Fatigue prediction method of high-temperature alloy considering temperature change

Abstract

The invention relates to a high-temperature alloy fatigue prediction method considering temperature change, which comprises the following steps: S1, establishing a fatigue life prediction model which comprehensively considers various factors such as temperature, stress and strain; s2, performing a material fatigue life test to obtain low-cycle fatigue test data under different temperature conditions; s3, performing regression analysis on the test data, and determining a material fatigue life calculation coefficient in the model; s4, verifying the generalization of the model, and ensuring the accuracy of a prediction result; and S5, calculating the fatigue life of the high-temperature alloy under the actual working condition by using the verified model. The device is compact and reasonable in structure and convenient to operate, and accurate prediction of the fatigue life of the high-temperature alloy is realized by establishing a scientific and comprehensive prediction model and adopting a method of combining an advanced simulation technology and test data regression analysis. The innovative method not only improves the accuracy and stability of prediction, but also greatly expands the application range and practicability of the prediction method.

Description

Technical Field

[0001] The invention relates to the technical field of fatigue prediction methods, in particular to a fatigue prediction method for high-temperature alloys taking temperature changes into consideration. Background Art

[0002] In the field of materials science and engineering, the prediction of fatigue life has always been one of the core research issues. Its complexity lies in the fact that there are many factors that affect the fatigue life of materials and they are intertwined, including stress, strain, temperature, environment, etc. These factors are often difficult to accurately quantify through simple mathematical models. Traditional fatigue life analysis methods, such as nominal stress method, local stress-strain method and stress field intensity method, have certain prediction capabilities under specific conditions, but generally have significant limitations. Specifically, most of these methods are only applicable to fatigue prediction at a specific temperature point. When there is a lack of fatigue data corresponding to the temperature of the danger point, the prediction error increases significantly, and it is difficult to accurately assess the error range. In addition, the scope of application of these methods is relatively narrow. Many models are only applicable to specific cases where the stress ratio or strain ratio R=-1, or the uniaxial tension state, such as the widely used Manson-Coffin model. When faced with complex and changeable actual working conditions, its prediction ability seems to be inadequate, especially for fatigue prediction under temperature changes.

[0003] In actual engineering applications, high-temperature alloy materials are widely used in key fields such as aviation, aerospace, and energy due to their excellent high-temperature resistance. However, the fatigue behavior of materials in high-temperature environments is more complicated. Temperature changes not only directly affect the mechanical properties of materials, but also interact with factors such as stress and strain to jointly affect the fatigue life of materials. Therefore, how to accurately predict the fatigue life of high-temperature alloys under temperature changes has become a technical problem that needs to be solved urgently. Solving this problem is of great significance for improving the safety of engineering structures, optimizing material selection, and reducing maintenance costs. In view of this, it is urgent to develop a fatigue prediction method that can comprehensively consider multiple factors such as temperature, stress, and strain, which is suitable for high-temperature alloy materials under different temperature change conditions, so as to overcome the limitations of existing technologies and improve the accuracy and reliability of fatigue life prediction.

[0004] To this end, we propose a fatigue prediction method for high-temperature alloys taking temperature changes into account. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned existing production technologies, the applicant provides a fatigue prediction method for high-temperature alloys that takes temperature changes into consideration, establishes a scientific and comprehensive prediction model, and adopts a method that combines advanced simulation technology and test data regression analysis to achieve accurate prediction of the fatigue life of high-temperature alloys.

[0006] The technical solution adopted by the present invention is as follows: 1. A fatigue prediction method for high temperature alloys considering temperature changes, characterized in that it includes: S1. Establish a fatigue life prediction model that comprehensively considers multiple factors such as temperature, stress, and strain; S2. Conduct material fatigue life tests and obtain low-cycle fatigue test data under different temperature conditions; S3. Perform regression analysis on the test data to determine the material fatigue life calculation coefficient in the model; S4. Verify the generalization of the model and ensure the accuracy of the prediction results; S5. Use the verified model to calculate the fatigue life of high-temperature alloys under actual working conditions.

[0007] In one embodiment, the model equation in S1 is: (1); It can also be transformed into: (2) Where: —Material fatigue life calculation coefficient; — Fatigue life calculation factor related to maximum stress; — Fatigue life calculation factor related to total strain; — Fatigue life calculation coefficient related to plastic strain; — Fatigue life calculation coefficient related to working temperature; —Low cycle fatigue life; —Maximum stress at the prediction point, unit: MPa; —Total strain range of the prediction point, unit: mm / mm; —Plastic strain range of prediction point, unit: mm / mm; —Working temperature at the predicted point, in K.

[0008] In one of the embodiments, the test data regression analysis uses advanced techniques such as multivariate linear regression and nonlinear regression to conduct in-depth analysis of the test data, extract model parameters, and evaluate the accuracy and stability of the model.

[0009] In one embodiment, the model generalization verification includes substituting the remaining test data into the model for prediction, and comparing it with the actual test data to evaluate the dispersion and prediction error of the model.

[0010] In one of the embodiments, in the step of calculating fatigue life, finite element simulation software is used to simulate and analyze the high-temperature alloy structure, obtain the temperature, stress and strain parameters of each node in the whole field, and substitute them into the prediction model to calculate the fatigue life.

[0011] In one of the embodiments, multiple groups of low-cycle fatigue tests are performed under different temperature conditions to obtain more comprehensive test data for verifying the accuracy and generalization ability of the model, and for further optimizing and improving the model.

[0012] In one embodiment, when a low-cycle fatigue life analysis is required for a structure at a specific temperature, the model equation is degraded to simplify the calculation process while retaining the effects of stress, strain and other factors on fatigue life.

[0013] In one embodiment, the model expression after the degradation process is: (3).

[0014] In one of the embodiments, according to engineering needs, the maximum stress, maximum strain, maximum plastic strain and other data of the global or dangerous points in the simulation analysis results are extracted, substituted into the model equation, and the fatigue life times are calculated to provide a scientific basis for engineering design and material selection.

[0015] The beneficial effects of the present invention are as follows: The present invention has a compact and reasonable structure and is easy to operate. It achieves accurate prediction of the fatigue life of high-temperature alloys by establishing a scientific and comprehensive prediction model and adopting a method combining advanced simulation technology and test data regression analysis. This innovative method not only improves the accuracy and stability of the prediction, but also greatly expands the scope of application and practicality of the prediction method, provides a scientific basis for engineering design and material selection, and is of great significance for improving the safety of engineering structures, optimizing material selection, and reducing maintenance costs. At the same time, the research and application of this method has also made important contributions to the development of the field of high-temperature alloy fatigue prediction and promoted the progress and development of related technologies.

[0016] At the same time, the present invention also has the following advantages: The present invention proposes a new method for fatigue prediction of high-temperature alloys that takes temperature changes into consideration, which effectively solves the problem that traditional fatigue life analysis methods have insufficient prediction capabilities under temperature change conditions. By introducing temperature as a key variable and comprehensively considering multiple factors such as stress and strain, this method can more accurately evaluate the fatigue life of high-temperature alloys under actual working conditions. This not only improves the accuracy of the prediction, but also greatly expands the scope of application of the prediction method, enabling it to cope with complex and changeable actual engineering environments. This innovation is of great significance for improving the safety of engineering structures and optimizing material selection, especially in key fields such as aviation, aerospace, and energy, and can ensure the stability and reliability of structures under extreme temperature conditions.

[0017] The present invention provides a solid theoretical basis for fatigue prediction by establishing a scientific and comprehensive fatigue life prediction model. Each parameter in the model is obtained through strict test data regression analysis, ensuring the accuracy and reliability of the prediction results. At the same time, the model also has good scalability, and influencing factors can be added or adjusted according to actual needs to meet the prediction requirements of different materials and working conditions. This flexibility and versatility make the method more adaptable and practical in practical applications, and can provide strong support for engineering design and material selection.

[0018] The present invention adopts a method that combines advanced finite element simulation technology and test data regression analysis to achieve accurate prediction of the fatigue life of high-temperature alloys. The temperature, stress, and strain parameters of each node in the whole field are obtained through finite element simulation, and then substituted into the prediction model to calculate the number of fatigue life. This combination makes the prediction results closer to the actual working conditions. At the same time, the model parameters are optimized and verified through test data regression analysis, which further improves the accuracy and stability of the prediction. This method of comprehensive application of multiple technical means provides new ideas and methodology for the research in the field of fatigue prediction of high-temperature alloys. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a working principle diagram of the present invention.

[0020] Figure 2 The test data is the relationship between the maximum stress and fatigue life of a certain high-temperature alloy.

[0021] Figure 3 The test data is the relationship between the total strain and fatigue life of a certain high-temperature alloy.

[0022] Figure 4 The test data is the relationship between plastic strain and fatigue life of a certain high-temperature alloy.

[0023] Figure 5 The calculation data is the fatigue calculation model coefficient of a certain high-temperature alloy.

[0024] Figure 6 The comparative data is the fatigue life prediction dispersion zone of a certain high temperature alloy according to the present invention.

[0025] Figure 7 The comparative data is a comparison of the predictions of the fatigue life model of the present invention and other classical models for a certain high-temperature alloy. DETAILED DESCRIPTION

[0026] The specific implementation of the present invention will be described below in conjunction with the accompanying drawings.

[0027] like Figure 1 As shown, this embodiment discloses a fatigue prediction method for high-temperature alloys taking temperature changes into consideration. This method can accurately predict the fatigue of alloy materials according to the temperature changes in actual working conditions, and provide a scientific basis for engineering design and material selection. The method mainly includes the following core steps: model establishment, material fatigue life test, test data regression analysis, model generalization verification, and fatigue life calculation.

[0028] S1. Establish fatigue life prediction model In order to achieve the purpose of the present invention, that is, to provide a prediction method that can comprehensively consider the influence of multiple factors such as temperature, stress, strain, etc. on the fatigue life of high-temperature alloys, we first established a scientific and comprehensive fatigue life prediction model. This model not only considers the traditional stress and strain factors, but also innovatively introduces the key variable of temperature, making the prediction results closer to the actual working conditions.

[0029] The model equation is as follows: (1) It can also be transformed into: (2) Where: —Material fatigue life calculation coefficient; — Fatigue life calculation factor related to maximum stress; — Fatigue life calculation factor related to total strain; — Fatigue life calculation coefficient related to plastic strain; — Fatigue life calculation coefficient related to working temperature; —Low cycle fatigue life; —Maximum stress at the prediction point, unit: MPa; —Total strain range of the prediction point, unit: mm / mm; —Plastic strain range of prediction point, unit: mm / mm; —Working temperature at the predicted point, in K.

[0030] The establishment of this model provides a solid theoretical basis for subsequent fatigue prediction. By comprehensively considering multiple factors such as temperature, stress, strain, etc., we can more accurately evaluate the fatigue life of high-temperature alloys under actual working conditions, providing strong support for engineering design and material selection. In addition, the model has good scalability and can add or adjust influencing factors according to actual needs to meet the prediction needs of different materials and working conditions. At the same time, each parameter in the model is obtained through rigorous test data regression analysis, ensuring the accuracy and reliability of the prediction results.

[0031] S2. Material fatigue life test In order to obtain the test data required for the model, we conducted comprehensive material fatigue life tests. These tests were low-cycle fatigue tests on high-temperature alloy materials under different temperature conditions to simulate the impact of temperature changes on material fatigue life in actual working conditions.

[0032] The material fatigue test in this embodiment is based on the above model to obtain at least three groups of low cycle fatigue life data at different temperature points.

[0033] During the test, we strictly controlled the test conditions, including temperature, stress level, strain range, etc., to ensure the accuracy and reliability of the test data. By changing these test parameters, we obtained multiple sets of low-cycle fatigue life data at different temperature points, providing rich data support for subsequent model parameter regression.

[0034] During the test, we also used advanced testing equipment and technology, such as high-precision stress-strain measurement system, temperature control system, etc., to ensure the accuracy and repeatability of the test results. At the same time, we also strictly screened and processed the test data, eliminated abnormal data and invalid data, and ensured the quality and reliability of the data. The acquisition of these test data provides a solid foundation for the establishment and verification of the model.

[0035] S3. Regression analysis of test data After obtaining abundant test data, we conducted regression analysis of the test data. The purpose of this step is to extract the parameters required for the model from the test data through mathematical methods. ~ Five unknown coefficients are used to obtain a full-parameter low-cycle fatigue life prediction model.

[0036] We used advanced regression analysis techniques, such as multivariate linear regression and nonlinear regression, to conduct in-depth analysis and processing of the test data. By continuously adjusting and optimizing the model parameters, we finally obtained a full-parameter low-cycle fatigue life prediction model. These parameters not only reflect the individual effects of factors such as temperature, stress, and strain on the fatigue life of the material, but also reflect the interaction and coupling effects between them.

[0037] During the regression analysis, we also conducted a rigorous evaluation of the accuracy and stability of the model. By comparing the test data with the prediction data, we verified the prediction and generalization capabilities of the model. At the same time, we also conducted a sensitivity analysis of the model parameters to understand the impact of each parameter on the prediction results. These analyses provide strong support for the optimization and improvement of the model.

[0038] The test data regression analysis in this embodiment is based on part of the test data, and the model equation (2) is subjected to regression analysis to obtain ~ Five unknown coefficients are used to obtain a full-parameter low-cycle fatigue life prediction model.

[0039] S4. Model generalization verification To ensure the generalization ability and prediction accuracy of the model, we performed a generalization validation of the model. The purpose of this step is to validate the model with the remaining data to assess the dispersion and prediction error of the model.

[0040] We substituted the remaining test data into the established fatigue life prediction model and calculated the prediction results. Then, we compared and analyzed the prediction results with the actual test data. By drawing a comparison chart of the test data and the prediction data, we can intuitively see the prediction effect of the model.

[0041] The verification results show that the prediction results obtained by the present invention are all within the 2-fold error band, and most of the results are within the 1.5-fold error band. This fully demonstrates that the present invention can well predict the fatigue life of high-temperature alloys at different temperatures. At the same time, we also evaluated the stability of the model to ensure that the model can maintain stable prediction performance under different working conditions. These verification results provide strong evidence for the reliability and practicality of the model.

[0042] The generalization of the model in this embodiment is verified by substituting the remaining data into the fatigue life calculation model to verify the dispersion of the model.

[0043] S5. Calculate fatigue life After completing the establishment and verification of the model, we can use this model to calculate the fatigue life of high-temperature alloys under actual working conditions. The purpose of this step is to obtain the temperature, stress, and strain parameters of each node (or dangerous point) in the field through finite element simulation and other technical means, and then substitute them into the prediction model to calculate the fatigue life times.

[0044] We used advanced finite element simulation software to conduct a comprehensive simulation analysis of the high-temperature alloy structure. By simulating the load and temperature conditions under actual working conditions, we obtained the temperature, stress and strain distribution of each node in the field. Then, we substituted these parameters into the prediction model and calculated the fatigue life times of each node.

[0045] The life prediction in this embodiment is based on finite element simulation, etc., to obtain the temperature, stress and strain parameters of each node (or dangerous point) in the whole field, and substitute them into the prediction model equation (1) to obtain the low cycle fatigue life.

[0046] Furthermore, the low-cycle fatigue life prediction of the full temperature field is established, and low-cycle fatigue tests are carried out on materials under different temperature conditions according to engineering needs. These test data can not only be used to verify the accuracy and generalization ability of the model, but also can be used to further optimize and improve the model.

[0047] We conducted multiple sets of low-cycle fatigue tests at different temperature points and obtained a wealth of test data. We then conducted in-depth analysis and processing of these data and obtained the relationship between the material fatigue life and the maximum stress, total strain range, plastic strain range, and operating temperature.

[0048] Get test data, such as Figure 2 , Figure 3 , Figure 4 The relationship between the fatigue life of a certain high-temperature alloy obtained from the experiment and the maximum stress, total strain range, and plastic strain range. Through these relationship diagrams, we can intuitively see the influence trends and laws of various factors on the fatigue life of the material. These laws not only help us to understand the fatigue mechanism of the material more deeply, but also provide strong support for the optimization and improvement of the model. At the same time, we also conducted in-depth mining and analysis on these relationship diagrams and extracted more valuable information and laws.

[0049] Select the test data points as regression samples, screen and analyze the test data. According to formula (1), the above five necessary fatigue calculation parameters are obtained by regression, such as Figure 5 shown.

[0050] The results need to be verified, and a comparison chart of the test and calculated data should be drawn based on the test data and calculated data. Figure 6As shown, the results obtained by the present invention are all within a 2-fold error band, and most of the results are within a 1.5-fold error band. Therefore, the present invention can well predict the fatigue life of high-temperature alloys at different temperatures. The prediction results obtained by the present invention are all within a small error range, indicating that the model has good predictive and generalization capabilities.

[0051] According to the needs of the project, simulation analysis is carried out, and the maximum stress, maximum strain, maximum plastic strain and other data of the global or dangerous point in the analysis results are extracted and substituted into the obtained fatigue life calculation model to obtain the fatigue life times.

[0052] Fatigue life prediction at a certain temperature point When a low cycle fatigue life analysis of a structure at a specific temperature is required, the model equation is: (1) We can degenerate the model equation. In this case, the temperature-related terms in the model equation can be regarded as constants, thus simplifying the calculation process.

[0053] The model equation can be degenerated into: (3) At this time, the low-cycle fatigue test data under a certain temperature condition is analyzed and the four necessary fatigue calculation parameters mentioned above are obtained by regression.

[0054] Degradation processing not only simplifies the calculation process, but also improves the calculation efficiency and accuracy of the model. At the same time, the degraded model still retains the influence of factors such as stress and strain on fatigue life, ensuring the accuracy and reliability of the prediction results. This processing method provides strong support for fatigue prediction at specific temperatures.

[0055] like Figure 2 , Figure 3 , Figure 4 The relationship between the fatigue life of a certain high-temperature alloy and the maximum stress, total strain range and plastic strain range obtained from the experiment is shown in Figure 2.

[0056] The results were verified, and a comparison chart of other classical models and the model of the present invention was drawn based on the test data and calculation data. Figure 7 As shown in the figure, the results obtained by the present invention are significantly better than those of the MC model and the SWT model. Therefore, the present invention can well predict the fatigue life of high-temperature alloys at a given temperature. These comparative results fully demonstrate that the present invention has significant advantages in predicting the fatigue life of high-temperature alloys at a given temperature. At the same time, we have also conducted in-depth mining and analysis of the prediction results and extracted more valuable information and laws. These information and laws provide strong support for engineering design and material selection.

[0057] According to the needs of the project, simulation analysis is carried out to extract the maximum stress, maximum strain, maximum plastic strain and other data of the global or dangerous point in the analysis results, and substitute them into the degraded model equation (3) to obtain the fatigue life times. These calculation results can provide a scientific basis for engineering design and material selection to ensure the safety and reliability of the structure at a specific temperature.

[0058] The above description is an explanation of the present invention, not a limitation of the present invention. The scope of the present invention is defined in the claims. Any form of modification may be made within the scope of protection of the present invention.

Claims

1. A fatigue prediction method for high temperature alloys taking temperature changes into consideration, characterized in that: include: S1. Establish a fatigue life prediction model that comprehensively considers multiple factors such as temperature, stress, and strain; S2. Conduct material fatigue life tests and obtain low-cycle fatigue test data under different temperature conditions; S3. Perform regression analysis on the test data to determine the material fatigue life calculation coefficient in the model; S4. Verify the generalization of the model and ensure the accuracy of the prediction results; S5. Use the verified model to calculate the fatigue life of high-temperature alloys under actual working conditions.

2. The fatigue prediction method according to claim 1, characterized in that: The model equation in S1 is: （1）； It can also be transformed into: （2） Where: —Material fatigue life calculation coefficient; — Fatigue life calculation factor related to maximum stress; — Fatigue life calculation factor related to total strain; — Fatigue life calculation coefficient related to plastic strain; — Fatigue life calculation coefficient related to working temperature; —Low cycle fatigue life; —Maximum stress at the prediction point, unit: MPa; —Total strain range of the prediction point, unit: mm / mm; —Plastic strain range of prediction point, unit: mm / mm; —Working temperature at the predicted point, in K.

3. The fatigue prediction method according to claim 1, characterized in that: The test data regression analysis uses advanced techniques such as multivariate linear regression and nonlinear regression to conduct in-depth analysis of the test data, extract model parameters, and evaluate the accuracy and stability of the model.

4. The fatigue prediction method according to claim 1, characterized in that: The model generalization verification includes substituting the remaining test data into the model for prediction, and comparing with the actual test data to evaluate the dispersion and prediction error of the model.

5. The fatigue prediction method according to claim 1, characterized in that: In the step of calculating fatigue life, finite element simulation software is used to perform simulation analysis on the high-temperature alloy structure.

6. The fatigue prediction method according to claim 5, characterized in that: The simulation analysis obtains the temperature, stress and strain parameters of each node in the whole field, and substitutes them into the prediction model to calculate the fatigue life.

7. The fatigue prediction method according to claim 1, characterized in that: It also includes conducting multiple sets of low-cycle fatigue tests under different temperature conditions to obtain more comprehensive test data to verify the accuracy and generalization ability of the model, as well as to further optimize and improve the model.

8. The fatigue prediction method according to claim 1, characterized in that: When it is necessary to perform low-cycle fatigue life analysis on a structure at a specific temperature, the model equation is degenerated to simplify the calculation process while retaining the effects of stress, strain and other factors on fatigue life.

9. The fatigue prediction method according to claim 8, characterized in that: The model expression after the degradation process is: （3）。 10. The fatigue prediction method according to any one of claims 1 to 9, characterized in that: It also includes extracting global or dangerous point maximum stress, maximum strain, maximum plastic strain and other data from the simulation analysis results according to engineering needs, substituting them into the model equation, and calculating the fatigue life times to provide a scientific basis for engineering design and material selection.

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