Austenitic stainless steel fatigue aging damage prediction and evaluation method, equipment and system

Abstract

A method for predicting and evaluating the fatigue aging damage of austenitic stainless steel is provided. Based on the Allometricl model calculation method, the fatigue stress-fatigue life relationship corresponding to the low-cycle accelerated fatigue experiment is output, and the coincidence degree is evaluated by establishing the fitting relationship between the stress amplitude and the fatigue life using the Basquin formula; based on the KAM value in EBSD as the characteristic defect parameter, the change curve with the number of cycles is calculated; based on the Vickers hardness as the mechanical characteristic parameter, the fitting relationship between the Vickers hardness and KAM of each stage sample of the fatigue sample under the fatigue stress is obtained; based on the correlation model between the KAM value and the saturation magnetic induction intensity, the fitting relationship between the saturation magnetization intensity and KAM of each stage sample of the fatigue under the stress action is obtained. By combining the evolution of micro-defects and the macroscopic magnetic properties, the present application realizes the in-situ non-destructive detection and life evaluation of the austenitic stainless steel structure, providing a new technical means for the reliability analysis of engineering structures.

Description

Technical Field

[0001] The present application relates to the technical field of metal material testing, and more particularly to a method for predicting and evaluating austenitic stainless steel fatigue aging damage, an electronic device, and a system for predicting and evaluating austenitic stainless steel fatigue aging damage. Background Art

[0002] Austenitic stainless steel has good heat resistance, corrosion resistance, weldability, and mechanical properties, etc., and is widely used in fields such as automotive parts, ship components, and reactor structures; however, during service, austenitic stainless steel often undergoes the superimposed disturbance of periodic dynamic and static loads, and is extremely prone to fatigue damage, which may then lead to fatigue failure, and in severe cases, it may endanger the operation safety of major equipment such as reactors. Therefore, in-depth research on the fatigue performance of austenitic stainless steel and prediction of its fatigue life are of great significance for ensuring the structural integrity and reliable operation under complex service environments.

[0003] Currently, there are mainly two types of damage mechanics models applied to fatigue damage evolution: one is the Chaboche model and its various simplified models, which are established by directly constructing a damage evolution function based on experimental fatigue curves, have strong universality, and can better characterize the damage behavior of materials under various fatigue load conditions, but have more model parameters and a relatively complex form, bringing certain difficulties to engineering applications; the other is the damage model proposed by Lemaitre based on the theory of thermodynamic dissipation, which essentially belongs to a ductile damage model and directly correlates the damage variable with the development of the plastic strain of the material, with a relatively solid theoretical basis and a relatively simple model form; however, although the above two types of models have been widely used in the field of fatigue damage, in actual engineering, problems such as difficulty in obtaining parameters, complex model calculations, and limited applicability still exist, and it is still difficult to achieve efficient and convenient engineering promotion and application. Summary of the Invention

[0004] The present application provides a method and system for evaluating austenitic stainless steel fatigue damage based on the evolution of mechanical properties, microdefects, and magnetic properties parameters, and realizes in-situ non-destructive detection and life evaluation of austenitic stainless steel structures by combining microdefect evolution with macroscopic magnetic properties.

[0005] In a first aspect, the present application provides a method for predicting and evaluating the fatigue aging damage of austenitic stainless steel, including: calculating the variation curve of the stress amplitude and the number of load cycles to failure during the fatigue damage of the material according to the Basquin formula; taking the kernel average misorientation degree characterizing the degree of plastic deformation as a characteristic defect parameter to calculate the variation curve of the characteristic defect parameter and the reduced cycle number during the service stage of the material; establishing a fatigue damage mechanics model relating the characteristic defect parameter and the Vickers hardness to calculate the Vickers hardness mechanical property parameter in the damage defect state during the service stage of the material; establishing a fatigue damage physical model relating the characteristic defect parameter and the magnetic property parameter to calculate the internal damage defect state during the service stage of the material; comparing the variation curves of the characteristic defect parameter and the mechanical property parameter with the number of load cycles to judge the deterioration state of the mechanical properties of the material and generate the remaining service life of the material.

[0006] In an alternative embodiment of the first aspect, the Basquin formula is: ,

[0007] , , where, σ a is the stress amplitude, σ f ’ is the fatigue strength coefficient, N f is the number of cycles at fatigue fracture under constant amplitude loading, b is the fatigue strength exponent, σ b is the tensile strength of the material in a static tensile test; σ f is the true fracture strength, and ≈σ f .

[0008] In an alternative embodiment of the first aspect, the fatigue strength exponent is determined according to the four-point correlation method, and σ b is determined from the results of the static tensile test of the material.

[0009] In an alternative embodiment of the first aspect, when calculating the variation curve of the characteristic defect parameter and the reduced cycle number, the method includes: defining the reduced fatigue life n = N / Nf, where N is the current number of cycles and Nf is the number of fracture cycles; taking the kernel average misorientation degree KAM characterizing the degree of plastic deformation as the characteristic defect parameter caused by fatigue damage, where KAM is positively correlated with the dislocation density: , where is the geometric dislocation density, μ is the dislocation Burgers vector length, and b' is the electron backscatter diffraction step size; the relationship between the reduced fatigue life n and KAM is: , where KAM0 is the initial KAM value, taking the KAM value at 1 cycle; k is the generation rate of dislocations with respect to the reduced fatigue life n; generating the linear fitting variation curve of KAM of the material under fatigue stress with respect to the reduced fatigue life n.

[0010] In an alternative of the first aspect, when calculating the mechanical property parameter of Vickers hardness, the method includes: according to the relationship between flow stress and dislocation density, fitting the relationship between Vickers hardness and KAM: , where H is the Vickers hardness, both A and B are fitting parameters, A is related to the elastic modulus and dislocation properties, and B is set to 0; where the relationship between flow stress and dislocation density is: , where G is the shear modulus, C is a constant, and b'' is the dislocation Burgers vector.

[0011] In an alternative of the first aspect, the method further includes: according to the linear relationship between KAM and the reduced fatigue life n: , fitting the relationship between the saturation magnetization Ms and n:

[0012] , where Ms0 is the initial saturation magnetization, Ms MAX is the maximum saturation magnetization, which takes the Ms of the fractured state sample or defines the calculated limit value as a fitting parameter; f > 0, which is the fitting parameter of the growth rate of the saturation magnetization with respect to the reduced fatigue life.

[0013] In an alternative of the first aspect, when calculating the internal damage defect state, the method includes: according to the linear relationship between KAM and n and the relationship between Ms and n, fitting the relationship between Ms and KAM of the material under stress: ,

[0014] .

[0015] In an alternative of the first aspect, when evaluating the remaining service life of the material, the method includes: according to the linear relationship between the Vickers hardness H and KAM: , let the initial hardness be :

[0016] ; substituting the relationship between KAM and Ms and the and linear relationship into the linear relationship between H and KAM, obtaining the relationship formula between H and Ms: ; defining the relative saturation magnetization: , substituting it into the relationship formula between H and Ms and letting B = 0, then: .

[0017] Second aspect, the present application provides an electronic device, including: at least one processor; at least one memory; the at least one memory is coupled to the at least one processor and is configured to store instructions executed by the at least one processor, and when the instructions are executed by the at least one processor, the electronic device is caused to execute the damage prediction evaluation method according to the above.

[0018] Third aspect, the present application provides an austenitic stainless steel fatigue aging damage prediction evaluation system using the damage prediction evaluation method according to the above or including the electronic device, including: a stress-fatigue life calculation module, configured to calculate a change curve of stress amplitude and load cycle times to failure during the fatigue damage process of the material according to the Basquin formula; a characteristic defect evolution calculation module, configured to use the kernel average misorientation degree characterizing the degree of plastic deformation as a characteristic defect parameter to calculate a change curve of characteristic defect parameters and reduced cycle times during the entire fatigue service stage of the material; a fatigue damage mechanics calculation module, configured to establish a fatigue damage mechanics model associating the characteristic defect parameters and Vickers hardness to calculate the Vickers hardness mechanical property parameters in the damage defect state during the service stage of the material; a fatigue damage physics calculation module, configured to establish a fatigue damage physics model associating the characteristic defect parameters and magnetic property parameters to calculate the internal damage defect state during the service stage of the material; a fatigue damage state evaluation module, configured to compare the change curves of the characteristic defect parameters and mechanical property parameters with the load cycle times to determine the deterioration state of the mechanical properties of the material and generate the remaining service life of the material.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present application. Description of the Drawings

[0020] The drawings incorporated herein and forming a part of the specification illustrate one or more embodiments of the present application and, together with the description, are used to explain the principles of the present application and to enable those of ordinary skill in the relevant art to make and use the present application.

[0021] Figure 1 is a flowchart of an exemplary austenitic stainless steel fatigue aging damage prediction evaluation method according to some embodiments of the present application.

[0022] Figure 2 is a schematic structural diagram of an exemplary austenitic stainless steel fatigue sample according to some embodiments of the present application.

[0023] Figure 3 is a schematic diagram of an exemplary austenitic stainless steel fatigue stress-fatigue life curve according to some embodiments of the present application.

[0024] Figure 4It is a schematic diagram of the defect statistics of a sample under an exemplary 660 MPa stress condition according to some embodiments of the present application. Among them, (a) is a schematic diagram of the KAM count statistics of fatigue fracture samples at different cycles, and (b) is a schematic diagram of the change trend of KAM with the number of cycles.

[0025] Figure 5 It is a schematic diagram of the linear fitting result of KAM and the reduced fatigue life n under an exemplary 660 Mpa stress condition according to some embodiments of the present application.

[0026] Figure 6 It is a schematic diagram of the statistical result of the Vickers hardness of fatigue samples at different cycles under an exemplary 660 Mpa stress condition according to some embodiments of the present application.

[0027] Figure 7 It is a schematic diagram of the linear fitting result of Vickers hardness and KAM under an exemplary 660 Mpa stress condition according to some embodiments of the present application.

[0028] Figure 8 It is a schematic diagram of the magnetization curve of austenitic stainless steel at different cycles according to some embodiments of the present application.

[0029] Figure 9 It is a schematic diagram of the change of magnetic data of austenitic stainless steel at different cycles according to some embodiments of the present application. Among them, a is the change of the remanent magnetization intensity with the number of fatigue cycles, b is the change of the saturation magnetization intensity with the number of fatigue cycles, c is the change of the coercivity with the number of fatigue cycles, d is the change of the maximum magnetic susceptibility with the number of fatigue cycles, e is the change of the minimum magnetic susceptibility with the number of fatigue cycles, and f is the change of the maximum magnetic permeability with the number of fatigue cycles.

[0030] Figure 10 It is a schematic diagram of the linear fitting result of the saturation magnetization intensity and the reduced fatigue life under an exemplary 660 Mpa stress condition according to some embodiments of the present application.

[0031] Figure 11 It is a schematic diagram of the linear fitting result of the saturation magnetization intensity and KAM under an exemplary 660 Mpa stress condition according to some embodiments of the present application.

[0032] Figure 12 It is a schematic diagram of the linear fitting result of the saturation magnetization intensity and KAM under an exemplary 730 Mpa stress condition according to some embodiments of the present application.

[0033] Figure 13 It is a schematic diagram of the linear fitting result of Vickers hardness and the saturation magnetization intensity under an exemplary 660 Mpa stress condition according to some embodiments of the present application.

[0034] Figure 14 It is a schematic diagram of the idea established according to an exemplary austenitic stainless steel fatigue aging damage prediction and evaluation process of some embodiments of the present application.

[0035] Figure 15 It is a schematic diagram of the connection of an exemplary electronic device according to some embodiments of the present application.

[0036] Description of the reference numerals in the drawings of the specification:

[0037] 201, Stress-fatigue life calculation module, 202, Characteristic defect evolution calculation module, 203, Fatigue damage mechanics calculation module, 204, Fatigue damage physics calculation module, 205, Fatigue damage state evaluation module, 301, Memory, 302, Processor, 303, Communication interface. Detailed implementation manners

[0038] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application.

[0039] In the embodiments of the present application, S30408 stainless steel is used as the fatigue test sample, and low-cycle fatigue tests are carried out under different stress conditions. Samples with different cycle numbers in the intermediate non-fracture stage are selected for mechanical property, physical property, and microstructure characterization tests, attempting to identify fatigue damage characteristic defects closely related to the degradation of mechanical properties, exploring characteristic physical property parameters related to fatigue-induced microstructural changes, and finally establishing a quantitative mathematical model of these three parameters. Another object of the present application is to reverse the defect state through the results of non-destructive magnetic testing and estimate the remaining service life. Thus, microdefect evolution and macroscopic magnetic properties are innovatively combined to achieve in-situ non-destructive detection and life assessment of austenitic stainless steel structures, providing a new technical means for the reliability analysis of engineering structures.

[0040] Specifically, the chemical composition of the S30408 stainless steel sample is shown in Table 1 below:

[0041] Table 1: Chemical composition of S30408 stainless steel (wt.%)

[0042]

[0043] Thus, referring to Figure 1 shown Figure 1The flowchart of an exemplary austenitic stainless steel fatigue aging damage prediction and evaluation method according to some embodiments of the present application is shown. The present application relates to a multi-dimensional fusion prediction and evaluation method for austenitic stainless steel fatigue aging damage. Specifically, the loss prediction and evaluation method includes:

[0044] S1: Calculate the variation curve of the stress amplitude and the number of load cycles to failure during the fatigue damage process of the material according to the Basquin formula.

[0045] Before performing the damage prediction and evaluation, the S30408 stainless steel is processed into a reference Figure 2 shown fatigue test sample, Figure 2 The structural schematic diagram of an exemplary austenitic stainless steel fatigue sample according to some embodiments of the present application is shown. Thus, the present application collectively refers to the fatigue test samples as samples.

[0046] Specifically: Tensile experiments are carried out on the S30408 stainless steel to determine the stress parameters of the fatigue experiment. Subsequently, according to GB / T 3075-2008 Metallic materials - Fatigue testing - Axial force-controlled method, low-cycle accelerated fatigue experiments are carried out under 4 stress conditions. The experimental parameters and results are shown in Table 2 below:

[0047] Table 2: Fatigue experiment data results (stress ratio R = 0.1)

[0048]

[0049] Among them, when performing the fatigue experiment, the selected Figure 2 shown S30408 austenitic stainless steel is used as the sample and its austenite structure is confirmed to be uniform through metallographic inspection, and the grain size meets the standard requirements; then a uniaxial tensile experiment is carried out and an electronic universal material testing machine is used, with the loading rate controlled at 1 mm / min. Five standard samples are taken for the tensile experiment, and the tensile strength, yield strength, and elastic modulus of the S30408 austenitic stainless steel are measured; furthermore, according to the national standard GB / T 3075-2008 Metallic materials - Fatigue testing - Axial force-controlled method, the fatigue experiment is carried out and an electro-hydraulic servo fatigue testing machine (MTS Landmark370.25) is used with the loading mode being the axial force control mode. And to study the influence of different stress levels on the fatigue life, 4 maximum stresses in Table 2 are set for the low-cycle fatigue experiment; the samples after fatigue fracture are observed for the fracture characteristics by a scanning electron microscope, and the morphological characteristics of the fatigue source area, propagation area, and instantaneous fracture area are analyzed. Based on this, the fatigue stress-fatigue life (S-N) curve is constructed in combination with the cycle number data for subsequent fatigue life modeling.

[0050] Thus, samples at the intermediate stage of fatigue were prepared according to the ratio of the number of fracture cycles under two stress conditions of 660 MPa and 730 MPa. The specific experimental parameters and experimental results are shown in Table 2 above. The S-N curves corresponding to the low-cycle accelerated fatigue experiments under 4 stress conditions are referenced Figure 3 as shown Figure 3 FIG. shows a schematic diagram of an exemplary austenitic stainless steel fatigue stress-fatigue life curve of some embodiments of the present application. Fitting the fatigue experimental data, the S-N results satisfy the following relationship: , where σ max is the maximum stress, and N f is the number of cycles at fatigue fracture under a constant amplitude load, that is, the number of fracture cycles.

[0051] Thus, the Basquin formula believes that during the constant stress amplitude fatigue test, there is a relationship between the stress amplitude and the number of load cycles at failure, that is, the Basquin expression: , where σ a is the stress amplitude, σ f ’ is the fatigue strength coefficient, and b is the fatigue strength index.

[0052] Meanwhile, the four-point correlation method is used to determine the fatigue strength index b, where: , where σ b is the tensile strength of the material in the static tensile test, and σ f is the true fracture strength. Among them, the four-point correlation method is a method for solving the stress-life curve parameters by selecting any four test data points. This method assumes that the S-N curve follows the Basquin relationship, and then selects four data points and solves the fatigue strength index b according to the existing four-point correlation method formula.

[0053] Thus, according to the tensile test results and the manufacturer's performance data in Table 3 below:

[0054] Table 3: Stainless steel manufacturer performance data

[0055]

[0056] we get σ b = 710 MPa, take σ f = 1060 MPa, and the relationship between the two is:

[0057] ;

[0058] Among them, for the convenience of calculation, it is usually considered that σ f ’≈σ f , then σ f= 1060 Mpa. At the same time, according to the formula of fatigue strength index b, b = -0.111892 is calculated. Then, the fatigue prediction model for the samples of this application is:

[0059] .

[0060] The comparison between the stress amplitude data obtained according to the above calculation model and the experimental data is shown in Table 4 below:

[0061] Table 4: Comparison between the stress amplitude data calculated by the model and the experimental data

[0062]

[0063] From this, it can be seen that the data obtained from theoretical calculation is in good agreement with the experimental data, and the error is within 10%. At the same time, it can be seen from Table 4 that the error under the stress condition of 730 MPa is relatively large, and 730 MPa is greater than the tensile strength of 710 MPa of the material given by the manufacturer. This may have a greater impact on the damage accumulation of the material during fatigue, resulting in a greater dispersion of fatigue life. Therefore, the mechanical and physical data of the samples at each stage under the stress condition of 660 MPa are selected for modeling in the follow-up.

[0064] S2: Calculate the change curve of the characteristic defect parameter of the material during the service stage and the reduced number of cycles by taking the kernel average misorientation degree representing the degree of plastic deformation as the characteristic defect parameter.

[0065] Specifically, in S2, the reduced fatigue life n = N / Nf is approximately defined, where N is the current number of cycles and Nf is the number of fracture cycles. The fatigue stage and remaining fatigue life of the material are evaluated in the form of a percentage, and KAM (Kernel Average Misorientation) is selected as the quantitative parameter of the characteristic defect caused by fatigue damage. Among them, according to the KAM statistical results of the samples at different fatigue stages under the stress condition of 660 MPa, refer to Figure 4 as shown. Figure 4 shows a schematic diagram of defect statistics of samples under an exemplary stress condition of 660 MPa in some embodiments of this application. Among them, Figure 4 the (a) of is the KAM count statistical chart of fatigue fracture samples at different cycles, Figure 4 the (b) of is the change trend chart of KAM with the number of cycles. KAM is positively correlated with the dislocation density, and the calculation formula is as follows: , where is the geometric dislocation density, μ is the dislocation Burgers vector length, and b' is the electron backscatter diffraction step size.

[0066] In this application, due to the large increase in dislocation density at the initial stage of fatigue and subsequent linear growth, and since the dislocation growth rate remains basically unchanged when the loading frequency changes little, the linear fitting relationship between the reduced fatigue life n and KAM is as follows:

[0067] , where KAM0 is the initial KAM value, which is the KAM value at 1 cycle; k is the generation rate of dislocations with respect to the reduced fatigue life n; according to the above relationship, the linear fitting curve of KAM of the sample under fatigue stress with respect to the reduced fatigue life n can be obtained. Specifically, for the sample under the experimental condition of 660 MPa stress, the linear fitting result of KAM with respect to the reduced fatigue life n is referenced Figure 5 as shown.

[0068] S3: Establish a fatigue damage mechanics model that correlates characteristic defect parameters and Vickers hardness to calculate the Vickers hardness mechanical property parameters in the damaged defect state during the service stage of the material.

[0069] Specifically, in S3, the statistical results of the Vickers hardness of the samples at different cycles under 660 MPa cyclic loading with respect to the number of cycles are as Figure 6 shown. It can be clearly seen from Figure 6 that as the number of cycles increases, the hardness of the samples generally shows an upward trend, and the increase mainly occurs after 1 cycle. Therefore, hardness is selected as the mechanical characteristic parameter and Vickers hardness measurement is carried out. The relationship between its flow stress and dislocation density is as follows: , where G is the shear modulus, C is a constant, and b'' is the dislocation Burgers vector; the flow stress and hardness generally satisfy a linear relationship. Therefore, combining the relationship formulas of KAM and dislocation density, reduced fatigue life n and KAM, and the relationship between flow stress and dislocation density, the relationship formula between hardness and KAM can be fitted as follows: , where H is the Vickers hardness, and both A and B are fitting parameters. A is related to the elastic modulus and dislocation properties, and B is set to 0. According to the above relationship formula between hardness and KAM, the fitting relationship between the Vickers hardness H and KAM of the samples at each stage of fatigue under fatigue stress can be obtained and the degree of coincidence can be compared.

[0070] Therefore, for the fitting relationship between the Vickers hardness H and KAM of the samples at each stage of fatigue under 660 MPa stress, it is referenced Figure 7 as shown; the correlation coefficient R 2 = 0.999, and the degree of coincidence is good. At this time, A = 247.84, and the maximum error is 5.71%.

[0071] S4: Establish a fatigue damage physical model that correlates characteristic defect parameters and magnetic property parameters to calculate the internal damage defect state during the service stage of the material.

[0072] Specifically, in S4, it is referenced Figure 8 as shown.Figure 8 Schematic diagrams of magnetization curves of an exemplary austenitic stainless steel at different cycle numbers according to some embodiments of the present application are shown; wherein the austenitic stainless steel exhibits typical soft magnetic material characteristics, with a narrow hysteresis loop, a large magnetic permeability, a small coercive force, and a small remanence. Refer to Figure 9 as shown Figure 9 Partial magnetization data of the samples at different fatigue cycle numbers and their variation trends are presented. Among them, Figure 9 a in Figure 9 shows a schematic diagram of the variation of the remanent magnetization intensity with the fatigue cycle number, Figure 9 b in Figure 9 shows a schematic diagram of the variation of the saturation magnetization intensity with the fatigue cycle number, Figure 9 c in Figure 9 shows a schematic diagram of the variation of the coercive force with the fatigue cycle number,

[0073] Taking the reduced fatigue life n as a parameter, the relationship between KAM and the saturation magnetization intensity Ms is obtained as follows:

[0074] It is known that the linear relationship between KAM and n: By deforming it, we can get: Therefore, the relationship between the saturation magnetization intensity Ms and n is fitted as: where Ms0 is the initial saturation magnetization intensity, and Ms MAX is the maximum saturation magnetization intensity, which takes the Ms of the fractured sample or defines the calculated limit value as the fitting parameter; f > 0 is the fitting parameter of the growth rate of the saturation magnetization intensity with respect to the reduced fatigue life; according to the above relationship, the fitting relationship between the saturation magnetization intensity Ms and the reduced fatigue life n of the samples at each stage of fatigue under stress can be obtained and the goodness of fit can be compared.

[0075] For the fitting relationship between the saturation magnetization intensity Ms and the reduced fatigue life n of the samples at each stage of fatigue under a stress of 730 MPa, refer to Figure 10 as shown. The correlation coefficient R 2 = 0.9808, and the goodness of fit is good. At this time, Ms0 = 2.0749 emu / g, Ms MAX = 2.6438 emu / g, f = 4.1701, and the maximum error is 1.87%.

[0076] Furthermore, by deforming the above relationship between Ms and n, we can obtain: , and then, by combining the linear relationship between KAM and n and the relationship between Ms and n, the relationship between the saturation magnetization Ms and KAM of the sample under stress is fitted as follows: ,

[0077] .

[0078] According to the above relationship, the fitting relationship between the saturation magnetization Ms and KAM of the samples in each stage of fatigue under stress can be obtained and the degree of fit can be compared.

[0079] For the fitting relationship between the saturation magnetization Ms and KAM of the samples in each stage of fatigue under the stress of 660 MPa, refer to Figure 11 as shown, the correlation coefficient R 2 = 0.999, with a good degree of fit. At this time, Ms0 = 2.0749 emu / g, Ms MAX = 2.6438 emu / g, KAM0 = 1.47, k = 0.53, f = 4.1701, and the maximum error is 1.87%.

[0080] Similarly, for the fitting relationship between the saturation magnetization Ms and KAM of the samples in each stage of fatigue under the stress of 730 MPa, refer to Figure 12 as shown, the correlation coefficient R 2 = 0.999, with a good degree of fit. At this time, Ms0 = 2.0749 emu / g, Ms MAX = 3.696 emu / g, KAM0 = 1.76, k = 0.32, f = 16.4686, and the maximum error is 1.51%.

[0081] S5: Compare the change curves of the characteristic defect parameters and mechanical property parameters with the number of load cycles to judge the deterioration state of the mechanical properties of the material and generate the remaining service life of the material.

[0082] Specifically, in S5, combining the foregoing content, taking KAM as a parameter, the relationship between the Vickers hardness H and the saturation magnetization Ms is obtained as follows:

[0083] The known linear relationship between H and KAM: , let the initial hardness be , and then and are substituted into the linear relationship formula of H and KAM to obtain the relationship formula of H and Ms: ;

[0084] Define the relative saturation magnetization: , substitute it into the relationship formula of H and Ms and let B = 0, then: .

[0085] Thus, for the fitting relationship between the Vickers hardness H and KAM of the samples at each stage of fatigue under a stress of 660 MPa, refer to Figure 13 As shown, the correlation coefficient R 2 = 0.999, with a good degree of fit. At this time, Ms0 = 2.0749 emu / g, Ms MAX = 2.6438 emu / g, A = 247.84, B = 0, k = 0.53, f = 4.1701. Because for low-cycle fatigue, the hardness increases significantly after one cycle of fatigue, so H0 is 300.49 MPa, which is calculated according to the formula by taking KAM0 = 1.47; the maximum error is 6.48%.

[0086] In summary, by adopting the austenitic stainless steel low-cycle fatigue damage prediction and evaluation method of the present application, the following beneficial effects are achieved:

[0087] First, the Allometricl model calculation method is adopted to output the fatigue stress-fatigue life (S-N) relationship corresponding to the low-cycle accelerated fatigue experiment, and the fitting relationship between the stress amplitude and the fatigue life is established with the Basquin formula for goodness-of-fit evaluation. The fitting result is in good agreement with the experimental result.

[0088] Second, the KAM value in EBSD is used as the characteristic defect parameter to obtain the change curve of austenitic stainless steel with the number of cycles.

[0089] Third, the Vickers hardness is used as the mechanical characteristic parameter to obtain the fitting relationship between the Vickers hardness H and KAM at each stage of austenitic stainless steel under fatigue stress. The fitting result is in good agreement with the experimental result.

[0090] Fourth, the magnetic induction intensity parameter is used to correlate the low-cycle fatigue damage characteristic micro-defects, and the fitting relationship between the saturation magnetization intensity Ms and KAM of the samples at each stage of fatigue under stress is obtained. The fitting result is in good agreement with the experimental result.

[0091] Fifth, the defect state is inferred from the non-destructive magnetic detection result to estimate the remaining service life.

[0092] Therefore, starting from the non-destructive magnetic detection result, the present application accurately determines multiple detailed evaluation parameters such as the mechanical property degradation condition, the internal micro-defect damage condition, and the remaining service life condition of the austenitic stainless steel fatigue at the current service stage from the basic theoretical perspective, so as to accurately judge the current service state of the austenitic stainless steel and improve the safety of its service system.

[0093] Refer to Figure 14 As shown. In some embodiments of the present application, the present application also relates to an austenitic stainless steel fatigue aging damage prediction and evaluation system using the above damage prediction and evaluation method. The system includes:

[0094] The stress-fatigue life calculation module 201 is used to calculate the change curve of the stress amplitude and the number of load cycles to failure during the fatigue damage process of the material according to the Basquin formula.

[0095] The characteristic defect evolution calculation module 202 is used to calculate the change curve of the characteristic defect parameters and the reduced cycle number of the material during the whole fatigue service stage by taking the kernel average misorientation degree representing the plastic deformation degree as the characteristic defect parameter.

[0096] The fatigue damage mechanics calculation module 203 is used to establish a fatigue damage mechanics model relating the characteristic defect parameters and the Vickers hardness to calculate the Vickers hardness mechanical property parameters in the damage defect state during the service stage of the material.

[0097] The fatigue damage physics calculation module 204 is used to establish a fatigue damage physics model relating the characteristic defect parameters and the magnetic property parameters to calculate the internal damage defect state during the service stage of the material.

[0098] The fatigue damage state evaluation module 205 is used to compare the change curves of the characteristic defect parameters and the mechanical property parameters with the number of load cycles to judge the deterioration state of the mechanical properties of the material and generate the remaining service life of the material.

[0099] In summary, referring to Figure 14 as shown, the overall idea of the low-cycle fatigue damage prediction and evaluation process of the austenitic stainless steel in this application is as follows:

[0100] 1. The saturation magnetization intensity Ms of the current material can be obtained through physical property tests.

[0101] 2. The plastic deformation degree KAM of the current material can be obtained through the fatigue damage physics calculation module 204.

[0102] 3. The reduced service duration n of the current material (n ≤ 1, a percentage parameter, specifically meaning the current service cycle N / the fatigue fracture cycle Nf) can be obtained through the characteristic defect evolution calculation module 202.

[0103] 4. The Vickers hardness H of the current material can be obtained through the fatigue damage mechanics calculation module 203.

[0104] 5. The number of cycles of the austenitic stainless steel under specific stress conditions can be obtained through the stress-fatigue life calculation module 201. According to the calculated reduced fatigue life n, it can be obtained how many more cycles can be cycled before failure under this stress condition.

[0105] Therefore, all the models in the modeling process of this application include 26 parameter values. The summary of the model characteristic parameters is shown in the following table:

[0106]

[0107] In some embodiments, referring to Figure 15 as shown, Figure 15 a schematic connection diagram of an electronic device for implementing the embodiments of the present application is shown. The electronic device 3 includes: a memory 301 and a processor 302. A computer program that can run on the processor 302 is stored in the memory 301. When the processor 302 executes the computer program, the methods in the above embodiments are implemented. The number of the memory 301 and the processor 302 can be one or more.

[0108] The electronic device 3 further includes:

[0109] a communication interface 303, configured to communicate with external devices and perform data interaction and transmission.

[0110] If the memory 301, the processor 302, and the communication interface 303 are implemented independently, the memory 301, the processor 302, and the communication interface 303 can be interconnected through a bus and complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 15 only a thick line is shown in

[0111] but it does not mean that there is only one bus or one type of bus. Optionally, in specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a chip, the memory 301, the processor 302, and the communication interface 303 can complete communication with each other through an internal interface.

[0112] The embodiments of the present application provide a computer-readable storage medium, which stores a computer program. When the program is executed by the processor 302, the methods provided in the embodiments of the present application are implemented.

[0113] The embodiments of the present application further provide a chip, which includes a processor 302, configured to call and run instructions stored in the memory 301 from the memory 301, so that a communication device installed with the chip executes the methods provided in the embodiments of the present application.

[0114] An embodiment of the present application further provides a chip, including: an input interface, an output interface, a processor 302, and a memory 301. The input interface, the output interface, the processor 302, and the memory 301 are connected through an internal connection path. The processor 302 is configured to execute the code in the memory 301. When the code is executed, the processor 302 is configured to execute the method provided by the embodiment of the application.

[0115] It should be understood that the above-mentioned processor 302 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor 302 may be a processor that supports the advanced reduced instruction set machines (ARM) architecture.

[0116] Further, the above-mentioned memory 301 may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory 301 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0117] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0118] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for predicting and evaluating the fatigue aging damage of austenitic stainless steel, characterized in that, The method includes: Calculating the variation curve of the stress amplitude and the number of load cycles to failure during the fatigue damage process of the material according to the Basquin formula; Taking the kernel average misorientation degree characterizing the degree of plastic deformation as the characteristic defect parameter to calculate the variation curve of the characteristic defect parameter and the reduced cycle number during the service stage of the material; Establishing a fatigue damage mechanics model relating the characteristic defect parameter and the Vickers hardness to calculate the Vickers hardness mechanical property parameter in the damage defect state during the service stage of the material; Establishing a fatigue damage physical model relating the characteristic defect parameter and the magnetic property parameter to calculate the internal damage defect state during the service stage of the material; Comparing the variation curves of the characteristic defect parameter and the mechanical property parameter with the number of load cycles to judge the deterioration state of the mechanical properties of the material and generate the remaining service life of the material; When calculating the variation curve of the characteristic defect parameter and the reduced cycle number, the method includes: Defining the reduced fatigue life n = N / Nf, where N is the current cycle number and Nf is the fracture cycle number; The kernel average misorientation (KAM) characterizing the degree of plastic deformation is used as the characteristic defect parameter caused by fatigue damage, where KAM is positively correlated with the dislocation density: , where is the geometric dislocation density, μ is the length of the dislocation Burgers vector, and b' is the electron backscatter diffraction step size; The relationship between the reduced fatigue life n and KAM is as follows: , where KAM0 is the initial KAM value, taking the KAM value at 1 cycle; k is the generation rate of dislocations with respect to the reduced fatigue life n. Generating a linear fitting variation curve of the KAM of the material under the fatigue stress with the reduced fatigue life n; When calculating the Vickers hardness mechanical property parameter, the method includes: According to the relationship between flow stress and dislocation density, the relationship between Vickers hardness and KAM is fitted: , where H is the Vickers hardness, and both A and B are fitting parameters. A is related to the elastic modulus and dislocation properties, and B is set to 0; among them, the relationship between flow stress and dislocation density is: , where G is the shear modulus, C is a constant, and b'' is the dislocation Burgers vector; According to the linear relationship between KAM and the reduced fatigue life n: , fit the relationship between the saturation magnetization Ms and n: , where Ms0 is the initial saturation magnetization, and Ms MAX is the maximum saturation magnetization, which takes the Ms of the fractured state sample or defines the calculated limit value as the fitting parameter; f > 0, which is the fitting parameter of the saturation magnetization with respect to the growth rate of the reduced fatigue life; When calculating the internal damage defect state, the method includes: According to the linear relationship between KAM and n and the relationship between Ms and n, fit the relationship between Ms and KAM of the material under stress: , 。 2. The damage prediction evaluation method according to claim 1, characterized in that The Basquin formula is: , , , where σ a is the stress amplitude, σ f ’ is the fatigue strength coefficient, N f is the number of cycles at fatigue fracture under constant amplitude loading, b is the fatigue strength exponent, σ b is the tensile strength of the material in a static tensile test; σ f is the true fracture strength, and ≈ σ f .

3. The damage prediction evaluation method according to claim 2, characterized in that, The fatigue strength index is determined according to the four-point correlation method, σ b which is determined by the static tensile test results of the material.

4. The damage prediction evaluation method according to claim 1, characterized in that When evaluating the remaining service life of the material, the method includes: According to the linear relationship between Vickers hardness H and KAM: , let the initial hardness be : ; Substitute the relationship between KAM and Ms and and 's linear relationship into the linear relationship between H and KAM to obtain the relationship formula between H and Ms: ; Define the relative saturation magnetization: , substitute it into the relationship between H and Ms and let B = 0, then: .

5. An electronic device, characterized in that, Including: At least one memory; At least one processor; The at least one memory is coupled to the at least one processor and is used to store instructions executed by the at least one processor. When the instructions are executed by the at least one processor, the electronic device executes the damage prediction evaluation method according to any one of claims 1 to 4.

6. An austenitic stainless steel fatigue aging damage prediction and evaluation method using the damage prediction and evaluation method according to any one of claims 1 to 4 or an austenitic stainless steel fatigue aging damage prediction and evaluation system including the electronic device according to claim 5, characterized in that, Including: A stress-fatigue life calculation module for calculating the variation curve of the stress amplitude and the number of load cycles to failure during the fatigue damage process of the material according to the Basquin formula; A characteristic defect evolution calculation module for taking the kernel average misorientation degree characterizing the degree of plastic deformation as the characteristic defect parameter to calculate the variation curve of the characteristic defect parameter and the reduced cycle number during the full fatigue service stage of the material; A fatigue damage mechanics calculation module for establishing a fatigue damage mechanics model relating the characteristic defect parameter and the Vickers hardness to calculate the Vickers hardness mechanical property parameter in the damage defect state during the service stage of the material; A fatigue damage physical calculation module for establishing a fatigue damage physical model relating the characteristic defect parameter and the magnetic property parameter to calculate the internal damage defect state during the service stage of the material; A fatigue damage state evaluation module for comparing the variation curves of the characteristic defect parameter and the mechanical property parameter with the number of load cycles to judge the deterioration state of the mechanical properties of the material and generate the remaining service life of the material.

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