A method for predicting fatigue correction factors in metallic environments that takes surface roughness into account

Abstract

This invention discloses a method for predicting fatigue correction factors in metallic environments that considers surface roughness. The method includes: acquiring the surface roughness of a processed sample using a laser confocal microscope; selecting an appropriate surface roughness measurement method and results; conducting fatigue tests on austenitic stainless steel materials after controlling several factors among four factors: sample surface roughness, loading strain rate, loading strain amplitude, and experimental environment, to study the influence of each factor on the fatigue life of austenitic stainless steel; obtaining the best-fit curves for fatigue tests in air and high-temperature, high-pressure water environments for both environments; and obtaining a calculation model for the environmental fatigue correction factor by introducing a surface roughness factor, thereby predicting the fatigue life of austenitic stainless steel materials in coolant environments. This invention considers the combined influence of multiple factors and reduces costs while ensuring accuracy.

Description

Technical Field

[0001] This patent relates to the field of environmental fatigue testing and numerical prediction technology for metallic materials, specifically to a method for predicting environmental fatigue correction factors for metallic materials that takes into account surface roughness. Background Technology

[0002] When austenitic stainless steel is used in high-temperature, high-pressure water, it may experience pitting corrosion, intergranular corrosion, and fatigue-induced cracking (EAC) failures (such as SCC and EAF). SCC refers to the cracking phenomenon that occurs in metallic materials under tensile stress and specific corrosive media conditions, while EAF refers to fatigue cracking that occurs under cyclic loading and specific corrosive media conditions; both are essentially EAC processes. Current experiments have shown that the fatigue life of austenitic stainless steel in high-temperature, high-pressure water environments is significantly reduced compared to air environments, but the specific reasons remain unknown. To address this issue, numerous researchers have conducted related studies and proposed various failure mechanisms, but a universally accepted explanation has not yet been reached, and considerable controversy remains. Due to the lack of direct experimental evidence on the microscopic initiation and propagation processes of cracks, it is currently difficult to determine which mechanism controls environmental fatigue damage.

[0003] Based on limited fatigue test results, an environmental fatigue life prediction model considering the simultaneous effects of multiple influencing factors was proposed, which reduced costs while ensuring accuracy. The influence of typical environmental factors such as strain amplitude, loading rate, temperature and roughness on the fatigue behavior of austenitic stainless steel was discussed, which can provide guidance for revealing the environmental fatigue mechanism and structural reliability design of austenitic stainless steel. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting environmental fatigue correction factors of metals that considers surface roughness. This method enables the prediction of environmental fatigue correction factors of austenitic stainless steel under the coupled effects of surface roughness, loading strain rate, loading strain amplitude, and experimental environment, thereby obtaining the environmental fatigue design curve of austenitic stainless steel and providing a basis for fatigue analysis in engineering structural design.

[0005] The technical solution of the present invention is as follows: A method for predicting fatigue correction factors in metallic environments considering surface roughness, comprising the following steps:

[0006] S1: Obtain the surface roughness of the machined austenitic stainless steel sample. The surface roughness is measured using the index R. a To represent, the expression is:

[0007]

[0008] Where n is the number of segments, Z i i is the roughness value of the i-th segment, l rTo represent the length of the region;

[0009] S2: Under air environment, considering the surface roughness, loading strain rate and loading strain amplitude of the austenitic stainless steel sample, fatigue test is carried out under air environment to obtain the results.

[0010] S3: Fatigue tests were conducted in a high-temperature and high-pressure water environment, taking into account factors such as the surface roughness, loading strain rate and loading strain amplitude of austenitic stainless steel specimens.

[0011] S4: Based on the fatigue test data obtained in steps S2 and S3, analyze the effects of surface roughness, loading strain rate, and loading strain amplitude on the fatigue life of austenitic stainless steel specimens, and use the Langer equation to fit the fitting curves of austenitic stainless steel specimens under air and high temperature and high pressure water conditions. The expression is:

[0012]

[0013] Where, ε a Where is the strain amplitude, N is the fatigue life, and A1, A2, and n1 are fitting parameters;

[0014] S5: Analyze the degree of agreement between the fitting results in S4 and the experimental data;

[0015] S6: Define the environmental fatigue correction factor, expressed as follows:

[0016]

[0017] Where, N RTair and N water These represent fatigue life in a room temperature air environment and fatigue life in a high temperature and high pressure water environment, respectively.

[0018] S7: To eliminate the influence of roughness in an air environment, the fitting curve of austenitic stainless steel processed samples in an air environment is corrected:

[0019]

[0020] Among them, A en Surface roughness correction factor

[0021] S8: Considering the effects of temperature, dissolved oxygen content, strain rate, and surface roughness on environmental fatigue life, based on formulas (2), (4), and (5), the relationships between the loading strain rate, temperature, dissolved oxygen content, surface roughness, and environmental fatigue correction factor are established respectively.

[0022]

[0023] ln(F en-T)=f(T)

[0024] ln(F en-DO )=f(DO)

[0025] ln(F en-Ra )=f(R a )

[0026] S9: Joint Formula The calculated expression for the environmental fatigue correction factor Fen of austenitic stainless steel, taking into account surface roughness, is as follows:

[0027]

[0028] Where T * O * , and R a * These parameters respectively reflect the effects of temperature, dissolved oxygen content, strain rate, and surface roughness on environmental fatigue life.

[0029] In step S1, the surface roughness of the austenitic stainless steel sample is obtained using a laser confocal microscope.

[0030] In S1, four surface roughnesses are selected, namely R a =0.2μm, 1.2μm, 6.5μm, 12.5μm.

[0031] In step S1, the X-shaped line sampling method is used to measure each roughness 5 times, and the average value of the measurement results is taken.

[0032] S2 includes:

[0033] S2.1: By controlling the surface roughness, strain rate, and air environment of the austenitic stainless steel sample to be constant, the relationship between different strain amplitudes and the fatigue life of the austenitic stainless steel sample was obtained.

[0034] S2.2: By controlling the surface roughness, strain amplitude, and air environment of the austenitic stainless steel sample, the relationship between different strain rates and the fatigue life of the austenitic stainless steel sample was obtained.

[0035] S2.3: By controlling the loading strain rate, loading strain amplitude, and air environment of the specimens and keeping them constant, the relationship between different surface roughness and the fatigue life of the processed stainless steel specimens was obtained.

[0036] The strain amplitudes were selected as 0.4%, 0.5%, 0.6%, 0.7%, and 0.8%.

[0037] The strain rates were selected as 0.02% / s, 0.04% / s, 0.06% / s, 0.08% / s, and 0.1% / s.

[0038] The ambient temperature was selected as 25℃ and 325℃.

[0039] S3 includes:

[0040] S3.1: By controlling the surface roughness, loading strain rate, and high temperature and high pressure water environment of the austenitic stainless steel processed specimens, the relationship between different strain amplitudes and the fatigue life of the austenitic stainless steel processed specimens was obtained.

[0041] S3.2: By controlling the surface roughness, strain amplitude, and high temperature and high pressure water environment of the austenitic stainless steel processed specimens, the relationship between different strain rates and the fatigue life of the austenitic stainless steel processed specimens was obtained.

[0042] S3.3: By controlling the loading strain rate, loading strain amplitude, and maintaining a constant high-temperature and high-pressure water environment for the specimens, the relationship between different surface roughness and the fatigue life of the processed martensitic stainless steel specimens was obtained.

[0043] In step S3.1, the strain amplitude is selected as 0.4%, 0.5%, 0.6%, 0.7%, or 0.8%.

[0044] In S3.2, the strain rate is selected as 0.02% / s, 0.04% / s, 0.06% / s, 0.08% / s, and 0.1% / s.

[0045] In S2, the high-temperature and high-pressure water environment is 325℃, 15.5MPa, and 90±2ppb dissolved oxygen.

[0046] In S4, under air conditions, the fitting parameters are A1 = 28.0, A2 = 0.112 and n1 = -0.5235.

[0047] In S4, the fitting parameters under high temperature and high pressure water environment are A1 = 18.6136, A2 = 0.0722 and n1 = -0.4783.

[0048] In S5, the coefficient of determination R from regression analysis is introduced. 2 The expression is used to evaluate the degree of agreement between the fitting results and the experimental data.

[0049]

[0050] Among them, y i Let i be the experimental value of the i-th sample. The mean of the experimental values. These are the predicted values ​​from the model.

[0051] In S7, the surface roughness R a For surface roughness values ​​of 0.2μm, 1.2μm, 6.5μm, and 12.5μm, the surface roughness correction factor A... en The values ​​were 1.164, 1, 0.448, and 0.181, respectively.

[0052] In S6, F en The value ranges from 3.679 to 4.042.

[0053] In S9, C = 0.861845.

[0054] S9 specifically includes:

[0055] S9.1: The strain rate range for high-temperature and high-pressure water environment tests and air environment tests is 0.02% / s to 0.2% / s. When the strain rate is below 0.0004% / s or above 7% / s, F en Set to a fixed value, that is

[0056]

[0057] S9.2: F at temperatures below 100℃ en F is set to 1 when the temperature is above 325℃. en Set to a fixed value, that is

[0058]

[0059] S9.3: The upper limit of surface roughness is 12.5 μm, and the lower limit of surface roughness is 0.02 μm, that is...

[0060]

[0061] S9.4: When the dissolved oxygen content (DO) ≤ 100 ppb, O * =0.29.

[0062] The significant advantage of this invention is that by introducing a surface roughness factor to obtain a calculation model for the environmental fatigue correction factor, the fatigue life of austenitic stainless steel materials under high temperature and high pressure water environment can be predicted, which can provide guidance for revealing the environmental fatigue mechanism of austenitic stainless steel and designing structural reliability. Attached Figure Description

[0063] Figure 1 Schematic diagram of the prediction process for environmental fatigue correction factors of austenitic stainless steel considering surface roughness

[0064] Figure 2 Schematic diagram of surface roughness characterization

[0065] Figure 3 Schematic diagram of surface roughness sampling using an "X"-shaped line.

[0066] Figure 4 Fatigue life-strain amplitude distribution curve of austenitic stainless steel in air environment

[0067] Figure 5 Fatigue life-strain amplitude distribution curve of austenitic stainless steel under high temperature and high pressure water environment

[0068] Figure 6 Optimal fatigue life curve of austenitic stainless steel in air environment

[0069] Figure 7 Optimal fatigue life curve of austenitic stainless steel materials under high temperature and high pressure water environment

[0070] Figure 8 Evolution curves of correction factor under different surface roughness Detailed Implementation

[0071] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0072] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0073] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first.

[0074] This invention proposes a method for predicting fatigue correction factors in metallic environments that considers surface roughness, the steps of which are as follows:

[0075] The specific process of implementing this invention is as follows: Figure 1 As shown, the following is an example of predicting the environmental fatigue factor of austenitic stainless steel considering the influence of surface roughness:

[0076] S1: Surface roughness of austenitic stainless steel machined samples was obtained using laser confocal microscopy, and appropriate surface roughness measurement methods and results were selected. For example, Figure 2 As shown, surface roughness is expressed using the index R. a To represent, the expression is

[0077]

[0078] Where n is the number of segments, Z i i is the roughness value of the i-th segment, l r This represents the length of the region.

[0079] In step S1, in order to better obtain the required experimental data, the following preferred conditions need to be considered during the experiment:

[0080] 1. Preferably, in step S1, at least four design surface roughnesses for the processed sample are selected, namely R a =0.2μm, 1.2μm, 6.5μm, 12.5μm.

[0081] 2. Preferably, to ensure that the surface roughness measurement results in step S1 are as close to the actual situation as possible, an "X"-shaped line sampling method is used to perform 5 measurements, and the average value of the measurement results is taken. Figure 3 As shown. With roughness R a Taking 1.2μm as an example, the final results are shown in Table 1.

[0082] Table 1. Roughness parameters of the "X" shaped line sampling

[0083]

[0084] S2: In an air environment, fatigue tests are conducted on austenitic stainless steel specimens considering factors such as surface roughness, loading strain rate, and loading strain amplitude.

[0085] S3: Fatigue tests are conducted in a high-temperature and high-pressure water environment, taking into account factors such as the surface roughness, loading strain rate, and loading strain amplitude of the austenitic stainless steel specimen.

[0086] In steps S2 and S3, in order to better obtain the required experimental data, the following preferred conditions need to be considered during the experiment:

[0087] 1. Preferably, at least five loading strain rates are selected, namely 0.02% / s, 0.04% / s, 0.06% / s, 0.08% / s, and 0.1% / s; at least five loading strain amplitudes are selected, namely 0.4%, 0.5%, 0.6%, 0.7%, and 0.8%.

[0088] 2. Preferably, the high-temperature and high-pressure water environment is 325℃, 15.5MPa and 90±2ppb dissolved oxygen.

[0089] 3. Preferably, the ambient temperature in step S2 is selected as 25℃ and 325℃.

[0090] 4. Preferably, the following sequence is adopted in the test process of steps S2 and S3: First, the surface roughness of the specimen, the loading strain rate, and the air or water environment are kept constant to study the effect of different strain amplitude loading conditions on the fatigue life of rough austenitic stainless steel; Second, the surface roughness of the specimen, the loading strain amplitude, and the air or water environment are kept constant to study the effect of different strain rate loading conditions on the fatigue life of rough austenitic stainless steel; Finally, the loading strain rate, the loading strain amplitude, and the air or water environment are kept constant to study the effect of different surface roughness conditions on the fatigue life of austenitic stainless steel.

[0091] Among them, the surface roughness in air environment is R a =1.2μm, strain rate kept constant at 0.04% / s, environmental fatigue life under different strain amplitude conditions as follows: Figure 4 As shown, the fatigue life gradually decreases with increasing strain amplitude. The surface roughness R under high temperature and high pressure water conditions is... a =12.5μm, strain rate kept constant at 0.08% / s, environmental fatigue life under different strain amplitude conditions as follows: Figure 5 As shown, fatigue life increases as the strain amplitude decreases.

[0092] S4: Based on the fatigue test data obtained in steps S2 and S3, analyze the influence of factors such as surface roughness, loading strain rate, and loading strain amplitude on the fatigue life of austenitic stainless steel. Use the Langer equation to obtain the best fitting curves for austenitic stainless steel under air and high-temperature, high-pressure water conditions. The expression is:

[0093]

[0094] Where, ε a Let N be the strain amplitude, N be the fatigue life, and A1, A2, and n1 be the fitting parameters. The optimal fitting curve under air conditions is obtained as follows: Figure 6 As shown, the fitting parameters are A1 = 28.0, A2 = 0.112 and n1 = -0.5235. The optimal fitting curve under high temperature and high pressure water conditions is as follows: Figure 7 As shown, the fitting parameters are A1 = 18.6136, A2 = 0.0722 and n1 = -0.4783.

[0095] S5: Introducing the coefficient of determination R in regression analysis 2 The expression is used to evaluate the degree of agreement between the fitting results and the experimental data.

[0096]

[0097] Among them, y i Let i be the experimental value of the i-th sample. The mean of the experimental values. R represents the model's predicted value. 2 The closer the value is to 1, the better the model fits.

[0098] S6: High-temperature and high-pressure water corrosion fatigue is a material failure caused by the combined effects of three major factors: environment, material properties, and alternating stress. To consider the influence of high-temperature and high-pressure water environment on fatigue life in the environmental fatigue analysis of austenitic stainless steel, the ratio of fatigue life in room temperature air to fatigue life in a high-temperature and high-pressure water environment (under the same strain amplitude) is defined as an environmental fatigue correction factor, expressed as:

[0099]

[0100] Where, N RTair and N water These represent the fatigue life in a room temperature air environment and the fatigue life in a high temperature and high pressure water environment, respectively.

[0101] S7: Eliminate the influence of roughness in air environment and correct the best fitting curve of austenitic stainless steel in air environment.

[0102]

[0103] Among them, A en This is the surface roughness correction factor. The polishing process roughness is taken as 1.2 μm. The fatigue life is calculated by taking the logarithm and then multiplying it by the surface roughness R. a The surface exhibits a linear correlation. By setting the correction factor to 1 when the surface roughness is 1.2 μm, the correction factors for other surface roughnesses are obtained, as shown in Table 2.

[0104] Table 2 Surface Roughness Correction Coefficient

[0105] <![CDATA[Surface roughness R a / μm]]> 0.2 1.2 6.5 12.5 <![CDATA[Correction factor A en > 1.164 1 0.448 0.181

[0106] S8: Based on the best fitting curves obtained from formulas (2) and (5) under air and high temperature and high pressure water conditions, considering temperature T, dissolved oxygen content DO, and strain rate The influence of surface roughness Ra on environmental fatigue life is discussed. Based on formula (4), the relationships between loading strain rate, temperature, dissolved oxygen content, surface roughness, and environmental fatigue correction factor are established respectively.

[0107]

[0108] ln(F en-T )=f(T)

[0109] ln(F en-DO )=f(DO)

[0110]

[0111] S9: Joint Formula The calculated expression for the environmental fatigue correction factor Fen of austenitic stainless steel, taking into account surface roughness, is as follows:

[0112]

[0113] Where C = 0.861845 is a parameter calculated based on the conditions in S9.1-S9.4, and T * O * , and R a * These parameters respectively reflect the effects of temperature, dissolved oxygen content, strain rate, and surface roughness on environmental fatigue life.

[0114] S9.1: The strain rate range for high-temperature and high-pressure water environment tests and air environment tests is 0.02% / s to 0.2% / s. When the strain rate is below 0.0004% / s or above 7% / s, F en Set to a fixed value, that is

[0115]

[0116] S9.2: Since the experimental data for the high-temperature and high-pressure water environment were all obtained at 325℃, in order to obtain the temperature correlation function, F at temperatures below 100℃ was used. en F is set to 1 when the temperature is above 325℃. en Set to a fixed value, that is

[0117]

[0118] S9.3: The upper limit of surface roughness is 12.5 μm, and the lower limit of surface roughness is 0.02 μm, that is...

[0119]

[0120] S9.4: When the dissolved oxygen content (DO) ≤ 100 ppb, O * =0.29.

[0121] The prediction results of the model are shown in Table 3.

[0122] Table 3 Model lifetime prediction results

[0123]

[0124]

[0125] The evolution trend of environmental fatigue correction factor with strain rate under different surface roughness is as follows: Figure 8 As shown in the figure, the calculated environmental fatigue correction factor F... en The environmental fatigue correction factor F decreases with increasing strain rate, with a threshold range of 0.0004% / s to 7% / s. Simultaneously, the environmental fatigue correction factor F increases with increasing surface roughness. en It has increased, as indicated by the arrow in the diagram.

[0126] The above description is merely a preferred embodiment of this patent and is not intended to limit this patent. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this patent shall be included within the scope of protection of this patent.

[0127] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0128] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0129] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of this application, thereby enabling those skilled in the art to better understand and utilize this application.

Claims

1. A method for predicting fatigue correction factors in metallic environments considering surface roughness, characterized in that: Includes the following steps: S1: Obtain the surface roughness of the machined austenitic stainless steel sample. The surface roughness is measured using the index R. a To represent, the expression is: Where n is the number of segments, Z i Let l be the roughness value of the i-th segment. r To represent the length of the region; S2: Under air environment, considering the surface roughness, loading strain rate and loading strain amplitude of the austenitic stainless steel sample, fatigue test is carried out under air environment to obtain the results. S3: Fatigue tests were conducted on austenitic stainless steel specimens under high temperature and high pressure water conditions, taking into account the surface roughness, loading strain rate and loading strain amplitude. S4: Based on the fatigue test data obtained in steps S2 and S3, analyze the effects of surface roughness, loading strain rate, and loading strain amplitude on the fatigue life of austenitic stainless steel specimens, and use the Langer equation to fit the fitting curves of austenitic stainless steel specimens under air and high temperature and high pressure water conditions. The expression is: Where, ε a Where is the strain amplitude, N is the fatigue life, and A1, A2, and n1 are fitting parameters; S5: Analyze the degree of agreement between the fitting results in S4 and the experimental data; S6: Define the environmental fatigue correction factor, expressed as follows: Where, N RTair and N water These represent fatigue life in a room temperature air environment and fatigue life in a high temperature and high pressure water environment, respectively. S7: To eliminate the influence of roughness in an air environment, the fitting curve of austenitic stainless steel processed samples in an air environment is corrected: Among them, A en Surface roughness correction factor S8: Considering the effects of temperature, dissolved oxygen content, strain rate, and surface roughness on environmental fatigue life, based on formulas (2), (4), and (5), the relationships between the loading strain rate, temperature, dissolved oxygen content, surface roughness, and environmental fatigue correction factor are established respectively. ln(F en-T )=f(T) ln(F en-DO )=f(DO) S9: Joint Formula The calculated expression for the environmental fatigue correction factor Fen of austenitic stainless steel, taking into account surface roughness, is as follows: Where T * O * , and R a * These parameters respectively reflect the effects of temperature, dissolved oxygen content, strain rate, and surface roughness on environmental fatigue life.

2. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In step S1, the surface roughness of the austenitic stainless steel sample is obtained using a laser confocal microscope.

3. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S1, four surface roughnesses are selected, namely R a =0.2μm, 1.2μm, 6.5μm, 12.5μm.

4. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 3, characterized in that: In step S1, the X-shaped line sampling method is used to measure each roughness 5 times, and the average value of the measurement results is taken.

5. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: S2 includes: S2.1: By controlling the surface roughness, strain rate, and air environment of the austenitic stainless steel sample to be constant, the relationship between different strain amplitudes and the fatigue life of the austenitic stainless steel sample was obtained. S2.2: By controlling the surface roughness, strain amplitude, and air environment of the austenitic stainless steel sample, the relationship between different strain rates and the fatigue life of the austenitic stainless steel sample was obtained. S2.3: By controlling the loading strain rate, loading strain amplitude, and air environment of the specimen, the relationship between different surface roughness and the fatigue life of austenitic stainless steel processed specimens is obtained.

6. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 5, characterized in that: The strain amplitudes were selected as 0.4%, 0.5%, 0.6%, 0.7%, and 0.8%.

7. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 5, characterized in that: The strain rates were selected as 0.02% / s, 0.04% / s, 0.06% / s, 0.08% / s, and 0.1% / s.

8. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 5, characterized in that: The ambient temperature was selected as 25℃ and 325℃.

9. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: S3 includes: S3.1: By controlling the surface roughness, loading strain rate, and high temperature and high pressure water environment of the austenitic stainless steel processed specimens, the relationship between different strain amplitudes and the fatigue life of the austenitic stainless steel processed specimens was obtained. S3.2: By controlling the surface roughness, strain amplitude, and high temperature and high pressure water environment of the austenitic stainless steel processed specimens, the relationship between different strain rates and the fatigue life of the austenitic stainless steel processed specimens was obtained. S3.3: By controlling the loading strain rate, loading strain amplitude, and maintaining a constant high-temperature and high-pressure water environment for the specimens, the relationship between different surface roughness and the fatigue life of the processed martensitic stainless steel specimens was obtained.

10. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 9, characterized in that: In step S3.1, the strain amplitude is selected as 0.4%, 0.5%, 0.6%, 0.7%, and 0.8%.

11. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 9, characterized in that: In S3.2, the strain rate is selected as 0.02% / s, 0.04% / s, 0.06% / s, 0.08% / s, and 0.1% / s.

12. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 9, characterized in that: In S2, the high-temperature and high-pressure water environment is 325℃, 15.5MPa, and 90±2ppb dissolved oxygen.

13. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S4, under air conditions, the fitting parameters are A1 = 28.0, A2 = 0.112 and n1 = -0.5235.

14. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S4, the fitting parameters under high temperature and high pressure water environment are A1 = 18.6136, A2 = 0.0722 and n1 = -0.4783.

15. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S5, the coefficient of determination R from regression analysis is introduced. 2 The expression is used to evaluate the degree of agreement between the fitting results and the experimental data. Among them, y i Let i be the experimental value of the i-th sample. The mean of the experimental values. These are the predicted values ​​from the model.

16. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S7, the surface roughness R a For surface roughness values ​​of 0.2μm, 1.2μm, 6.5μm, and 12.5μm, the surface roughness correction factor A... en The values ​​were 1.164, 1, 0.448, and 0.181, respectively.

17. A method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S6, F en The value ranges from 3.679 to 4.

042.

18. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: In S9, C = 0.861845.

19. The method for predicting fatigue correction factors in metallic environments considering surface roughness according to claim 1, characterized in that: S9 specifically includes: S9.1: The strain rate range for high-temperature and high-pressure water environment tests and air environment tests is 0.02% / s to 0.2% / s. When the strain rate is below 0.0004% / s or above 7% / s, F en Set to a fixed value, that is S9.2: F at temperatures below 100℃ en F is set to 1 when the temperature is above 325℃. en Set to a fixed value, that is S9.3: The upper limit of surface roughness is 12.5 μm, and the lower limit of surface roughness is 0.02 μm, that is... S9.4: When the dissolved oxygen content (DO) ≤ 100 ppb, O * =0.29.

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