Alloy superstructure fatigue life prediction method

Abstract

The present application relates to the technical field of fatigue life prediction, and particularly relates to a kind of alloy superstructure fatigue life prediction method.BCC superstructure alloy sample is prepared and material S-N curve is obtained;The roughness parameter and effective bearing area of the surface of BCC superstructure alloy sample are extracted by CT scanning;The stress concentration coefficient and fatigue notch factor of BCC superstructure alloy sample are calculated;Material S-N curve is corrected, and the corrected material S-N curve is twice corrected by fatigue notch factor;According to the material S-N curve after twice correction, the superstructure fatigue life prediction model is mapped to carry out prediction;The power law relationship expression of the relative density and stress concentration coefficient of BCC superstructure alloy sample is established;According to the life requirement of BCC superstructure alloy sample, the relative density in power law relationship expression is optimized.The fatigue life prediction model proposed in the present application has better prediction ability.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of fatigue life prediction, and particularly relates to an alloy superstructure fatigue life prediction method. BACKGROUND

[0002] A superstructure is a three-dimensional porous structure composed of connected unit cells, and can also be regarded as a structure composed of connected rods and nodes in a three-dimensional space, and has high design flexibility; a metal superstructure is light in weight by reducing materials in non-critical regions, has characteristics such as negative Poisson's ratio and high specific surface area, and thus has excellent vibration and noise reduction and heat dissipation performance; with the increase of the flight speed of an aircraft and the increasing harshness of the operating environment, the requirements for the structural design, light weight, vibration and noise reduction, heat dissipation and other multifunctional integration of the aircraft are higher and higher, and the superstructure can well meet the requirements, and has gradually become a new material for aerospace structures.

[0003] Traditional building material technologies such as cutting and casting are low in efficiency when processing superstructures with complex geometrical shapes, and limit the application and development of superstructures in engineering structures, and with the development of metal additive manufacturing technology, especially the development of laser selective melting technology (L-PBF), the forming precision is high (±0.05 mm), the process constraints are small, and various types of superstructures can be directly manufactured, effectively promoting the application and development of the superstructures; however, the surface roughness and internal pores introduced in the additive manufacturing process will significantly affect the performance of the printed structure; scholars have deeply studied the influence of manufacturing defects on the quasi-static mechanical properties (elastic modulus and strength) of superstructures, but the influence on fatigue life is still unclear, and these defects, as stress concentration sources, will induce premature plasticization and fracture, and the influence on the yield stress and ultimate fatigue strength cannot be ignored; at present, there is a lack of in-depth research and standard calculation model for the analysis and calculation of the fatigue life of the additive manufacturing superstructure, and the aircraft is affected by dynamic loads such as aerodynamic loads and vibration loads and complex environments during flight, and the superstructure components often bear cyclic loads and are prone to fatigue failure, thereby affecting the stability and safety of the entire structure, therefore, it is crucial to master the strength and fatigue life of the superstructure to ensure flight safety.

[0004] With the rapid development of laser additive manufacturing technology in China and the wide application of titanium alloy components in the field of aerospace, it is of great significance to study the fatigue fracture failure behavior and life prediction model of Ti-6Al-4V superstructure, and the surface quality has a serious impact on the fatigue life of the structure, which is mainly described by three parameters of surface roughness, residual stress and microstructure. For the Ti-6Al-4V alloy structure printed by L-PBF process, the residual stress can be effectively reduced and the microstructure can be optimized after heat treatment, and then the surface roughness becomes the main factor affecting the fatigue life. In the current research, the fatigue life prediction of superstructure mainly depends on numerical simulation, and the influence of surface roughness and pore defects on fatigue life is difficult to accurately reflect in the overall life calculation, and there is a lack of regulation method for the fatigue life of superstructure. SUMMARY

[0005] In order to solve the problems existing in the prior art, the application provides a kind of alloy superstructure fatigue life prediction method. The method is prepared BCC superstructure alloy sample and obtains material S-N curve;Through CT scanning, the roughness parameters and effective bearing area of the surface of the BCC superstructure alloy sample are extracted;The stress concentration coefficient and fatigue notch coefficient of the BCC superstructure alloy sample are calculated;The material S-N curve is corrected, and the corrected material S-N curve is twice corrected by the fatigue notch coefficient;According to the twice corrected material S-N curve, the superstructure fatigue life prediction model is mapped to predict;The power law relationship expression of the relative density and stress concentration coefficient of the BCC superstructure alloy sample is established;According to the life requirement of the BCC superstructure alloy sample, the relative density in the power law relationship expression is optimized. The fatigue life prediction model provided by the application has better prediction ability.

[0006] The application adopts the following technical scheme, a kind of alloy superstructure fatigue life prediction method, comprising:

[0007] Preparation BCC superstructure alloy sample, and obtain material S-N curve by symmetric cycle fatigue test;

[0008] CT scanning is carried out on the BCC superstructure alloy sample, and the roughness parameters and effective bearing area of the surface of the BCC superstructure alloy sample are extracted;

[0009] The stress concentration coefficient of the BCC superstructure alloy sample is calculated according to the roughness parameters;

[0010] The fatigue notch coefficient of the BCC superstructure alloy sample is calculated according to the stress concentration coefficient and the effective bearing area;

[0011] The material S-N curve is corrected by Goodman model, and the corrected material S-N curve is twice corrected by the fatigue notch coefficient;

[0012] A superstructure fatigue life prediction model is obtained according to the secondary corrected material SN curve mapping; and the fatigue life of a BCC superstructure alloy sample is predicted using the fatigue life prediction model.

[0013] Furthermore, the roughness parameters of the surface of the BCC superstructure alloy sample are extracted, specifically:

[0014] At least four paths are selected along the rod of the BCC superstructure alloy specimen, and the surface curve of each path is extracted;

[0015] The roughness parameters of the BCC superstructure alloy sample surface are calculated based on the surface curve of each path;

[0016] The roughness parameters include: arithmetic mean roughness, ten-point height of micro-roughness, maximum valley depth of profile, maximum height of profile and average width of profile unit.

[0017] Furthermore, the effective bearing area is extracted as follows:

[0018] The theoretical cross-sectional area of ​​the rod was extracted based on the prepared BCC superstructure alloy specimens;

[0019] According to the results of CT scanning of BCC superstructure alloy specimens, the porosity defects of the rod projected along the load direction are obtained;

[0020] Calculate the total defect area in the projection plane based on the area of ​​all pore defects;

[0021] The effective bearing area is obtained according to the difference between the theoretical cross-sectional area and the total defect area.

[0022] Furthermore, the method for calculating the stress concentration factor of the BCC superstructure alloy sample according to the roughness parameter is:

[0023] Calculating the stress concentration factor of each path based on a semi-empirical formula according to the roughness parameter;

[0024] The stress concentration factor of the BCC superstructure alloy specimen is obtained based on the arithmetic mean of the stress concentration factors of the four paths;

[0025] The expression for calculating the stress concentration factor of each path is:

[0026] ;

[0027] in, represents the stress concentration factor, represents the arithmetic mean roughness, Indicates the maximum height of the contour, represents a micro-unevenness ten-point height, represents a notch radius of curvature.

[0028] Further, the method for calculating the fatigue notch factor of the BCC superstructure alloy specimen according to the stress concentration factor and the effective bearing area is:

[0029] According to the ratio of the theoretical cross-sectional area of the rod to the effective bearing area, a correction coefficient is obtained;

[0030] According to the product of the stress concentration factor and the correction coefficient, the fatigue notch factor of the BCC superstructure alloy specimen is obtained.

[0031] Further, the material S-N curve is secondarily corrected through the fatigue notch factor, specifically:

[0032] The stress amplitude corresponding to each life point in the corrected material S-N curve is obtained;

[0033] The superstructure stress amplitude corresponding to each life point is obtained by the ratio of the stress amplitude corresponding to each life point to the fatigue notch factor.

[0034] Further, a superstructure fatigue life prediction model is mapped according to the secondarily corrected material S-N curve, specifically:

[0035] According to the superstructure stress amplitude corresponding to each life point in the secondarily corrected material S-N curve and the corresponding life value, a power law equation is fitted to obtain a superstructure fatigue life prediction model.

[0036] Further, after predicting the fatigue life of the BCC superstructure alloy specimen, the method further comprises:

[0037] The relative density of the BCC superstructure alloy specimen is obtained, and a power law relationship expression of the relative density and the stress concentration factor is established;

[0038] According to the life requirement of the BCC superstructure alloy specimen, the relative density in the power law relationship expression is optimized.

[0039] Further, the power law relationship expression of the relative density and the stress concentration factor is established, specifically:

[0040] A plurality of superstructure finite element models corresponding to unit cells in the BCC superstructure alloy specimen under different relative densities are designed;

[0041] The Mises stress concentration factor of each superstructure finite element model is solved to obtain a stress concentration factor set corresponding to the unit cells in the BCC superstructure alloy specimen under different relative densities;

[0042] The stress concentration coefficient set corresponding to the unit cell of the BCC superstructure alloy sample under different relative densities is nonlinearly fitted to obtain a power law expression of the relative density and the stress concentration coefficient.

[0043] The application has the advantages that: the application corrects the calculation method of the fatigue life by considering the surface roughness and internal pore, optimizes the stress concentration coefficient by using the micro-reconstructed surface roughness and porosity characteristics, and corrects the fatigue life prediction model of the superstructure based on the optimized stress concentration coefficient, so that the corrected model considers the influence of micro-defects on the fatigue life of the superstructure, and the prediction ability is improved; the application also considers the influence of the relative density on the fatigue life of the superstructure, studies the change of the stress concentration coefficient by changing the relative density of the superstructure, obtains the power law relationship between the relative density and the stress concentration coefficient of the superstructure, and realizes the regulation and control of the fatigue life of the superstructure by the relative density. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0045] Figure 1 A flow chart of a superstructure fatigue life prediction method of an alloy according to an embodiment of the present application;

[0046] Figure 2 A material S-N curve diagram according to an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the surface detail structure of a compressed superstructure sample extracted after CT reconstruction according to an embodiment of the present application;

[0048] Figure 4 A schematic diagram of the extracted rough surface profile curve according to an embodiment of the present application;

[0049] Figure 5 A schematic diagram of the effective bearing area of a BCC superstructure alloy sample according to an embodiment of the present application;

[0050] Figure 6 A schematic diagram of the surface morphology of a unit cell according to an embodiment of the present application;

[0051] Figure 7 A schematic diagram of the internal pore information of a unit cell according to an embodiment of the present application;

[0052] Figure 8A single cell introduction internal pore defect model schematic diagram of an embodiment of the present application;

[0053] Figure 9 A standard defect-free BCC superstructure model schematic diagram of an embodiment of the present application;

[0054] Figure 10 A model schematic diagram of an embodiment of the present application introducing internal pore defects;

[0055] Figure 11 A model schematic diagram of an embodiment of the present application restoring real internal pore and surface defects of a single cell;

[0056] Figure 12 A single rod model schematic diagram of an embodiment of the present application restoring real internal pore and surface defects;

[0057] Figure 13 A material S-N curve schematic diagram of an embodiment of the present application after correction using the Goodman model;

[0058] Figure 14 A material S-N curve schematic diagram of an embodiment of the present application after secondary correction;

[0059] Figure 15 A comparison schematic diagram of prediction results and test data of a superstructure fatigue life prediction model of an embodiment of the present application;

[0060] Figure 16 A superstructure finite element model and load distribution schematic diagram of an embodiment of the present application with a relative density of

[0061] Figure 17 A BCC superstructure alloy sample stress concentration coefficient change trend graph corresponding to a single cell under different relative densities of an embodiment of the present application;

[0062] Figure 18 A surface curve schematic diagram extracted under a path of an embodiment of the present application;

[0063] Figure 19 A surface curve sampling length schematic diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. ​

[0065] A schematic flow chart of a method for predicting fatigue life of an alloy superstructure according to an embodiment of the present invention is shown in FIG. Figure 1 Shown, including:

[0066] Prepare BCC superstructure alloy specimens and obtain the material SN curve through symmetrical cyclic fatigue test;

[0067] In the embodiment of the present invention, the BCC superstructure alloy specimen is based on the Ti6Al4V standard test piece and is printed by additive manufacturing technology. The BCC (body-centered cubic) superstructure alloy is an alloy material with a specific crystal structure. The method of the symmetric cyclic fatigue test is implemented in accordance with the GBT6398-2017 standard, which is the national standard for "Fatigue Crack Growth Method of Metallic Materials Fatigue Test". This standard specifies the test method for determining the fatigue crack growth rate of metallic materials, including test principles, specimens, test equipment, test procedures, test result processing and test reports. When conducting the symmetric cyclic fatigue test, the various provisions and requirements in this standard are followed to ensure the standardization, accuracy and repeatability of the test, thereby obtaining reliable test data and results, and obtaining the following. Figure 2 The material SN curve shown is the relationship curve between effective stress S and number of cycles N, which reflects the fatigue performance of the material under alternating load.

[0068] The BCC superstructure alloy specimens were CT scanned to extract the surface roughness parameters and effective bearing area of ​​the BCC superstructure alloy specimens.

[0069] In the embodiment of the present invention, a BCC superstructure alloy sample is scanned and reconstructed in three dimensions with high precision by using CT scanning technology, thereby characterizing its detailed appearance. The CT scanning technology can adopt computed tomography technology. When performing CT scanning, a unit cell in the BCC superstructure alloy sample is used as the scanning object, and a voxel size of 10 μm is set. Then, the scanning data is processed using AVIZO software to extract a high-density point cloud and generate a surface tessellation file, such as Figure 3 As shown in the figure, the surface details of the compressed superstructure specimen extracted after CT reconstruction are given, providing a reference for further analysis of surface roughness and effective bearing area.

[0070] In an embodiment of the present invention, the effective bearing area is extracted by: extracting the theoretical cross-sectional area of ​​the rod based on the prepared BCC superstructural alloy specimen; obtaining the porosity defects of the rod along the projection plane of the load direction based on the results of CT scanning of the BCC superstructural alloy specimen; calculating the total defect area in the projection plane based on the areas of all porosity defects; and obtaining the effective bearing area based on the difference between the theoretical cross-sectional area and the total defect area.

[0071] In one specific embodiment of the present application, the equivalent diameter of the rod of the BCC superstructure alloy sample is 0.6 mm after CT scanning, and the effective bearing area of the BCC superstructure alloy sample is obtained as shown in Figure 5 .

[0072] In the embodiment of the present application, the method for extracting the roughness parameters of the surface of the BCC superstructure alloy sample is as follows: at least four paths are selected along the rod of the BCC superstructure alloy sample, and the surface curve of each path is extracted; the roughness parameters of the surface of the BCC superstructure alloy sample are calculated according to the surface curve of each path; and the extracted roughness parameters include the arithmetic average roughness, the micro-irregularity ten-point height, the maximum valley depth of the profile, the maximum height of the profile, and the average width of the profile unit.

[0073] In one specific embodiment of the present application, the influence of the surface roughness on the structural fatigue life is considered when the fatigue life is predicted, the surface roughness can be represented by the statistical roughness parameters, and one rough surface profile curve extracted is as shown in Figure 4 , thereby directly reflecting the actual situation of the surface roughness of the BCC superstructure alloy sample.

[0074] Specifically, as shown in Figure 18 , the surface curve extracted by one path in the embodiment of the present application is given, the average line direction of the surface curve in the figure is , the surface curve is represented by , and the arithmetic average roughness is the arithmetic average of the absolute values of the peaks and valleys in the surface curve, and the calculation method is as follows:

[0075] ;

[0076] In the formula, is the arithmetic average roughness, is the sampling length of the surface curve, is the surface curve.

[0077] Starting from the average line of the surface curve selected from Figure 18 , the first five peak top elevations are , , , and , and the first five valley bottom elevations are , , , and , and the sum of the absolute value average of the first five peak top elevations and the absolute value average of the first five valley bottom elevations is the micro-irregularity ten-point height, and the expression is as follows:

[0078] ;

[0079] Where, is the ten-point height of micro-roughness, Indicates the Peak elevation, Indicates the Valley bottom elevation, Indicates absolute value.

[0080] like Figure 19 As shown, within the sampling length of a surface curve, the maximum valley depth of the profile is , the maximum contour peak is , maximum height of the profile It is the vertical distance between the maximum profile peak and the maximum profile valley depth within the sampling length, and is calculated as follows:

[0081] ;

[0082] in, is the maximum valley depth of the profile, is the maximum profile peak, is the maximum height of the outline.

[0083] Average width of contour elements is the average value of the width of the contour unit within the sampling length of the surface curve, which is calculated as follows:

[0084] ;

[0085] Where, is the average width of the contour unit, Indicates the The width of the contour unit, Indicates the number of contour elements.

[0086] Calculate the stress concentration factor of BCC superstructure alloy specimens based on roughness parameters;

[0087] In the embodiment of the present invention, surface roughness is introduced into the local stress concentration that controls crack initiation and propagation. Therefore, the stress concentration factor can be calculated by treating the surface roughness as a series of microscopic notches using a semi-empirical formula method. In the embodiment of the present invention, a correlation expression between the stress concentration factor and the statistical parameters of surface roughness is established by combining numerical simulation and Bayesian learning, which is expressed as:

[0088] ;

[0089] in, represents the stress concentration factor, represents the arithmetic mean roughness, Indicates the maximum height of the contour, represents a ten-point height of micro-unevenness, represents a notch radius of curvature.

[0090] In one embodiment of the present application, a series of results of calculating the stress concentration coefficient according to the roughness parameter are given as shown in Table 1:

[0091] Table 1 Roughness parameter and stress concentration coefficient

[0092] ;

[0093] In the embodiment of the present application, the stress concentration coefficient of each of the four selected paths is calculated respectively, and the average of the stress concentration coefficients of the four paths is taken to obtain the stress concentration coefficient of the rod.

[0094] According to the stress concentration coefficient and the effective bearing area, the fatigue notch coefficient of the BCC superstructure alloy sample is calculated;

[0095] In the embodiment of the present application, the method for calculating the fatigue notch coefficient of the BCC superstructure alloy sample is specifically: a correction coefficient is obtained according to the ratio of the theoretical cross-sectional area of the rod to the effective bearing area; the fatigue notch coefficient of the BCC superstructure alloy sample is obtained according to the product of the stress concentration coefficient and the correction coefficient, and the calculation expression is as follows:

[0096] ;

[0097] ;

[0098] In the formula, is a correction coefficient, is a theoretical cross-sectional area, is an effective bearing area, is a fatigue notch coefficient, is a stress concentration coefficient.

[0099] In the embodiment of the present application, a result of calculating the fatigue notch coefficient of the BCC superstructure alloy sample is given as shown in Table 2:

[0100] Table 2 Fatigue notch coefficient after correction of effective bearing area

[0101] ;

[0102] The material S-N curve is corrected by using the Goodman model, and the corrected material S-N curve is secondarily corrected by the fatigue notch coefficient;

[0103] The Goodman model is a model for correcting the fatigue strength evaluation result of only considering alternating stress by establishing the relationship between the average stress and the alternating stress, and in the embodiment of the application, the material S-N curve is corrected by using the Goodman model to obtain , as shown in Figure 13 , and then the material S-N curve after correction is secondarily corrected by using the calculated fatigue notch factor, so as to realize the mapping of the material S-N curve to the superstructure S-N curve, as shown in Figure 14 , that is, the prediction model of the superstructure fatigue life considering internal and external defects is obtained.

[0104] In one specific embodiment of the application, when the Goodman model is used to correct the material S-N curve, the stress amplitude corresponding to the key life point in the material S-N curve is first extracted, the key life point can be the life point corresponding to , times of cycles, and can be selected according to actual conditions; at the same time, the ultimate tensile strength of the BCC superstructure alloy sample is obtained by a tensile test, which is used as the core parameter of the Goodman model, the average stress borne by the BCC superstructure alloy sample is calculated according to the conditions of the engineering load, for each life point in the S-N curve, the value of 1 is reduced by the ratio of the average stress borne by the BCC superstructure alloy sample to the ultimate tensile strength, and is multiplied by the stress amplitude of the key life point, to obtain the corrected stress amplitude of each life point, and the curve is refitted according to the corrected stress amplitude of each life point, to obtain the corrected material S-N curve.

[0105] The method for secondarily correcting the corrected material S-N curve by the fatigue notch factor is as follows: the stress amplitude corresponding to each life point in the corrected material S-N curve is obtained; the superstructure stress amplitude corresponding to each life point is obtained by the ratio of the stress amplitude corresponding to each life point to the fatigue notch factor, and the curve is refitted by using the superstructure stress amplitude corresponding to each life point, so as to obtain the material S-N curve after secondary correction.

[0106] The superstructure fatigue life prediction model is obtained by mapping the material S-N curve after secondary correction; the fatigue life of the BCC superstructure alloy sample is predicted by using the fatigue life prediction model

[0107] In the embodiment of the application, the power law equation is fitted by using the superstructure stress amplitude corresponding to each life point in the material S-N curve after secondary correction and the corresponding life value, to obtain the superstructure fatigue life prediction model, as shown in Figure 15As shown, it is a schematic diagram comparing the prediction results of the superstructure fatigue life prediction model and the test data. It can be seen that the superstructure fatigue life prediction model proposed in the present invention has a high prediction accuracy within a 3-fold error band.

[0108] Obtain the relative density of BCC superstructure alloy specimens and establish a power law relationship between relative density and stress concentration factor;

[0109] In the embodiment of the present invention, the method for establishing the power law relationship expression between relative density and stress concentration factor is as follows:

[0110] Design multiple superstructure finite element models corresponding to unit cells in BCC superstructure alloy specimens at different relative densities; solve the Mises stress concentration coefficient of each superstructure finite element model to obtain a set of stress concentration coefficients corresponding to unit cells in BCC superstructure alloy specimens at different relative densities; perform nonlinear fitting on the set of stress concentration coefficients corresponding to unit cells in BCC superstructure alloy specimens at different relative densities to obtain a power law relationship expression between the relative density and the stress concentration coefficient.

[0111] In a specific embodiment of the present invention, the surface morphology and internal pore information of the unit cell are obtained after CT scanning of the BCC superstructure alloy sample, such as Figure 6 The figure shows the schematic diagram of the surface morphology of a unit cell; Figure 7 Schematic diagram of the pore information inside the unit cell; Figure 8 This is a schematic diagram of a unit cell internal pore defect model. In this embodiment of the present invention, four superstructure finite element models are designed based on the surface morphology and internal pore information of the unit cell. These models are: a standard defect-free BCC superstructure model; a model with internal pore defects; a model that truly restores the internal pores and surface defects of the unit cell; and a single-rod model that truly restores the internal pores and surface defects. Figure 9 Shown is a schematic diagram of a standard defect-free BCC superstructure model; Figure 10 Schematic diagram of the model for importing internal pore defects; Figure 11 Schematic diagram of the model that truly restores the internal pores and surface defects of the unit cell; Figure 12 The schematic diagram of the single-rod model is used to truly restore the internal pores and surface defects; and ABAQUS software is used to simulate the stress concentration of the superstructure finite element model under simple compression load for different relative densities. At the same time, periodic boundary conditions are applied to the superstructure finite element model, and pressure is applied to the unit cell structure according to the contact area to obtain the load distribution of the superstructure finite element model under different relative densities, as shown in the following figure: Figure 16 As shown, the embodiment of the present invention provides a relative density of The superstructure finite element model and load distribution diagram under the above conditions are shown in Table 3. Finally, the Mises stress concentration coefficient of the superstructure finite element model is solved, and the stress concentration coefficient corresponding to the unit cell in the BCC superstructure alloy specimen under different relative densities is obtained. The corresponding change trend diagram is shown in Table 3. Figure 17 As shown, Figure 17 The volume ratio on the horizontal axis is the relative density in the embodiment of the present invention:

[0112] Table 3 Stress concentration factors of BCC unit cells at different relative densities

[0113] ;

[0114] In the embodiment of the present invention, after performing nonlinear fitting on the stress concentration coefficient set corresponding to the unit cell in the BCC superstructure alloy specimen at different relative densities, the power law equation of the stress concentration coefficient of the unit cell with the relative density is obtained as follows:

[0115] ;

[0116] Where, represents the stress concentration factor of the unit cell, the subscript represents a single cell, is the relative density.

[0117] The relative density in the power law relationship expression is optimized according to the life requirement of the BCC superstructure alloy sample.

[0118] According to the power law equation of the stress concentration coefficient of the unit cell and the relative density obtained above, the value of the stress concentration coefficient can be controlled by adjusting the relative density, thereby achieving the life regulation of the BCC superstructure alloy.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting fatigue life of alloy superstructures, characterized in that: include: Prepare BCC superstructure alloy specimens and obtain the material SN curve through symmetrical cyclic fatigue test; The BCC superstructure alloy specimens were CT scanned to extract the surface roughness parameters and effective bearing area of ​​the BCC superstructure alloy specimens. Calculating the stress concentration factor of the BCC superstructure alloy sample according to the roughness parameter; The fatigue notch factor of the BCC superstructure alloy sample is calculated according to the stress concentration factor and the effective bearing area; the method is: Obtaining a correction coefficient based on a ratio of a theoretical cross-sectional area of ​​the rod to the effective bearing area; Obtaining a fatigue notch factor of a BCC superstructure alloy specimen according to a product of the stress concentration factor and the correction factor; The material SN curve is corrected using the Goodman model, and the corrected material SN curve is secondarily corrected using the fatigue notch coefficient; A superstructure fatigue life prediction model is obtained according to the secondary corrected material SN curve mapping; and the fatigue life of a BCC superstructure alloy sample is predicted using the fatigue life prediction model.

2. The method for predicting fatigue life of an alloy superstructure according to claim 1, wherein: The roughness parameters of the BCC superstructure alloy sample surface are extracted, specifically: At least four paths are selected along the rod of the BCC superstructure alloy specimen, and the surface curve of each path is extracted; The roughness parameters of the BCC superstructure alloy sample surface are calculated based on the surface curve of each path; The roughness parameters include: arithmetic mean roughness, ten-point height of micro-roughness, maximum valley depth of profile, maximum height of profile and average width of profile unit.

3. The method for predicting fatigue life of an alloy superstructure according to claim 1, wherein: The effective bearing area is extracted as follows: The theoretical cross-sectional area of ​​the rod was extracted based on the prepared BCC superstructure alloy specimens; According to the results of CT scanning of BCC superstructure alloy specimens, the porosity defects of the rod projected along the load direction are obtained; Calculate the total defect area in the projection plane based on the area of ​​all pore defects; The effective bearing area is obtained according to the difference between the theoretical cross-sectional area and the total defect area.

4. The method for predicting fatigue life of an alloy superstructure according to claim 2, wherein: The method for calculating the stress concentration factor of the BCC superstructure alloy sample according to the roughness parameters is: Calculating the stress concentration factor of each path based on a semi-empirical formula according to the roughness parameter; The stress concentration factor of the BCC superstructure alloy specimen is obtained based on the arithmetic mean of the stress concentration factors of the four paths; The expression for calculating the stress concentration factor of each path is: ； in, represents the stress concentration factor, represents the arithmetic mean roughness, Indicates the maximum height of the contour, Indicates the ten-point height of micro-roughness, Indicates the notch curvature radius.

5. The method for predicting fatigue life of an alloy superstructure according to claim 1, wherein: The corrected material SN curve is corrected twice using the fatigue notch coefficient, specifically: Obtain the stress amplitude corresponding to each life point in the modified material SN curve; The superstructure stress amplitude corresponding to each life point is obtained by calculating the ratio of the stress amplitude corresponding to each life point to the fatigue notch factor.

6. The method for predicting fatigue life of an alloy superstructure according to claim 5, characterized in that: The superstructure fatigue life prediction model is obtained based on the secondary corrected material SN curve mapping, specifically: According to the superstructure stress amplitude corresponding to each life point in the secondary corrected material SN curve and the corresponding life value fitting power law equation, the superstructure fatigue life prediction model is obtained.

7. The method for predicting fatigue life of an alloy superstructure according to claim 1, wherein: After predicting the fatigue life of BCC superstructure alloy specimens, it also includes: Obtaining the relative density of the BCC superstructure alloy sample, and establishing a power law relationship expression between the relative density and the stress concentration factor; The relative density in the power law relationship expression is optimized according to the life requirement of the BCC superstructure alloy sample.

8. The method for predicting fatigue life of an alloy superstructure according to claim 7, characterized in that: A power law relationship expression between the relative density and the stress concentration factor is established, specifically: Design multiple superstructure finite element models corresponding to the unit cell of BCC superstructure alloy specimens at different relative densities; The Mises stress concentration coefficient of each superstructure finite element model is solved to obtain the stress concentration coefficient set corresponding to the unit cell in the BCC superstructure alloy specimen at different relative densities; A nonlinear fitting is performed on the stress concentration coefficient set corresponding to the unit cell in the BCC superstructure alloy specimens at different relative densities to obtain a power law relationship expression between the relative density and the stress concentration coefficient.

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