain amplitude is selected as 0.4%, 0.6%, and 0.8%. The thermomechanical fatigue experiment is carried out at a temperature of 350-650°C, the phase is selected as the anti-phase (OP), and the mechanical strain amplitude is selected as 0.4%, 0.6%, and 0.8%.

[0065] Step 2: After the test, the mid-life hysteresis loop data is obtained. Figure 2 Calculate the hysteresis energy density under different mechanical strain amplitudes, establish a linear relationship between the hysteresis energy density and fatigue life in double logarithmic coordinates, and obtain the linear fitting parameters W0 and β (such as Figure 3 As shown). For the current experimental conditions, the thermomechanical fatigue test under the reverse phase, W0 = 12460.9, β = 0.8.

[0066] Step 3: Using the linear relationship between the hysteresis energy density and the product of the stress variation range and the plastic strain range (e.g. Figure 4 As shown), the shape factor k is obtained. For the current experimental conditions, k≈0.82 for isothermal low-cycle fatigue and thermomechanical fatigue.

[0067] Step 4: Replace the stress variation range of thermomechanical fatigue with the stress variation range of low-cycle fatigue at peak temperature (e.g. Figure 5 shown).

[0068] Step 5: Linearly fit the plastic strain range of low cycle fatigue and the plastic strain range of thermomechanical fatigue, such as Figure 6 As shown, the corresponding fitting parameters m and n are obtained, m=1.53, n=-0.13.

[0069] Step 6: Based on the experimental parameters obtained in steps 2, 3, 4, and 5, the thermomechanical fatigue life of the material can be predicted through low-cycle fatigue. Figure 7As shown, the relationship between the thermomechanical fatigue life prediction results and the test results verifies the accuracy of the prediction results.

[0070] Embodiment 2:

[0071] like Figure 1 As shown, this embodiment predicts the anti-phase thermomechanical fatigue life of vermicular graphite cast iron material (RuT450), including the following steps:

[0072] Step 1: According to the service conditions, isothermal low-cycle fatigue is carried out at 400°C, and the mechanical strain amplitude is selected as 0.15%, 0.2%, and 0.25%; the thermomechanical fatigue experiment is carried out at a temperature of 100-400°C, the phase is selected as the anti-phase (OP), and the mechanical strain amplitude is selected as 0.15%, 0.2%, and 0.25%.

[0073] Step 2: After the test, the mid-life hysteresis loop data is obtained. Figure 2 Calculate the hysteresis energy density under different mechanical strain amplitudes, establish a linear relationship between the hysteresis energy density and fatigue life in double logarithmic coordinates, and obtain the linear fitting parameters W0 and β (such as Figure 8 As shown in Figure 2 ). For the current experimental conditions, the anti-phase thermomechanical fatigue W0=22.5, β=1.38.

[0074] Step 3: Using the linear relationship between the hysteresis energy density and the product of the stress variation range and the plastic strain range (e.g. Fig. 9 As shown), the shape factor k is obtained; for the current experimental conditions, k≈0.78 for isothermal low-cycle fatigue and thermomechanical fatigue.

[0075] Step 4: Replace the stress variation range of thermomechanical fatigue by the stress variation range of low-cycle fatigue at the corresponding peak temperature, such as Fig.10 shown.

[0076] Step 5: Linearly fit the plastic strain range of low cycle fatigue and the plastic strain range of thermomechanical fatigue, such as Fig.11 As shown, the corresponding fitting parameters m and n are obtained, m=0.79, n=-0.0075.

[0077] Step 6: Based on the experimental parameters obtained in steps 2, 3, 4, and 5, the thermomechanical fatigue life of the material can be predicted through low-cycle fatigue. Fig.12 As shown, the relationship between the thermomechanical fatigue life prediction results and the test results verifies the accuracy of the prediction results.

[0078] The implementation results show that the method of the present invention establishes a correlation between low-cycle fatigue and thermomechanical fatigue by utilizing the similarities between the two during cyclic loading. The thermomechanical fatigue life of the corresponding material can be quickly predicted through low-cycle fatigue, which effectively reduces the amount of experiments required for thermomechanical fatigue life prediction and greatly reduces the cost of predicting and evaluating the thermomechanical fatigue performance of metal materials. It has high accuracy and can be widely used in strain-controlled thermomechanical fatigue life prediction of high-temperature alloys and heat-resistant metal materials such as turbine discs, turbine blades, internal combustion engine cylinder heads and pistons.

[0079] The above embodiments and comparative examples are only intended to illustrate the principles and performance of the present invention, and are not intended to be exhaustive. People can also obtain other embodiments based on the embodiments without creative work, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density, characterized in that: The method specifically comprises the following steps: (1) Select two different mechanical strain amplitudes to perform strain-controlled thermomechanical fatigue and isothermal low-cycle fatigue performance tests on metal materials at the corresponding peak temperature of thermomechanical fatigue, and obtain the corresponding hysteresis curves and fatigue life values; (2) Select the mid-life hysteresis curve to obtain the corresponding stress variation range and plastic strain range, and obtain the hysteresis energy density and shape factor data by calculating the hysteresis curve area; (3) The mid-life hysteresis energy density W s and fatigue life N f Perform linear fitting under double logarithmic coordinates to obtain corresponding fitting parameters; (4) Using the low-cycle fatigue and thermomechanical fatigue plastic strain range Δε obtained in step (2) p Perform linear fitting to obtain corresponding fitting parameters m and n; (5) Using the low-cycle fatigue plastic strain range Δε obtained in step (2) p , the stress variation range Δσ, the shape factor k and the fitting parameters m and n obtained in step (4) are used to calculate the thermomechanical fatigue hysteresis energy density under the same mechanical strain amplitude; (6) Substitute the thermomechanical fatigue hysteresis energy density obtained in step (5) into step (3) to calculate the corresponding thermomechanical fatigue life.

2. The method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density according to claim 1 is characterized in that: In step (1), strain-controlled thermomechanical fatigue includes low-cycle fatigue and thermomechanical fatigue, and the selected mechanical strain amplitude is not lower than the yield strain of the material under corresponding experimental conditions.

3. The method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density according to claim 1 is characterized in that: In step (2), the area of ​​the hysteresis curve is not 0, the stress variation range is the difference between the maximum stress and the minimum stress, the plastic strain range is the distance between the intersection of the hysteresis loop and the strain axis when the stress is 0, and the shape factor is the ratio of the hysteresis energy density to the product of the stress variation range and the plastic strain range.

4. The method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density according to claim 1 is characterized in that: In step (3), the linear fitting parameters under double logarithmic coordinates are W0 and β, and their expressions are: W s =W0·N f -1 / β Where W s Represents the hysteresis energy density (MJ / m 3 ), W0 represents intrinsic fatigue toughness (MJ / m 3 ), β represents the fatigue damage conversion index (dimensionless parameter), N f Represents fatigue life (cycles).

5. The method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density according to claim 1 is characterized in that: In step (4), the plastic strain range of low cycle fatigue and thermomechanical fatigue is linearly related, and the expression is: No p-TMF =mNo p-LCF +n In the formula, Δε p-TMF represents the thermomechanical fatigue plastic strain range (%), Δε p-LCF represents the low cycle fatigue plastic strain range (%), and m and n are fitting constants.

6. The method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density according to claim 1 is characterized in that: In step (5), the shape coefficient k and stress variation range Δσ obtained by the same metal material during high temperature low cycle fatigue (the experimental temperature is the peak temperature of thermomechanical fatigue) and thermomechanical fatigue are basically the same, and an equivalent replacement is performed.

7. The method for rapid prediction of thermomechanical fatigue life of metal materials based on hysteresis energy density according to claim 1 is characterized in that: In step (6), the thermomechanical fatigue hysteresis energy density W s-TMF The calculation formula is as follows: W s-TMF =k LCF ·Board LCF ·(mNo p-LCF +n) Where W s-TMF is the thermomechanical fatigue hysteresis energy density (MJ / m 3 ), Δε p-LCF is the low cycle fatigue plastic strain range (%), k LCF Represents the shape factor of low cycle fatigue, Δσ LCF Represents the stress variation range of low cycle fatigue, parameter k LCF and Δσ LCF The parameters m and n are determined by linear fitting of the plastic strain range obtained from the thermomechanical fatigue and low-cycle fatigue tests.

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