Hydrogen-containing metal low-cycle fatigue life prediction method and system considering hydrogen influence

Abstract

The invention discloses a hydrogen-containing metal low-cycle fatigue life prediction method and system considering hydrogen influence. The method comprises the following steps: calculating a hydrogen embrittlement index; constructing a microscopic material model based on a crystal plasticity finite element method, and carrying out parameter calibration on the microscopic material model to obtain a calibrated microscopic material model; constructing a fatigue index parameter considering the hydrogen influence, and carrying out life cycle cycle simulation on the hydrogen-containing metal sample to obtain a fatigue index parameter evolution curve; and determining a fatigue index parameter critical value, and performing low-cycle fatigue life prediction on the hydrogen-containing metal sample to obtain the predicted fatigue life of the hydrogen-containing metal sample. According to the method, the fatigue index factors of the microcosmic non-uniformity characterization parameters can be used, the material fatigue life prediction efficiency is improved, and the resource consumption is reduced. The hydrogen-containing metal low-cycle fatigue life prediction method and system considering the hydrogen influence can be widely applied to the technical field of metal material fatigue life prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of fatigue life prediction of metal materials, and in particular to a method and system for predicting the low-cycle fatigue life of hydrogen-containing metals taking into account the influence of hydrogen. Background Art

[0002] The hydrogen-related environment makes metal structural components face a complex working environment. They not only have to withstand high stress and large plastic deformation caused by low-cycle fatigue loads, but also face huge challenges brought by hydrogen corrosion. Driven by cyclic loads, hydrogen atoms easily diffuse and enrich into the interior of the material, thereby inducing the risk of hydrogen-induced fatigue fracture.

[0003] Currently, most research analyzes the effects of hydrogen on material fatigue life through specific experiments, lacking micromechanical methods for evaluating the fatigue life of hydrogen-containing materials based on micromodels. This is primarily because hydrogen promotes various failure mechanisms in materials, and the complex interactions between hydrogen and the material microstructure under applied loads make it difficult to determine damage parameters. This makes fatigue life prediction of hydrogen-containing metals based on micromodels difficult. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method and system for predicting the low-cycle fatigue life of hydrogen-containing metals taking into account the influence of hydrogen, which can predict the life of hydrogen-containing metals in advance based on the fatigue index factor of the microscopic inhomogeneity characterization parameter, thereby improving the efficiency of material fatigue life prediction and reducing resource consumption.

[0005] The first technical solution adopted by the present invention is: a method for predicting the low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen, comprising the following steps:

[0006] Obtain stress-strain response curves, fatigue life data, and mechanical property parameters of hydrogen-containing metal specimens, and calculate the hydrogen embrittlement index;

[0007] A mesoscopic material model is constructed based on the crystal plasticity finite element method, and the parameters of the mesoscopic material model are calibrated according to the stress-strain response curve to obtain a calibrated mesoscopic material model;

[0008] Combining mechanical properties with hydrogen embrittlement index, fatigue index parameters that take hydrogen influence into account are constructed. Based on the calibrated microscopic material model, full life cycle simulations are performed on hydrogen-containing metal specimens to obtain the fatigue index parameter evolution curve.

[0009] Based on the fatigue index parameter evolution curve and fatigue life data, the critical value of the fatigue index parameter is determined, and the low-cycle fatigue life of the hydrogen-containing metal sample is predicted to obtain the predicted fatigue life of the hydrogen-containing metal sample.

[0010] Furthermore, the step of obtaining the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample and calculating the hydrogen embrittlement index specifically includes:

[0011] The target metal is subjected to lathe rough machining, CNC lathe fine machining and grinding, and then surface polishing treatment is performed to obtain a metal sample;

[0012] The metal sample is pre-charged with hydrogen using an electrochemical cathode hydrogen charging method to obtain a hydrogen-containing metal sample;

[0013] A tensile test and fatigue test are performed on a hydrogen-containing metal sample using a testing machine to obtain the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample, and the hydrogen embrittlement index is calculated. The stress-strain response curve includes a uniaxial tensile stress-strain curve and a stable cyclic hysteresis curve, and the mechanical property parameters include a uniaxial tensile yield stress and a stress peak value of the stable cyclic hysteresis curve.

[0014] Furthermore, the calculation expression of the hydrogen embrittlement index is specifically as follows:

[0015]

[0016] In the above formula, EI represents the hydrogen embrittlement index, Δl uncharged Indicates the elongation after fracture of the non-hydrogen-charged sample, Δl charged Indicates the elongation after fracture of the hydrogen-filled specimen.

[0017] Furthermore, the microscopic material model includes a crystal plastic constitutive model and a representative volume unit model, and the representative volume unit model includes a plurality of grains with anisotropic mechanical behaviors.

[0018] Furthermore, the expression of the fatigue index parameter considering the influence of hydrogen is specifically as follows:

[0019]

[0020] In the above formula, represents the fatigue index parameter considering the influence of hydrogen, σ0 represents the yield stress, σ max represents the stress peak of the macroscopic stable hysteresis curve, and are the statistical standard deviation and mean of the axial strain in the representative volume unit model, and EI is the hydrogen embrittlement index measured by the quasi-static tensile test of the hydrogen-charged specimen.

[0021] Furthermore, the step of determining the critical value of the fatigue index parameter based on the fatigue index parameter evolution curve combined with the fatigue life data, performing low-cycle fatigue life prediction on the hydrogen-containing metal sample, and obtaining the predicted fatigue life of the hydrogen-containing metal sample specifically includes:

[0022] Inserting the fatigue life data into the fatigue index parameter evolution curve, and determining the fatigue index parameter value corresponding to the fatigue life data as the fatigue index parameter critical value;

[0023] The horizontal coordinate value of the intersection of the horizontal line of the fatigue index parameter critical value and the fatigue index parameter evolution curve is obtained, and the low-cycle fatigue life of the hydrogen-containing metal sample is predicted to obtain the predicted fatigue life of the hydrogen-containing metal sample.

[0024] The second technical solution adopted by the present invention is: a low-cycle fatigue life prediction system for hydrogen-containing metals considering the influence of hydrogen, comprising:

[0025] The first module is used to obtain the stress-strain response curve, fatigue life data and mechanical property parameters of hydrogen-containing metal samples, and calculate the hydrogen embrittlement index;

[0026] The second module is used to construct a mesoscopic material model based on the crystal plasticity finite element method, and calibrate the parameters of the mesoscopic material model according to the stress-strain response curve to obtain the calibrated mesoscopic material model;

[0027] The third module is used to combine mechanical properties and hydrogen embrittlement index to construct fatigue index parameters that take hydrogen into account. Based on the calibrated microscopic material model, full life cycle simulation of hydrogen-containing metal samples is performed to obtain the fatigue index parameter evolution curve;

[0028] The fourth module is used to determine the critical value of fatigue index parameters based on the fatigue index parameter evolution curve combined with fatigue life data, predict the low-cycle fatigue life of hydrogen-containing metal samples, and obtain the predicted fatigue life of hydrogen-containing metal samples.

[0029] The beneficial effects of the method and system of the present invention are as follows: the present invention obtains the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample, and calculates the hydrogen embrittlement index, and then constructs a mesoscopic material model based on the crystal plasticity finite element method, and calibrates the parameters of the mesoscopic material model according to the stress-strain response curve to obtain the calibrated mesoscopic material model, further combines the mechanical property parameters with the hydrogen embrittlement index to construct fatigue index parameters considering the influence of hydrogen, and performs full life cycle simulation on the hydrogen-containing metal sample based on the calibrated mesoscopic material model to obtain a fatigue index parameter evolution curve, and finally determines the fatigue index parameter critical value based on the fatigue index parameter evolution curve combined with the fatigue life data, performs low-cycle fatigue life prediction on the hydrogen-containing metal sample, and obtains the predicted fatigue life of the hydrogen-containing metal sample. Only one fatigue test is needed to measure the fatigue life to determine the critical value, and the fatigue life of the material under the same hydrogen charging condition and different loading conditions can be predicted, and good prediction effect can be obtained. It has the advantages of simplicity and convenience, significantly improves the efficiency of material fatigue life prediction and reduces resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flowchart of the steps of a method for predicting low-cycle fatigue life of hydrogen-containing metals taking into account the influence of hydrogen in the present invention;

[0031] Figure 2 This is a structural block diagram of a low-cycle fatigue life prediction system for hydrogen-containing metals taking into account the influence of hydrogen in the present invention;

[0032] Figure 3 is a schematic diagram of a process for predicting fatigue life provided by a specific embodiment of the present invention;

[0033] Figure 4 is a schematic diagram of a polycrystalline RVE material model provided by a specific embodiment of the present invention;

[0034] Figure 5 1 is a schematic diagram comparing the measured stable hysteresis loop and the numerical simulation results of HRB400 steel at different strain amplitudes provided by a specific embodiment of the present invention;

[0035] Figure 6 The fatigue index parameters of the samples with different hydrogen charging time under strain controlled cyclic loading provided by the specific embodiment of the present invention are Schematic diagram of the evolution law with the cycle number;

[0036] Figure 7 The specific embodiment of the present invention provides different critical values. Schematic diagram of the error evaluation results between the predicted fatigue life under strain-controlled cyclic loading and the measured life. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are provided for ease of description only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted based on the understanding of those skilled in the art.

[0038] In the field of metal fatigue life prediction, the Crystal Plasticity Finite Element Method (CPFEM) is widely used to predict the low-cycle fatigue (LCF) life of metallic materials. This method not only reveals the deformation mechanism dominated by slip at the mesoscopic scale, but also enables fatigue life prediction using fatigue indicator factors (FIPs), such as those based on accumulated plastic slip, strain energy dissipation, and microscopic deformation heterogeneity. Among them, fatigue prediction methods that use deformation heterogeneity as a FIP only require a fatigue test at a specified strain amplitude to determine the FIP critical value, and can accurately predict the fatigue life of materials under other strain amplitude cycles. This method has the advantages of simple model solution and can provide predicted life values ​​before fatigue experiments. However, no relevant research has been found to use microscopic deformation heterogeneity as a FIP for life prediction of hydrogen-containing metals. Therefore, a FIP based on microscopic deformation heterogeneity that considers the influence of hydrogen is proposed to address the above issues for life prediction of hydrogen-containing metals.

[0039] Based on this, the embodiment of the present invention introduces the hydrogen embrittlement index EI and the coefficient of variation of axial strain within the representative volume element (RVE) to correct the characterization parameters of the material's microscopic deformation heterogeneity, and obtains a new fatigue index parameter that takes into account the influence of hydrogen. Based on this new FIP, the low-cycle fatigue life of hydrogen-containing metals is predicted. First, the stress-strain response curves and fatigue life data (N) of the hydrogen-containing metal specimens under uniaxial tension and fatigue are obtained through experiments. f ) and hydrogen embrittlement index (EI); secondly, a microscopic material model is established based on the crystal plasticity finite element method; and the material microscopic model parameters are calibrated; then, the full life cycle cycle process of the hydrogen-containing metal sample is simulated based on the established material model, and the evolution curve of the fatigue index parameter with the number of cycles is plotted; finally, the critical value of the fatigue index parameter is determined by combining the fatigue index parameter evolution curve and the measured life point. The critical value horizontal line can be used to predict the low-cycle fatigue life of metal materials under the same hydrogen charging and different loading conditions. The horizontal coordinate of the intersection of the horizontal line and the evolution curve is the predicted fatigue life.

[0040] Reference Figure 1 The present invention provides a method for predicting the low-cycle fatigue life of hydrogen-containing metals taking into account the influence of hydrogen, the method comprising the following steps:

[0041] S100, obtaining a stress-strain response curve, fatigue life data, and mechanical property parameters of a hydrogen-containing metal sample, and calculating a hydrogen embrittlement index;

[0042] Specifically, the target metal is subjected to lathe rough machining, CNC lathe fine machining and grinding, and then surface polishing treatment is performed to obtain a metal sample; the metal sample is pre-charged with hydrogen using an electrochemical cathode hydrogen charging method to obtain a hydrogen-containing metal sample; the hydrogen-containing metal sample is subjected to a tensile test and a fatigue test using a testing machine to obtain the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample, and the hydrogen embrittlement index is calculated, the stress-strain response curve includes a uniaxial tensile stress-strain curve and a stable cycle hysteresis curve, and the mechanical property parameters include a uniaxial tensile yield stress and a stress peak value of the stable cycle hysteresis curve.

[0043] It should be noted that the metal specimens were obtained by lathe rough machining, CNC lathe fine machining, and grinding, followed by surface polishing. The hydrogen-containing metal specimens were obtained by pre-charging the metal specimens with hydrogen using the electrochemical cathode hydrogen charging method. The stress-strain response curves included a uniaxial tensile stress-strain curve and a stable cyclic hysteresis curve. The mechanical performance parameters included the uniaxial tensile yield stress and the stress peak of the stable cyclic hysteresis curve. The tests were conducted using an MTS809 testing machine, and the loading process control and test data acquisition were completed and recorded by the Teststar control system.

[0044] Among them, the calculation formula of hydrogen embrittlement index (EI) is:

[0045]

[0046] In the above formula, EI represents the hydrogen embrittlement index, Δl uncharged Indicates the elongation after fracture of the non-hydrogen-charged sample, Δl charged It indicates the elongation after fracture of the hydrogen-charged specimen. The elongation after fracture is the percentage of the elongation of the original gauge length to the original gauge length after the specimen is tensile fractured.

[0047] In some specific embodiments, HRB400 steel (hot-rolled ribbed steel bar 400) is selected as the test metal material, which is processed into plate specimens through lathe rough machining, CNC lathe fine machining and grinding, and the plate specimens are surface polished to make their surfaces sufficiently smooth.

[0048] The HRB400 steel samples were pre-charged with hydrogen using the electrochemical cathode hydrogen charging method, and the hydrogen charging time was set to 0 h, 6 h, and 24 h, respectively.

[0049] Immediately after hydrogen charging, the hydrogen-containing metal specimens were subjected to uniaxial tensile and low-cycle fatigue testing using an MTS809 testing machine. An MTS axial extensometer with a gauge length of 25 mm and an ultimate axial strain of 10% was installed at the center of the specimen's effective length. The tensile loading process was displacement-controlled at a loading rate of 0.5 mm / min. Five fatigue tests were conducted, with strain amplitudes of 0.35%, 0.4%, 0.6%, 0.8%, and 1.0%, respectively. Strain control was achieved using a triangular wave loading mode with a frequency of 1 Hz. The loading process and measured data were controlled and recorded by the Teststar control system, including the uniaxial tensile stress-strain curve, the stable cyclic hysteresis curve, the uniaxial tensile yield stress value, and the stress peak of the stable cyclic hysteresis curve.

[0050] The calculation results of hydrogen embrittlement index of different hydrogen-charged samples are shown in Table 1.

[0051] Table 1 Hydrogen embrittlement index of different hydrogen-charged samples

[0052] Hydrogen charging conditions Hydrogen charging 0h Hydrogen charging for 6 hours Hydrogen filling for 24 hours Hydrogen embrittlement index EI 0 0.414 0.595

[0053] S200, constructing a mesoscopic material model based on a crystal plasticity finite element method, and calibrating parameters of the mesoscopic material model according to a stress-strain response curve to obtain a calibrated mesoscopic material model;

[0054] Specifically, the material microstructure model is derived from a crystal plasticity constitutive model combined with a representative volume element (RVE) model. The RVE consists of 8,000 8-node hexahedral elements and 9,621 nodes, containing 125 grains with anisotropic mechanical behavior. The crystal plasticity constitutive model is calculated using the ABAQUS user material subroutine UMAT. The model parameters are calibrated using a trial-and-error method, with the goal of ensuring that the RVE stress-strain hysteresis curves are consistent with experimental results.

[0055] In some specific embodiments, a microscopic material model of the HRB400 hydrogen-charged sample is established based on the crystal plasticity finite element method. The Voronoi method is used to form a polycrystalline RVE model. The RVE consists of 8000 8-node hexahedral units and 9621 nodes, containing 125 grains with anisotropic mechanical behavior, as follows: Figure 4 As shown in Figure 3, the RVE finite element model combined with the crystal plasticity constitutive model can obtain the microscopic material model of hydrogen-charged HRB400 steel.

[0056] Furthermore, the trial-and-error method was used to calibrate the parameters of the microscopic material model of hydrogen-charged HRB400 steel. First, the stable hysteresis curve of the sample with a strain amplitude of 0.6% for 0-hour hydrogen charging was used to calibrate the model material parameters under this amplitude as shown in Table 2. Based on the material parameters in Table 2 (except the saturation value of the critical shear stress τs The stable hysteresis curves of five different strain amplitudes of the samples without hydrogen filling, hydrogen filling for 6h and 24h were simulated to calibrate the τ under the corresponding conditions. s The calibration results are shown in Table 3. Using the material parameters in Tables 2 and 3, the cyclic loading process of the samples with different hydrogen charging times was simulated. The comparison between the simulated stable hysteresis curve and the measured curve is shown in Figure 5 As shown, Figure 5 (a) shows the comparison between the measured stable hysteresis loop and the numerical simulation results after 0 hours of hydrogen charging. Figure 5 (b) shows the comparison between the measured stable hysteresis loop and the numerical simulation results after 6 hours of hydrogen charging. Figure 5 (c) shows the comparison between the measured stable hysteresis loop after 24 hours of hydrogen filling and the numerical simulation results. It can be seen that the simulated stable hysteresis curve is in good agreement with the experimental measured curve.

[0057] Table 2 Crystal plasticity model parameters of HRB400 steel bars without hydrogenation (when ε a =0.6%)

[0058]

[0059] Table 3 Model parameters τ of HRB400 steel at different strain amplitudes for different hydrogen charging times s Value

[0060] Strain amplitude 0.35％ 0.4％ 0.6％ 0.8％ 1.0％ Hydrogen charging 0h 91 91 94 98 102 Hydrogen charging for 6 hours 99 99 102 105 108 Hydrogen filling for 24 hours 103 103 105 108 111

[0061] S300, combining mechanical properties and hydrogen embrittlement index to construct fatigue index parameters that take hydrogen into account, and based on the calibrated microscopic material model, conducting full life cycle simulations on hydrogen-containing metal specimens to obtain fatigue index parameter evolution curves;

[0062] Among them, the calculation formula of fatigue index parameters considering the influence of hydrogen is:

[0063]

[0064] In the above formula, represents the fatigue index parameter considering the influence of hydrogen, σ0 represents the yield stress, σ max represents the stress peak of the macroscopic stable hysteresis curve, and are the statistical standard deviation and mean of the axial strain in the representative volume unit model, and EI is the hydrogen embrittlement index measured by the quasi-static tensile test of the hydrogen-charged specimen.

[0065] In some specific embodiments, based on the above microscopic material model, the fatigue life full cycle process of three hydrogen-charged HRB400 samples under the above five different loading conditions is simulated respectively, and then the fatigue index parameter formula considering the influence of hydrogen is used to calculate the fatigue life at each cycle tensile peak point. The value of The three groups of curves that evolve with the cycle times are as follows: Figure 6 As shown, Figure 6 (a) represents the fatigue index parameters of the sample with 0 hours of hydrogen filling under strain-controlled cyclic loading. With the evolution of the cycle, Figure 6 (b) shows the fatigue index parameters of the sample filled with hydrogen for 6 hours under strain-controlled cyclic loading. With the evolution of the cycle, Figure 6 (c) represents the fatigue index parameters of the sample filled with hydrogen for 24 hours under strain-controlled cyclic loading. As the cycle evolves, the three groups Take any curve in the evolution curve as the FIP critical value The calibration curve is used to mark the lifetime points obtained from the corresponding experiments, including the lifetime of a single experiment (asterisk) and the average lifetime (red point). Critical value The critical value level line and the rest The horizontal coordinate of the intersection of the curves is the fatigue life of the hydrogen-charged specimen under the corresponding loading conditions, so the predicted fatigue life of three different hydrogen-charged specimens under five different loading conditions can be obtained.

[0066] Further analysis Figure 6 It can be seen that the five lines of the hydrogen-filled 0h sample When any one of the evolution curves (corresponding to the five loading conditions) is selected as the calibration curve, the minimum critical value obtained is 0.02441 and the maximum critical value is 0.03317. The minimum critical value of the sample charged with hydrogen for 6 hours is 0.22145 and the maximum critical value is 0.33718. The minimum critical value of the sample charged with hydrogen for 24 hours is 0.26271 and the maximum critical value is 0.37083.

[0067] S400. Determine a critical value of a fatigue index parameter based on the fatigue index parameter evolution curve in combination with fatigue life data, perform a low-cycle fatigue life prediction on the hydrogen-containing metal sample, and obtain a predicted fatigue life of the hydrogen-containing metal sample.

[0068] Specifically, the fatigue life data is inserted into the fatigue index parameter evolution curve, and the fatigue index parameter value corresponding to the fatigue life data is determined as the fatigue index parameter critical value; the horizontal coordinate value of the intersection of the horizontal line of the fatigue index parameter critical value and the fatigue index parameter evolution curve is obtained, and the low-cycle fatigue life of the hydrogen-containing metal sample is predicted to obtain the predicted fatigue life of the hydrogen-containing metal sample.

[0069] Among them, the measured life is the measured average life, and the measured life points are plotted on the fatigue index parameters. In the evolution curve of The value is the critical value. According to the critical value, the predicted fatigue life of the same hydrogen-charged sample under different loading conditions is obtained. The predicted fatigue life of the same hydrogen-charged sample under different loading conditions is the difference between the critical value horizontal line and the corresponding The horizontal coordinate value of the intersection point of the evolution curve.

[0070] In this embodiment, the maximum value, the middle value and the minimum value are used as the critical values. The predicted lifespans of the three groups of hydrogen-charged samples (as shown in Table 4, Table 5, and Table 6) are plotted against the measured lifespans and placed within a 2-fold error band. Figure 7 As shown, Figure 7 (a) indicates that different critical values ​​are used at 0 hours of hydrogen charging Error evaluation results between predicted fatigue life and measured life under strain-controlled cyclic loading, Figure 7 (b) in the figure indicates that different critical values ​​are used when hydrogen is charged for 6 hours. Error evaluation results between predicted fatigue life and measured life under strain-controlled cyclic loading, Figure 7 (c) in the figure indicates that different critical values ​​are used when hydrogen is charged for 24 hours. The error evaluation results of the predicted fatigue life under strain-controlled cyclic loading and the measured life. The horizontal and vertical axes in the figure are the measured fatigue life and the fatigue life predicted by the critical value, respectively. The black solid line represents the ideal prediction result, and the black dashed line represents the 2-fold error factor boundary. As can be seen from the figure, regardless of the maximum, intermediate, or minimum critical value, the predicted fatigue life and the measured life are basically within a 2-fold error. Only a few samples (fatigue life less than 10 2 , which belongs to ultra-low cycle fatigue life) exceeds 2 times the error. This shows that the fatigue index parameters considering the influence of hydrogen are used. The LCF life of HRB400 steel specimens pre-charged with hydrogen is predicted and the results are reasonable and effective. When the middle value (mean) is taken, the predicted life is near the black solid line (y=x) and is within the 2-fold error range. The predicted life at this time is closer to the measured life, indicating that the prediction accuracy of this method can be significantly improved with the increase of measured fatigue life data.

[0071] Table 4 Different critical values ​​of hydrogen charging 0h Predicted fatigue life at different strain amplitudes

[0072]

[0073] Table 5 Different critical values ​​of hydrogen charging for 6 hours Predicted fatigue life at different strain amplitudes

[0074]

[0075] Table 6 Different critical values ​​of hydrogen charging for 24 hours Predicted fatigue life at different strain amplitudes

[0076]

[0077] Therefore, the low-cycle fatigue life prediction method of the pre-charged hydrogen metal material proposed in the embodiment of the present invention is feasible and effective. The method only requires one fatigue test to measure the fatigue life to determine the critical value. The fatigue life of materials can be predicted under different cycles under the same hydrogen charging conditions. The accuracy of the prediction increases with the number of fatigue tests and by using intermediate values ​​between critical values. The proposed method offers the advantages of simplicity and convenience, significantly improving the efficiency of fatigue life prediction for metal materials and reducing resource consumption.

[0078] This paper provides a method for predicting the low-cycle fatigue life of metals using a FIP (Fatigue Inhomogeneity Index) parameter that considers the influence of hydrogen. Currently, all FIP methods based on mesoscopic inhomogeneity parameters within the crystal plasticity finite element framework have failed to predict the life of hydrogen-containing metals. Therefore, the core objective of this research is to develop a new fatigue index factor based on mesoscopic inhomogeneity parameters to provide early life prediction for hydrogen-containing metals.

[0079] Under the action of external loads, the interaction between hydrogen and the material microstructure makes the failure mechanism of hydrogen-containing metal materials complicated, making it difficult to determine the material damage parameters. It becomes difficult to predict the fatigue life of hydrogen-containing metals based on microscopic models, and traditional prediction methods need to be based on multiple sets of measured life data, which is costly. The provided prediction method solves the above problems very well. Only one fatigue test is needed to measure the fatigue life to determine the critical value. The fatigue life of the material under the same hydrogen charging condition and different loading conditions can be predicted, and good prediction results can be obtained. The overall performance is good, and it has the advantages of simplicity and convenience. It significantly improves the efficiency of material fatigue life prediction and reduces resource consumption. In addition, as the number of fatigue tests increases and the life prediction is based on the intermediate value of the critical value, the prediction accuracy can be increased.

[0080] In summary, if Figure 3 As shown, the embodiment of the present invention obtains the stress-strain response curve and fatigue life data (N) of the hydrogen-containing metal sample through experiments. f ) and mechanical properties parameters, and calculate the hydrogen embrittlement index (EI). Based on the crystal plasticity finite element method, a material microscopic model is established, the material microscopic model parameters are calibrated, and the full life cycle of hydrogen-containing metal samples is simulated. Based on the fatigue index parameter calculation formula considering the influence of hydrogen, the fatigue index parameter evolution curve with the number of cycles is plotted, and the critical value of the fatigue index parameter is determined by combining the fatigue index parameter evolution curve and the measured life point. The predicted fatigue life of the same hydrogen-charged specimen under different loading conditions is obtained based on the critical value.

[0081] Reference Figure 2 , a low-cycle fatigue life prediction system for hydrogen-containing metals considering the influence of hydrogen, including:

[0082] The first module 201 is used to obtain the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample, and calculate the hydrogen embrittlement index;

[0083] The second module 202 is used to construct a mesoscopic material model based on the crystal plasticity finite element method, and calibrate the parameters of the mesoscopic material model according to the stress-strain response curve to obtain a calibrated mesoscopic material model;

[0084] The third module 203 is used to construct fatigue index parameters considering the influence of hydrogen by combining mechanical property parameters and hydrogen embrittlement index, and to perform full life cycle simulation on hydrogen-containing metal samples based on the calibrated microscopic material model to obtain fatigue index parameter evolution curve;

[0085] The fourth module 204 is used to determine the critical value of the fatigue indicator parameter based on the fatigue indicator parameter evolution curve combined with the fatigue life data, perform low-cycle fatigue life prediction on the hydrogen-containing metal sample, and obtain the predicted fatigue life of the hydrogen-containing metal sample.

[0086] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0087] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for predicting low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen, characterized in that: The following steps are involved: Obtain stress-strain response curves, fatigue life data, and mechanical property parameters of hydrogen-containing metal specimens, and calculate the hydrogen embrittlement index; A mesoscopic material model is constructed based on the crystal plasticity finite element method, and the parameters of the mesoscopic material model are calibrated according to the stress-strain response curve to obtain a calibrated mesoscopic material model; Combining mechanical properties with hydrogen embrittlement index, fatigue index parameters that take hydrogen influence into account are constructed. Based on the calibrated microscopic material model, full life cycle simulations are performed on hydrogen-containing metal specimens to obtain the fatigue index parameter evolution curve. Based on the fatigue index parameter evolution curve and fatigue life data, the critical value of the fatigue index parameter is determined, and the low-cycle fatigue life of the hydrogen-containing metal sample is predicted to obtain the predicted fatigue life of the hydrogen-containing metal sample.

2. A method for predicting low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen according to claim 1, characterized in that: The step of obtaining the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample and calculating the hydrogen embrittlement index specifically includes: The target metal is subjected to lathe rough machining, CNC lathe fine machining and grinding, and then surface polishing treatment is performed to obtain a metal sample; The metal sample is pre-charged with hydrogen using an electrochemical cathode hydrogen charging method to obtain a hydrogen-containing metal sample; A tensile test and fatigue test are performed on a hydrogen-containing metal sample using a testing machine to obtain the stress-strain response curve, fatigue life data and mechanical property parameters of the hydrogen-containing metal sample, and the hydrogen embrittlement index is calculated. The stress-strain response curve includes a uniaxial tensile stress-strain curve and a stable cyclic hysteresis curve, and the mechanical property parameters include a uniaxial tensile yield stress and a stress peak value of the stable cyclic hysteresis curve.

3. A method for predicting low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen according to claim 2, characterized in that: The calculation expression of the hydrogen embrittlement index is specifically as follows: In the above formula, EI represents the hydrogen embrittlement index, Δl uncharged Indicates the elongation after fracture of the non-hydrogen-charged sample, Δl charged Indicates the elongation after fracture of the hydrogen-filled specimen.

4. A method for predicting low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen according to claim 3, characterized in that: The microscopic material model includes a crystal plastic constitutive model and a representative volume unit model, wherein the representative volume unit model includes a plurality of grains with anisotropic mechanical behaviors.

5. A method for predicting low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen according to claim 4, characterized in that: The expression of the fatigue index parameter considering the influence of hydrogen is specifically as follows: In the above formula, represents the fatigue index parameter considering the influence of hydrogen, σ0 represents the yield stress, σ max represents the stress peak of the macroscopic stable hysteresis curve, and are the statistical standard deviation and mean of the axial strain in the representative volume unit model, and EI is the hydrogen embrittlement index measured by the quasi-static tensile test of the hydrogen-charged specimen.

6. A method for predicting low-cycle fatigue life of hydrogen-containing metals considering the influence of hydrogen according to claim 5, characterized in that: The step of determining the critical value of the fatigue index parameter based on the fatigue index parameter evolution curve combined with the fatigue life data, performing low-cycle fatigue life prediction on the hydrogen-containing metal sample, and obtaining the predicted fatigue life of the hydrogen-containing metal sample specifically includes: Inserting the fatigue life data into the fatigue index parameter evolution curve, and determining the fatigue index parameter value corresponding to the fatigue life data as the fatigue index parameter critical value; The horizontal coordinate value of the intersection of the horizontal line of the fatigue index parameter critical value and the fatigue index parameter evolution curve is obtained, and the low-cycle fatigue life of the hydrogen-containing metal sample is predicted to obtain the predicted fatigue life of the hydrogen-containing metal sample.

7. A low-cycle fatigue life prediction system for hydrogen-containing metals considering the influence of hydrogen, characterized in that: Includes the following modules: The first module is used to obtain the stress-strain response curve, fatigue life data and mechanical property parameters of hydrogen-containing metal samples, and calculate the hydrogen embrittlement index; The second module is used to construct a mesoscopic material model based on the crystal plasticity finite element method, and calibrate the parameters of the mesoscopic material model according to the stress-strain response curve to obtain the calibrated mesoscopic material model; The third module is used to combine mechanical properties and hydrogen embrittlement index to construct fatigue index parameters that take hydrogen into account. Based on the calibrated microscopic material model, full life cycle simulation of hydrogen-containing metal samples is performed to obtain the fatigue index parameter evolution curve; The fourth module is used to determine the critical value of fatigue index parameters based on the fatigue index parameter evolution curve combined with fatigue life data, predict the low-cycle fatigue life of hydrogen-containing metal samples, and obtain the predicted fatigue life of hydrogen-containing metal samples.

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