Fatigue damage numerical simulation method for SLM formed high-strength aluminum alloy

Abstract

The invention provides a fatigue damage numerical simulation method for SLM formed high-strength aluminum alloy. The method comprises the steps that firstly, a fatigue part model is established; 2, simulating internal defects of the finite element model; 3, carrying out finite element simulation analysis, and carrying out fatigue life simulation on the standard fatigue sample; 4, the step 3 is repeatedly executed according to finite element simulation of the defects of different positions and different sizes, and a stress life curve is drawn; and in the fifth step, based on the acquired data, predicting the fatigue life under the conditions of large defect size, multiple defect number and long defect distance by adopting a method based on logarithm regression. According to the method, the fatigue life prediction model for the size, the number and the position of the microdefect is established, the reliability of the model is verified, and the method can be applied more widely.

Description

Technical Field

[0001] The present application relates to the field of numerical simulation technology, and in particular to a method for numerically simulating fatigue damage of SLM-formed high-strength aluminum alloys. Background Art

[0002] Metal additive manufacturing, particularly selective laser melting (SLM), is widely used in aerospace and other fields, but faces challenges such as difficult process control, high costs, and limited part size. High-strength aviation aluminum alloys hold great promise for application due to their excellent properties, but relatively little research has been conducted on their fatigue properties.

[0003] Finite element simulation can be used to optimize manufacturing parameters, predict the mechanical properties of parts, and provide important information for part qualification and certification. However, this method still faces challenges and shortcomings: high computational costs, limited model applicability, and insufficient understanding of physical mechanisms, which restrict the further application of simulation technology. Summary of the Invention

[0004] The present application provides a numerical simulation method for fatigue damage of SLM-formed high-strength aluminum alloys, which can be used to solve the technical problem of poor accuracy in finite element prediction of part properties.

[0005] A numerical simulation method for fatigue damage of SLM-formed high-strength aluminum alloy, the method comprising:

[0006] Step 1: Establish fatigue part model;

[0007] Step 2: simulate the internal defects of the finite element model;

[0008] Step 3: Conduct finite element simulation analysis to simulate the fatigue life of standard fatigue specimens;

[0009] Step 4: Repeat step 3 for finite element simulation of defects at different locations and sizes, and draw stress-life curves;

[0010] In step 5, based on the acquired data, the fatigue life under the conditions of large defect size, multiple defects and long defect distance is predicted using a logarithmic regression method.

[0011] The simulation results of this application will reveal the plastic deformation behavior and failure mechanism of materials under different load conditions, providing a theoretical basis and technical support for improving the fatigue performance of SLM-formed components. Through simulation, the research will be able to predict the performance of materials in actual applications and provide data support for the design of more durable components.

[0012] This application establishes a fatigue life prediction model for micro-defect size, number and location, and verifies the reliability of the model, which can be used in a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of the solution provided for the embodiment of this application;

[0014] Figure 2 Internal defect diagram of the standard fatigue specimen provided in the embodiment of this application;

[0015] Figure 3 A diagram showing the simulated stress results provided by an embodiment of the present application;

[0016] Figure 4 A simulation grid division diagram provided in an embodiment of the present application;

[0017] Figure 5 A graph showing micro-defects of different sizes provided in the embodiments of the present application;

[0018] Figure 6 A graph showing micro-defects at different locations provided in the embodiments of the present application;

[0019] Figure 7 A graph showing different numbers of micro-defects provided in the embodiments of the present application;

[0020] Figure 8 This is one of the other micro-defect size prediction life curves provided in the embodiments of the present application;

[0021] Figure 9 The second graph of lifespan prediction of other micro-defect quantity provided in the embodiment of the present application;

[0022] Figure 10 This is another micro-defect quantity prediction life curve diagram provided in the embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0024] The following first introduces the embodiments of the present application with reference to the accompanying drawings.

[0025] The sample was first polished and cleaned for electron backscatter diffraction (EBSD) scanning using a scanning electron microscope (SEM) with an EBSD camera and associated data collection and analysis software. The EBSD data was directly used to generate a true finite element mesh and calculate the initial dislocation density. Following the EBSD test, electron backscatter diffraction analysis and imaging were performed on the same region of interest within the specimen. Transmission electron microscopy (TEM) was then performed to investigate the dislocation interactions and distribution within the microstructure, as well as the local metallographic distribution.

[0026] Step 1: Establish fatigue part model;

[0027] The geometric model is established according to the standard fatigue tensile specimen to ensure the accuracy of the finite element analysis results.

[0028] Since the influence of the standard sample clamping method on the test results can be ignored, when establishing the finite element model, the external thread is optimized as a cylinder in the modeling, and the model is established from three aspects: thread parameters, cylinder structure and verification evaluation;

[0029] Optimize thread parameters, adjust pitch, lead and diameter, and simplify tooth profile;

[0030] Design cylindrical structures, determine lengths, optimize transitions, and consider tolerances;

[0031] Finally, an evaluation is conducted through strength analysis and connection reliability verification to improve the efficiency of finite element analysis.

[0032] Step 2: Simulate internal defects of the finite element model.

[0033] In fatigue part models without prefabricated defects, internal defects can be created using the cut function in the modeling software. Since the shapes of defects inside additively manufactured metal parts are usually irregular, they need to be optimized for modeling.

[0034] The details are as follows: When creating a standard fatigue specimen in the modeling software, a complete specimen model is first constructed based on the geometric dimension parameters. Next, the defect characteristics, including size and shape, are determined, and the software's geometry editing function is used to create a defect at the center of the specimen. Defect types include circular pores of varying radii, elliptical pores with varying semi-axes and angles, various surface and internal cracks, spherical inclusions with varying center coordinates and radii, and fibrous inclusions with varying lengths, diameters, and orientations.

[0035] To achieve defects of different sizes and positions, size parameters can be set. For example, for spherical defects, different radii can be defined as r1, r2, r3, etc., and offset by preset distances along the axis of the specimen or in different radial directions, and the creation process can be repeated.

[0036] During the creation process, the size, shape and location information of each defect must be recorded in detail for subsequent analysis and comparison.

[0037] Finally, all existing defects are optimized into circular holes. By adjusting the shape parameters of the defects, the defects are made to conform to the geometric characteristics of the circular holes, and the model is further improved to provide an accurate model basis for fatigue performance analysis and other research. Figure 2 shown.

[0038] To explore the effects of micro-defects of different sizes on fatigue life, multiple models were established. Finally, for the single defect model, the defect sizes were set to 0.2mm, 0.3mm, 0.4mm, 0.6mm, 0.8mm, and 1.0mm, respectively, to analyze the effect of defect size on fatigue life in the case of a single defect.

[0039] For the case of a single defect position change, on the basis of keeping the defect size at 0.4mm, models with radial offsets of 1.5mm, 1.8mm, 2.2mm, 2.5mm, and 2.6mm were established to observe the effect of radial variation of defect position on fatigue life. In multiple defect models, such as Figure 5 As shown in the figure, different numbers of micro-defects are created within the same cross-section. Examples include three uniformly distributed defects, four uniformly distributed defects, a single defect, and a single defect with a radial offset of 2.6 mm. Taking a 0.4 mm defect as an example, the effect of the interaction of multiple micro-defects on fatigue life is studied by varying the number and location of defects.

[0040] The third step is to conduct finite element simulation analysis and simulate the fatigue life of the standard fatigue specimen.

[0041] Fix the lower clamping end of the sample and apply pressure load on the upper end of the standard fatigue specimen;

[0042] Tetrahedron was chosen as the mesh type for finite element analysis because of its excellent geometric adaptability and good computational stability. In addition, mesh density is an important factor affecting the accuracy of fatigue life finite element results. However, there is currently no quantitative method to calibrate mesh density. Only a lower threshold is guaranteed, and there is no upper limit on the number of meshes required. Although the denser the mesh, the higher the analysis accuracy, there is no linear correlation between the two. Based on the comparison of different simulation results, a mesh density with a cell size of 0.2 mm was selected;

[0043] Apply different pressures to the same fatigue part to obtain different stress results at key parts (stress concentration parts), convert the actual load spectrum into a stress spectrum, calculate the fatigue life of the fatigue part under the stress, and then draw the stress-life SN curve. ANSYS simulation results are as follows Figure 3 As shown in the figure. The fatigue life model is as follows Figure 4 shown.

[0044] In step 4, step 3 is repeated for the finite element simulation of defects at different locations and sizes, and the stress-life curve (SN curve) is plotted.

[0045] In step 5, based on the acquired data, the fatigue life under the conditions of large defect size, multiple defects and long defect distance is predicted using a logarithmic regression method.

[0046] Since the data showed an exponential relationship, a logarithmic regression method was used to predict the fatigue life under different defect sizes. The SN curve (stress-life curve) was established to describe the relationship between stress amplitude and fatigue life.

[0047] Specifically, the acquired fatigue life data is first logarithmically transformed to a linear relationship for the application of linear regression. Then, using MATLAB, code is written to input the known defect size and corresponding fatigue life data, and a logarithmic regression function is applied to fit the parameters of the SN curve. The SN curve is then used to predict fatigue life for different defect sizes. During the code development process, attention must be paid to data preprocessing, the establishment and verification of the regression model, and the visualization of the prediction results to ensure the accuracy and reliability of the prediction.

[0048] Compared with existing research results, the simulation results show consistent trends with literature data. The simulation process fully considers the influence of microdefect location and size on the material fatigue performance, as well as the material response under different loading conditions. Using experimental data and logarithmic regression methods, a microdefect and fatigue life assessment model was successfully established. Based on logarithmic regression analysis, this model correlates parameters such as defect size, number, and location with fatigue life. The relationship between stress amplitude and fatigue life is described by fitting the S-N curve. The model can predict the fatigue life of materials under different conditions based on the input defect parameters, providing an important basis for evaluating and optimizing material fatigue performance. The model also reveals the effects of microdefects of different locations on the fatigue life of SLM-formed aircraft-strength aluminum alloys. The farther the position is offset, the shorter the fatigue life. However, when the defect breaks through the fatigued part surface, the larger the offset distance, the smaller the defect size, and the fatigue life increases with increasing offset distance. The study also reveals the effects of different sizes and numbers of microdefects on the fatigue life of SLM-formed aircraft-strength aluminum alloys. Smaller defect sizes increase fatigue life, and a more symmetrical defect distribution results in a longer fatigue life.

[0049] This application innovatively combines finite element simulation with numerical simulation in the early simulation analysis of additive manufacturing. First, through finite element simulation, the formation and development of micro defects in the additive manufacturing process and how they affect the fatigue life of the material are analyzed in detail. This work will help to understand the influence mechanism of micro defects on fatigue crack initiation and propagation, especially in high-strength aviation aluminum alloy materials, the influence of micro defects is particularly critical. Then, by establishing an evaluation model of micro defects and fatigue life, the plastic deformation behavior and failure process of the material under different load conditions are simulated. This model can predict the fatigue life of the material under cyclic loads and evaluate the specific effects of different micro defects on fatigue life. This is crucial for optimizing the processing technology of the material and improving its reliability in high-end applications. Finite element simulation can also be used to optimize design and improve the fatigue properties of materials.

[0050] The above-described embodiments of the present application do not constitute a limitation on the scope of protection of the present application.

Claims

1. A numerical simulation method for fatigue damage of SLM-formed high-strength aluminum alloy, characterized in that: The method comprises: Step 1: Establish fatigue part model; Step 2: simulate the internal defects of the finite element model; Step 3: Conduct finite element simulation analysis to simulate the fatigue life of standard fatigue specimens; Step 4: Repeat step 3 for finite element simulation of defects at different locations and sizes, and draw stress-life curves; In step 5, based on the acquired data, the fatigue life under the conditions of large defect size, multiple defect numbers and long defect distance is predicted using a logarithmic regression method.

2. The method according to claim 1, characterized in that Step 1: Establish a fatigue part model; including: The geometric model is established according to the standard fatigue tensile specimen; When establishing the finite element model, the external thread is optimized as a cylinder in the modeling process, and is established from three aspects: thread parameters, cylinder structure and verification evaluation; Optimize thread parameters, adjust pitch, lead and diameter, and simplify tooth profile; Design cylindrical structures, determine lengths, optimize transitions, and consider tolerances; Finally, the evaluation is carried out through strength analysis and connection reliability verification.

3. The method according to claim 1, characterized in that Step 2: Simulate the internal defects of the finite element model; including: Determine defect characteristics, including size and shape, and use the software's geometry editing function to create defects at the center of the specimen. Defect types include circular pores of different radii, elliptical pores with different long and short semi-axes and angles, various surface and internal cracks, spherical inclusions with different spherical center coordinates and radii, and fibrous inclusions with different length, diameter, and orientation. For spherical defects, different radii are defined as r1, r2, r3 and other values, and the preset distances are offset along the axis of the specimen or in different radial directions, and the creation process is repeated; All existing defects are optimized into circular holes, and the shape parameters of the defects are adjusted to make them conform to the geometric characteristics of the circular holes; For the single defect model, the defect sizes are set to 0.2mm, 0.3mm, 0.4mm, 0.6mm, 0.8mm, and 1.0mm respectively to analyze the effect of defect size on fatigue life under the condition of single defect. For the case of a single defect position change, models with radial offsets of 1.5mm, 1.8mm, 2.2mm, 2.5mm, and 2.6mm were established while maintaining the defect size at 0.4mm.

4. The method according to claim 1, wherein Step 3: Conduct finite element simulation analysis to simulate the fatigue life of standard fatigue specimens, including: Fix the lower clamping end of the sample and apply pressure load on the upper end of the standard fatigue specimen; Select tetrahedron as the mesh type for finite element analysis; Select a mesh density with an element size of 0.2 mm; Apply different pressures to the same fatigue part to obtain different stress results in key parts. The actual load spectrum is converted into a stress spectrum, and the fatigue life of the fatigue part under the stress is calculated, and then the stress-life SN curve is drawn.

5. The method according to claim 1, wherein In step 5, based on the acquired data, fatigue life prediction for large defect size, multiple defects, and long defect distance is performed using a logarithmic regression method, including: First, the obtained fatigue life data is logarithmically transformed to convert it into a linear relationship; Input the known defect size and corresponding fatigue life data, and use the logarithmic regression function to fit the parameters of the SN curve; through the SN curve, predict the fatigue life under different defect sizes.

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