Additive manufacturing material fatigue strength prediction method based on microscopic image

Abstract

The invention provides an additive manufacturing material fatigue strength prediction method based on a microscopic image, and the method comprises the steps: obtaining the basic parameters of a material sample, and enabling the material sample to be obtained through the additive manufacturing technology; a microscopic image of the surface of the material sample is obtained, and microscopic information of the material sample is further determined; a material constitutive model based on a dislocation mechanism is adopted, a crystal plasticity finite element model is constructed based on the microscopic information of the material sample, and material constitutive parameters in the constructed model are calibrated based on the basic parameters of the material sample; and carrying out multi-scale stability analysis on the crystal plasticity finite element model, judging whether the model is in a stable state or not by extracting accumulated plastic strain based on a direct periodic method during analysis, carrying out iteration based on a dichotomy to obtain a stability limit load of the model in the stable state, and taking the stability limit load as the fatigue strength of the material sample. By adopting the scheme, the fatigue strength of the additive manufacturing material can be accurately predicted.

Description

Technical Field

[0001] The present application relates to the field of fatigue strength and reliability engineering, and in particular to a method and device for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images. Background Art

[0002] With the rapid development of the transportation sector, the railway, rail transit, aerospace and other fields have put forward the sustainable development requirements of lightweight, reliable, energy-saving and environmental protection. The complexity of the structural design, load conditions and functions of the main engineering components has increased day by day. Additive manufacturing technology combined with computer-aided design, topology optimization and other technologies can flexibly realize the processing and manufacturing of complex engineering components, as well as the repair of local wear of parts. It is of great significance to the integrated and lightweight design of parts in the fields of rail transit and aerospace.

[0003] Among the commonly used metal alloy materials for additive manufacturing, aluminum-silicon alloy materials have good machinability, high specific strength, high thermal conductivity and other excellent physical and mechanical properties. Their mechanical properties are generally better than other alloy materials. They are widely used in the manufacturing of lightweight components for products and equipment in the fields of rail transportation, aerospace, etc.

[0004] In actual service, key components such as train wheel bearings, aircraft engine rotor components, etc., are in a complex working environment of high temperature, high pressure, and alternating loads for a long time, and 90% of the failures of the components are fatigue failures. This problem is more significant in additively manufactured parts. During the additive manufacturing process, the parts usually undergo rapid solidification, repeated melting, and drastic temperature gradient changes, thus forming a complex microstructure. Even under the best process conditions, additively manufactured parts will inevitably form some micron-level defects, such as pores, inclusions, unfused defects, and thermal mismatch cracks. These defects will directly affect the fatigue strength of the components, so it is very important to explore the fatigue strength of additively manufactured materials. Fatigue strength refers to the maximum stress at which a material can be subjected to an infinite number of alternating loads without being damaged.

[0005] In the past, the commonly used methods for predicting fatigue strength of metal materials were experimental method, statistical analysis method and numerical simulation method. Among them, experimental method and statistical analysis method are the mainstream means of predicting material strength. However, due to the complexity of the special microstructure and mechanical properties of additive manufacturing, it is time-consuming and costly to analyze the mechanical properties of aluminum-silicon alloy through experimental statistics, and there are great limitations in studying the influence of microstructure on performance.

[0006] The existing methods have problems such as insufficient research on the fatigue failure mechanism of additive manufacturing materials, inaccurate life prediction, and the need to combine the prediction process with fatigue testing, which is time-consuming. Summary of the invention

[0007] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0008] To this end, the first purpose of the present application is to propose a fatigue strength prediction method for additively manufactured materials based on microscopic microscopic images, thereby achieving accurate prediction of fatigue strength of additively manufactured materials.

[0009] The second object of the present application is to propose a fatigue strength prediction device for additively manufactured materials based on microscopic microscopic images.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images, comprising:

[0011] Obtaining basic parameters of a material sample, wherein the material sample is manufactured by additive manufacturing technology;

[0012] Acquire a microscopic image of the surface of the material sample, and determine microscopic information of the material sample based on the microscopic image;

[0013] A material constitutive model based on dislocation mechanism is adopted to construct a crystal plasticity finite element model based on the microscopic information of the material sample, and the material constitutive parameters in the constructed model are calibrated based on the basic parameters of the material sample;

[0014] A multi-scale stability analysis is performed on the crystal plasticity finite element model. During the analysis, based on the direct periodic method, the cumulative plastic strain is extracted to determine whether the model is in a stable state. The stability limit load of the model in a stable state is obtained based on the dichotomy iteration, and the stability limit load is used as the fatigue strength of the material sample.

[0015] Optionally, in one embodiment of the present application, obtaining basic parameters of a material sample includes:

[0016] Use the strain rate control mode to conduct tensile tests on material samples at room temperature to measure the tensile properties of the materials and obtain the basic parameters of the material samples;

[0017] The basic parameter indicators of additive manufacturing materials include elastic modulus, Poisson's ratio, ultimate tensile strength and stress-strain curve data under uniaxial tension.

[0018] Optionally, in one embodiment of the present application, obtaining a microscopic image of the surface of a material sample includes:

[0019] The surface of the material sample was photographed using scanning electron microscopy and electron backscatter diffraction technology to obtain SEM images and EBSD images.

[0020] Optionally, in one embodiment of the present application, determining microscopic information of a material sample based on a microscopic microscopic image includes:

[0021] The pore size distribution and porosity distribution of the material sample are determined based on the SEM image, and the microscopic grain information of the material sample is determined based on the EBSD image.

[0022] Optionally, in one embodiment of the present application, constructing a crystal plasticity finite element model includes:

[0023] Input the micro grain information into the finite element modeling software to construct a polycrystalline RVE model. Based on the modeling platform, provide the pore size and porosity distribution of the material sample to generate pores in the polycrystalline RVE model, and obtain a finite element model that characterizes the micro information of the material sample.

[0024] Import the finite element model into the finite element calculation software, assign values ​​to the material properties of the model, and set boundary loads.

[0025] Optionally, in one embodiment of the present application, before calibrating the material constitutive parameters in the constructed model based on the basic parameters of the material sample, the method further includes:

[0026] The parameter range is preliminarily predicted through the tensile results obtained from the experiment. An iterative model is constructed in LS-OPT, and the curve mapping method is used to optimize the fitting of the target curve within the range of the custom parameters to be calibrated until the fitting error is less than the given value to obtain the calibration parameters.

[0027] Optionally, in one embodiment of the present application, determining the fatigue strength of a material sample based on a crystal plasticity finite element model includes:

[0028] Based on the crystal plasticity finite element model, finite element simulations of different cyclic loads are carried out, and the direct periodic method is used for evaluation. During the evaluation, the steady-state response of the model is calculated, and the stable steady state of the model is found by iterative solution through time domain integration of nonlinear materials;

[0029] The change of equivalent plastic strain during the cycle is extracted, and whether the model is in a stable state is determined based on the relationship between the extracted change and the threshold. The range of the stability limit is narrowed based on the dichotomy method, and the stability limit load that makes the model in an elastic stable state is obtained through multiple iterations. The stability limit load is used as the fatigue strength of the material sample.

[0030] Optionally, in one embodiment of the present application, the method further includes:

[0031] The incremental finite element method is used to verify the shakedown limit load. The verification process includes:

[0032] The shakedown stress level predicted by the direct cycle method is applied to the material specimen using the incremental method for cyclic loading in the crystal plasticity, and the shakedown strength at the microscopic scale of the material specimen is predicted by simulating the complete number of cycles using the finite element incremental method;

[0033] The incremental method cycle simulation results are compared with those of the direct periodic method to verify the solution results of the direct periodic method.

[0034] Optionally, in one embodiment of the present application, the method further includes:

[0035] In order to solve the randomness problem caused by random distribution of grains in additive manufacturing materials, random sampling is performed on the microscopic information of material samples, and models are constructed in batches. The stability limit loads of the corresponding models are calculated respectively, and the calculation results are statistically analyzed to verify the predicted fatigue strength of the material samples from a statistical point of view.

[0036] To achieve the above-mentioned purpose, the second embodiment of the present invention proposes a fatigue strength prediction device for additive manufacturing materials based on microscopic microscopic images, comprising:

[0037] A parameter acquisition module, used to acquire basic parameters of a material sample, wherein the material sample is manufactured by additive manufacturing technology;

[0038] A microscopic information acquisition module, used to acquire a microscopic image of the surface of the material sample and determine the microscopic information of the material sample based on the microscopic image;

[0039] A material model building module, which is used to adopt a material constitutive model based on a dislocation mechanism, build a crystal plasticity finite element model based on the microscopic information of a material sample, and calibrate the material constitutive parameters in the built model based on the basic parameters of the material sample;

[0040] The prediction module is used to perform multi-scale stability analysis on the crystal plasticity finite element model. During the analysis, based on the direct periodic method, the cumulative plastic strain is extracted to determine whether the model is in a stable state, and the stability limit load of the model in a stable state is obtained based on the dichotomy iteration, and the stability limit load is used as the fatigue strength of the material sample.

[0041] The fatigue strength prediction method and device of additive manufacturing materials based on microscopic microscopic images in the embodiments of the present application, by constructing a crystal plasticity finite element model that can take into account the influence of microstructure, can take into account the anisotropy caused by factors such as grain size, grain orientation and pore defects, and provide more accurate and detailed prediction capabilities than macroscopic models. The method uses the direct periodic method to calculate the stable limit load of the material model and then predict its fatigue strength, which is verified by the incremental method. Compared with the traditional incremental method, it does not require a large number of cyclic iterations to calculate the steady-state response of the material model under cyclic loads, and can quickly obtain the fatigue strength of the material, greatly reducing the calculation time and the required computing power.

[0042] In actual engineering applications, this embodiment can accurately predict the stable limit load and fatigue strength of parts manufactured by the material based on a small amount of additive manufacturing material tests and microscopic microscopic images, with only a short working cycle.

[0043] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0045] Figure 1 A schematic flow chart of a method for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images provided in Example 1 of the present application;

[0046] Figure 2 A schematic flow chart of a fatigue strength prediction method for additive manufacturing materials provided in Example 2 of the present application;

[0047] Figure 3 This is a schematic diagram of the processing dimensions of a smooth sample manufactured by additive manufacturing according to an embodiment of the present application;

[0048] Figure 4 This is a SN curve diagram of the additively manufactured smooth specimen of the embodiment of the present application under a uniaxial tensile test at room temperature;

[0049] Figure 5 This is a SEM image obtained from a scanning electron microscope test of the additive manufacturing material of an embodiment of the present application;

[0050] Figure 6 A schematic diagram of binarization processing of an SEM image of an additive manufacturing material according to an embodiment of the present application;

[0051] Figure 7 Schematic diagram of pore equivalent radius distribution and porosity distribution of the additive manufacturing material according to an embodiment of the present application;

[0052] Figure 8 The EBSD inverse pole figure obtained by the electron backscatter diffraction test of the additive manufacturing material in the embodiment of the present application;

[0053] Fig. 9 A schematic diagram of a material crystal polycrystalline RVE model constructed based on additive manufacturing material parameters according to an embodiment of the present application;

[0054] Fig.10 A schematic diagram of boundary conditions of a polycrystalline RVE model according to an embodiment of the present application;

[0055] Fig.11 This is a comparison chart of the fitting and test results after parameter calibration of the polycrystalline RVE model of the embodiment of the present application;

[0056] Fig.12 A schematic diagram of the flow chart of the direct periodic method for calculating the shakedown limit of the model according to an embodiment of the present application;

[0057] Fig.13 It is a distribution cloud diagram of the equivalent plastic strain variation under the shakedown limit load of the embodiment of the present application;

[0058] Fig.14 This is a schematic diagram of the incremental method results of the polycrystalline RVE model in the incremental method verification of the shakedown limit in an embodiment of the present application;

[0059] Fig.15 This is a schematic diagram comparing the calculation results of the direct periodic method and the incremental method in verifying the shakedown limit by the incremental method in the embodiment of the present application;

[0060] Fig.16 A schematic diagram of a batch-generated RVE model in the statistical analysis of the dispersion of additive manufacturing materials in an embodiment of the present application;

[0061] Fig.17 A statistical histogram of the stability strength in the statistical analysis of the dispersion of additive manufacturing materials in an embodiment of the present application;

[0062] Fig.18 A structural schematic diagram of fatigue strength prediction of additively manufactured materials based on microscopic microscopic images provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0064] The following describes the fatigue strength prediction method and device of additive manufacturing materials based on microscopic microscopic images according to an embodiment of the present application with reference to the accompanying drawings.

[0065] Figure 1 A schematic flow chart of a method for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images provided in Example 1 of the present application.

[0066] like Figure 1 As shown, the fatigue strength prediction method of additive manufacturing materials based on microscopic microscopic images includes the following steps:

[0067] Step 101, obtaining basic parameters of a material sample, wherein the material sample is manufactured by additive manufacturing technology;

[0068] Step 102, obtaining a microscopic image of the surface of the material sample, and determining microscopic information of the material sample based on the microscopic image;

[0069] Step 103, using a material constitutive model based on a dislocation mechanism, constructing a crystal plasticity finite element model based on microscopic information of the material sample, and calibrating the material constitutive parameters in the constructed model based on basic parameters of the material sample;

[0070] Step 104, performing a multi-scale stability analysis on the crystal plasticity finite element model. During the analysis, based on the direct periodic method, the cumulative plastic strain is extracted to determine whether the model is in a stable state, and the stability limit load of the model in a stable state is obtained based on the dichotomy iteration, and the stability limit load is used as the fatigue strength of the material sample.

[0071] The fatigue strength prediction method of additive manufacturing materials based on microscopic microscopic images in the embodiment of the present application adopts a crystal plasticity model combined with a fatigue criterion to construct a material fatigue model, and based on direct cycle method simulation, predicts its fatigue strength from the perspective of the material's microscopic crystal structure and internal pore defects. This embodiment fully considers the influence of the microscopic crystal structure and internal defects of additively manufactured parts on their fatigue strength, and provides a complete material microscopic model construction and fatigue strength evaluation method.

[0072] Embodiment 2 of the present application also proposes a fatigue strength prediction method for additively manufactured materials based on microscopic microscopic images, which is used to evaluate the fatigue strength of aluminum alloys manufactured by additive manufacturing technology.

[0073] like Figure 2 As shown, the method includes:

[0074] S1: Use additive manufacturing technology to manufacture material samples, and obtain the stress-strain curve of the sample and basic material parameters such as elastic modulus, Poisson's ratio, and tensile ultimate strength.

[0075] In this embodiment, the strain rate control mode is used to stretch the AlSi10Mg aluminum alloy tensile specimen prepared by laser direct deposition additive manufacturing technology (LMD) at room temperature. Figure 3 As shown in Figure 1, the additively manufactured AlSi10Mg material was prepared and processed into a smooth cylindrical specimen. The tensile properties of the material were measured to obtain its elastic modulus: 83.5 GPa, Poisson's ratio 0.3, and tensile strength 175.7 MPa. The obtained material stress-strain curve is shown in Figure 1. Figure 4 shown.

[0076] In this embodiment, the chemical composition of the additively manufactured AlSi10Mg material is summarized in Table 1.

[0077] Table 1 Summary of chemical composition of LMD AlSi10Mg materials

[0078]

[0079] S2: taking a microscopic image of the sample surface to obtain microscopic information such as the metallographic structure morphology, pore size distribution, porosity, grain equivalent circle diameter, crystal orientation and Euler angle of the material at a microscopic scale.

[0080] In this embodiment, scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) are used to take microscopic images of the sample surface. The equipment used for the SEM test is a ZEISS Gemini 300 scanning electron microscope, and the sample surface needs to be pre-treated by polishing and surface corrosion before the test. The equipment used for the EBSD test is an Oxford Instruments Symmetry probe, with a high voltage of 20Kv and an aperture of 120μm. The selected test material and area are the material surface of the additively manufactured AlSi10Mg of 665×665μm with a construction direction of Y, and a step length of 1μm.

[0081] Furthermore, by observing the microscopic images of the sample surface obtained by SEM test, the metallographic structure morphology and pore defects of LMD AlSi10Mg can be obtained, such as Figure 5 As shown in Figure 2, the size of the two features is not the same, so the SEM electron microscope magnification is adjusted to 50×. Figure 5 As shown in (a), it can be observed that the pore defects inside the aluminum alloy material are randomly distributed, and the pores basically present a relatively regular spherical feature, which is judged to be caused by insufficient gas escape. Focus the electron microscope on the material matrix area and magnify the microscope to 1000×, as shown in Figure 5 As shown in (b), the image shows the metallographic structure of AlSi10Mg, including the Al-Si eutectic phase in the bright white area and the α-Al cellular phase in the gray area.

[0082] To obtain the pore size distribution and porosity in SEM images, Figure 6 As shown, the initial SEM image is cropped to a fixed size of 70*70μm, and the SEM image is filtered and converted into the internal data format of Matlab based on the Matlab image processing script, the main morphological information is extracted and the component boundaries are identified, and the original SEM is converted into a binary image, in which the pore area is saved with information "1" and other material matrices are marked as "0" areas. All binary images are analyzed, the area occupied by each pore is calculated, and the equivalent size radius of each pore is calculated according to the following formula. The distribution of the equivalent radius of the pore is statistically obtained, and the porosity of the material is calculated based on the cumulative pore area.

[0083]

[0084] Where S is the projected area of ​​a single pore, R eq is the equivalent size radius.

[0085] In this embodiment, the pore equivalent radius distribution and material porosity of the LMD AlSi10Mg material are obtained as follows: Figure 7 As shown, the equivalent radius of the pores is basically below 50πm, most of the pore radii are concentrated between 0-25πm, and the macro-average porosity of the material is 0.71%.

[0086] Furthermore, by obtaining the microscopic image of the sample surface through the EBSD test, the microstructure and grain orientation information of LMD AlSi10Mg can be obtained, such as Figure 8 The figure shows the EBSD inverse pole figure. The image is imported into the EBSD post-processing software AZtecCrystal for post-processing and grain microscopic information extraction. The specific process includes:

[0087] The AZtecCrystal software cleaning module was selected to perform noise reduction on the EBSD image, including removing abnormal peaks in the image through the Wild spike removal function and removing zero solution in the image through the Zero solution removal function.

[0088] Select the grain size module, view the equivalent circle diameter statistics in the statistical data, export the equivalent circle diameter parameters of all grains to Matlab, calculate the natural logarithm of the equivalent circle diameter of all grains through the log() function, and calculate its logarithmic mean and standard deviation through the mean() and std() functions respectively, and calculate the maximum and minimum cutoff values ​​through the following formula.

[0089] C min =[μ-log(Φ min )] / σ

[0090] Cmax =[log(Φ max )-μ] / σ

[0091] In the formula, C min is the minimum cutoff value, C max is the maximum cutoff value, μ is the logarithmic mean of the equivalent circle diameter, and σ is the logarithmic standard deviation of the equivalent circle diameter.

[0092] In this embodiment, the grain equivalent circle diameter information of the model is obtained as follows: logarithmic mean value is 2.3, logarithmic standard deviation is 0.573, minimum cutoff value is 1.48, and maximum cutoff value is 4.18.

[0093] Select the pole figure module, view the pole figures of the EBSD image, determine the main crystal orientation of the grains, save the file as ctf format and import it into Matlab, select the three columns of parameters from Euler1 to Euler3, and extract the Euler angle parameters of all grains.

[0094] S3: A crystal plasticity finite element model is constructed based on the microscopic information obtained in step S2, a material constitutive model based on a dislocation mechanism is adopted, and the material constitutive parameters in the model are calibrated according to the uniaxial tensile curve in step S1.

[0095] In this embodiment, the data obtained in step S2 is used to establish a polycrystalline RVE model in the finite element modeling software DREAM.3D. The equivalent circle diameter information of the grains obtained in S2 and the Euler angle parameters of the grains are mainly input through the StatsGenerator filter, and the edge length and resolution of the generated polycrystalline RVE model are controlled through the Initialize Synthetic Volume filter. Based on the Digimat modeling platform, the material pore size distribution and porosity distribution are provided, and two spherical pores with a diameter of about 70μm are placed in the RVE model. Finally, a finite element model that can characterize the microscopic information of the material is generated, such as Fig. 9 The RVE model used in the modeling and analysis of this embodiment adopts about 250 grains, the size of the polycrystalline model uses the C3D8R unit type, and the mesh is divided with a global unit size of 10 μm (a total of 64,000 units), and the model size is 400×400×400 μm.

[0096] This embodiment adopts a cyclic plastic constitutive model based on dislocation mechanism within the framework of crystal plasticity, and explains the macroscopic plastic deformation phenomenon of the material through the dislocation movement of the crystal from the microscopic level. Based on the microscopic deformation mechanism of LMD AlSi10Mg, the improvement measures made by using the classic Armstrong Frederick nonlinear kinematic hardening model are referred to, and the simulation calculation is performed using the ABAQUS UMAT user subroutine.

[0097] In the constitutive model of this embodiment, the material AlSi10Mg single crystal structure is a face-centered cubic structure, which contains 12 slip systems. Under the assumption of small deformation, the deformation of each grain in the crystal plasticity model is only the slip dislocation on the slip system, without lattice rotation. As shown in the following formula, the strain tensor ε can be decomposed into the elastic strain tensor ε e and the plastic strain tensor ε p :

[0098] ε=ε e +ε p

[0099] According to Hooke's law, the local stress tensor σ of a grain is g It can be calculated by the following formula: C is the fourth-order elastic modulus tensor.

[0100] σ g =C·ε e

[0101] Through coordinate transformation and tensor decomposition, the stress tensor σ g Decomposed into each slip system to form the decomposed shear stress τ that drives the slip system to slip α .

[0102] τ α =σ g :p α

[0103]

[0104] Among them, p α is the Schmid orientation factor projected onto the α slip system by the stress tensor, where α is a constant ranging from 1 to 12, representing different slip systems. α is the unit vector of the slip direction of the α slip system, n α is the normal vector of the α slip surface.

[0105] When the decomposed shear stress reaches the critical decomposed shear stress, the slip system begins to produce dislocation slip, the grains produce plastic strain, and then the material enters yield. The yield criterion adopts the extension of Schmid's law including kinematic hardening, as shown in the following formula:

[0106] f α (τ α ,x α ,τ0)=|τ α -x α |-τ0

[0107]

[0108] Among them, τ0 is the initial strength of the slip system, x α is the back stress generated by kinematic hardening, τα is the decomposed shear stress of the slip system, n and K are sensitivity coefficient parameters. This constitutive model considers that the initial strength τ0 of the slip system and the back stress x generated by kinematic hardening α The slip will be prevented from happening. The rate-dependent flow criterion is adopted in the constitutive model, and the relevant sensitivity coefficient parameters n and K are used to adjust it so that it is close to the rate-independent material behavior. In this embodiment, n is set to 20 and K is set to 1, which can make the deformation behavior of the model close to rate-independent.

[0109] Furthermore, in the model constitutive model, the plastic deformation strain of the crystal is the accumulation result of all the activated slip systems in the grain, as shown in the following formula.

[0110]

[0111] In the formula, ε p is the plastic strain rate under all slip systems, N is the number of active slip systems, and p α is the Schmid orientation factor of the stress tensor projected onto the α slip system, γ α is the slip rate on the α slip system, γ is the accumulated slip, which is the integral of the slip rate and time on all slip systems.

[0112] The self-strengthening of a slip system after a certain degree of dislocation slip accumulation is called the hardening law of the material. The kinematic hardening phenomenon on the slip system is reflected in the fact that the yield surface of the slip system begins to move in the direction of kinematic hardening. The back stress increment Δx generated by kinematic hardening is shown in the following formula.

[0113] Δx α =vγ α -bc α |γ α |

[0114]

[0115] In the formula, c, b0, b sat , γ0 is the parameter for controlling kinematic hardening, γ α is the slip rate on the α slip system, and γ is the accumulated slip. The nonlinear kinematic hardening criterion used in this embodiment adopts the classic Armstrong-Frederick model and introduces the dynamic recovery term bx α |γ α |, to meet the requirements of simulating the hardening saturation behavior of LMA AlSi10Mg.

[0116] According to the above formula, K, n, c, b0, b are newly introduced in the constitutive model of this embodiment. sat ,γ0,τ0, a total of seven parameters, K, n control the flow criterion of the material model, c, b0, b sat,γ0 controls the kinematic hardening of the material model, and τ0 controls the isotropic hardening of the material model. sat , γ0, and τ0 need to be calibrated according to the tensile stress-strain curve obtained in S1.

[0117] In this embodiment, the simulation-based optimization design tool LS-OPT software is used for multi-objective optimization. The custom interface of the large-scale optimization algorithm integration software LS-OPT is used to connect with ABAQUS, so that the tensile stress-strain curve results of the finite element simulation are directly passed into the optimization algorithm of LS-OPT and fitted with the stress-strain curve obtained from the tensile test. Parameter fitting is performed by repeatedly adjusting the parameter range and batch submission. In ABAQUS, the crystal model boundary and load are as follows: Fig.10 As shown, including:

[0118] Left boundary of the RVE model: U1=0.

[0119] Lower boundary of the RVE model: U2=0.

[0120] Right boundary of the RVE model: U3=0.

[0121] Upper boundary of the RVE model: pressure load applied.

[0122] In this embodiment, the parameters required in the constitutive model of the additively manufactured AlSi10Mg crystalline plastic material are finally set as shown in Table 2. The stress-strain curve obtained by simulation using this parameter is compared with the experimental curve. Fig.11 Show.

[0123] Table 2 Material parameters of LMD AlSi10Mg crystal plasticity constitutive model

[0124]

[0125] S4: Multi-scale shakedown analysis is performed on the crystal plasticity finite element model to evaluate fatigue strength. The shakedown analysis is carried out using finite element calculation software based on the direct periodic method. The stability state of the model is determined by extracting the cumulative plastic strain, and the corresponding stability limit load and fatigue strength of the structure in the stable state are obtained based on the bisection iteration.

[0126] In this embodiment, step S4 performs direct periodic method finite element simulation under different cyclic loads based on the finite element model and material parameters obtained in the above steps. The flow chart of direct periodic method for calculating the shakedown limit of material model is as follows: Fig.12 shown.

[0127] S401: Determine the material stability limit parameter range

[0128] In the present embodiment, the step S401 makes an initial estimate of the material stability limit. It is known from step S3 that in the constitutive parameters of the crystal plastic material of the AlSi10Mg material in the present embodiment, the initial strength value of the slip system is 20MPa, and the kinematic hardening saturation value is 54MPa. Therefore, the critical decomposition shear stress of the slip system within the grains of the additively manufactured AlSi10Mg material is equal to 20MPa, and the elastic stability strength of the additively manufactured AlSi10Mg material at the crystal scale must be greater than the critical decomposition shear stress of 20MPa. The sum of the critical decomposition shear stress of the slip system inside the crystal and the kinematic hardening saturation value is 74MPa. From the perspective of microscopic slip to macroscopic structure, when the internal strength of the crystal reaches 74MPa, the macroscopic must be much greater than 74MPa. Since the macroscopic yield strength is 4.1 times the initial strength of the slip system, the initial prediction of the elastic stability strength range of the polycrystalline model structure of the additively manufactured AlSi10Mg material is as follows:

[0129] 20MPa<σ α <304MPa

[0130] S402: Apply loads based on Abaqus and simulate cyclic loading by direct periodic method

[0131] In this embodiment, the step S402 applies loads based on the direct periodic method to simulate cyclic loading through the finite element calculation software Abaqus. The direct periodic method integrates the nonlinear material in the time domain and iteratively solves to find the stable state of the material model.

[0132] This method uses the Fourier series approximation and time integration of the nonlinear characteristics of the material, and uses the improved Newton algorithm for iteration to obtain a stable cyclic solution. Its calculation accuracy is related to the number of Fourier functions used, the number of iterations, and the evaluation of its mechanical response and the number of residual vectors during loading.

[0133] In this embodiment, in the Step module of Abaqus, a Direct cyclic analysis step is created, the cycle length is set to 1s, the incremental step is set to 0.01s, the Fourier series is set to an initial value of 11, a maximum value of 25, and a maximum number of iterations of 50, and the displacement Fourier coefficients of the previous direct cyclic iteration are passed to the next iteration.

[0134] The time-dependent displacement function and residual function in the direct periodic method are both expanded in the form of Fourier series. Therefore, the loading amplitude selected in this embodiment is the Fourier series loading method, as shown in the following formula, applying a periodic load boundary F(t).

[0135]

[0136] In this embodiment, the Create Amplitude module in Abaqus creates a sine cycle with a duration of 1 second and an amplitude of 1. The boundary setting of the model is consistent with that in step S3, and a Displacement constraint is created in the Load module to constrain the displacements of U1, U2, and U3 of the left, lower, and right boundaries of the model, respectively. A Pressure load is created and applied to the upper boundary of the model, and a sine cycle is assigned to the load to create a periodic load, and the load size is modified during the iteration process.

[0137] S403: Determine the stable state by extracting equivalent plastic strain within the cycle based on Python

[0138] In this embodiment, the step S403 extracts and stores the equivalent plastic strain variation information of the material model obtained in step S402 through a Python program, and the visualized cloud diagram is as follows: Fig.13 When the change of equivalent plastic strain in the stable cycle is within 1e-5, the model is considered to be in an elastic stable state. When the change of equivalent plastic strain in the stable cycle is greater than 1e-5 or the maximum number of iterations cannot converge, the model is considered to not meet the elastic stability and the load exceeds the stability limit.

[0139] S404: Iterate based on the binary method until the model is stable

[0140] In this embodiment, the step S404 uses a Python script to narrow the range of the stability limit based on the dichotomy method, and finally obtains the load at which the model is in an elastic stability state through multiple iterations. This load is the elastic stability limit of the model. Under this stability load, the material will not suffer from low-cycle fatigue failure, and this stability limit load is the fatigue strength of the material. In this embodiment, the fatigue strength of the material is obtained to be 50MPa, and the number of iterations obtained under different stress levels is summarized as shown in Table 3:

[0141] Table 3 Iteration times of LMD AlSi10Mg material model at different stress levels

[0142]

[0143] S5: Verify the shakedown load obtained in step S4. The incremental finite element method is used to evaluate the structural shakedown behavior by taking the cyclic mechanical response of the structure under the cyclic load with the shakedown limit load as the peak value, and random sampling modeling is performed for the randomness of the additive manufacturing material to verify the reliability of the shakedown strength prediction results from a statistical perspective.

[0144] In this embodiment, in order to prove the authenticity and reliability of the simulation results of the direct cycle method, the cyclic mechanical response of the additively manufactured AlSi10Mg polycrystalline model under the direct cycle method is verified by the traditional finite element incremental method. From step S4, the shakedown strength of the polycrystalline RVE model constructed in this embodiment predicted by the direct cycle method is 50MPa. The shakedown stress level predicted by the direct cycle method is applied to the crystal plasticity of the additively manufactured AlSi10Mg material using the incremental method for a maximum of 150 cycles. The shakedown strength of the additively manufactured AlSi10Mg microscale is predicted by simulating the complete number of cycles using the finite element incremental method.

[0145] The result of the incremental method is as follows Fig.14 As shown in Figure 2, the model begins to stabilize elastically from the second cycle, and there is no new plastic accumulation in subsequent cycles. Fig.15 As shown in the figure, the macro stress-strain results of the incremental method are consistent with the macro stress-strain results of the direct periodic method. The structure is in a stable state, and the results of the direct method solution are verified, proving that the 50MPa stable limit load of the additively manufactured AlSi10Mg material model is objectively true.

[0146] In this embodiment, the additive manufacturing material has dispersion in material properties due to grains and internal defects. Random sampling and batch modeling are used to verify the representativeness of the stability limit predicted by the present invention from a statistical perspective. Based on the microscopic information in step S2, DREAM3D randomly generates 50 polycrystalline RVE models with consistent crystal orientation density, grain equivalent diameter and anisotropy. The grain orientations of all models are randomly generated according to the orientation distribution map of EBSD. The maximum grain diameter is 241μm, the average grain diameter is 44μm, the minimum grain diameter is 18μm, and the anisotropy is 2.44:1:1. The model size is consistent with the material model constructed in step S3. The batch-generated RVE models are as follows: Fig.16 shown.

[0147] In this embodiment, the shakedown limit of the SERVEs model is calculated based on the method described in step S4, and the statistical results are as follows: Fig.17 As shown, the shakedown strength of SERVEs ranges from 45 to 52 MPa, and the shakedown strength range conforms to the normal distribution. The shakedown strength predicted by the direct period method in step S4 is within the normal distribution range, and the prediction results of the direct period method have good stability and representativeness.

[0148] The whole process constructs a material model through microscopic images, combines crystal plasticity and shakedown theory to realize the fatigue strength prediction method of additive manufacturing materials, and on this basis, uses the direct cycle method to accelerate the calculation of the fatigue strength of the material model, and verifies the fatigue strength through the incremental method and statistical analysis. This method can not only be used for fatigue strength prediction of additively manufactured aluminum-silicon alloys, but also can serve the fatigue prediction of similar metal materials.

[0149] In order to implement the above-mentioned embodiment, the present application also proposes a fatigue strength prediction device for additive manufacturing materials based on microscopic microscopic images.

[0150] Fig.18 A structural schematic diagram of fatigue strength prediction of additively manufactured materials based on microscopic microscopic images provided in an embodiment of the present application.

[0151] like Fig.18 As shown, the fatigue strength prediction device for additive manufacturing materials based on microscopic microscopic images includes:

[0152] A parameter acquisition module, used to acquire basic parameters of a material sample, wherein the material sample is manufactured by additive manufacturing technology;

[0153] A microscopic information acquisition module, used to acquire a microscopic image of the surface of the material sample and determine the microscopic information of the material sample based on the microscopic image;

[0154] A material model building module, which is used to adopt a material constitutive model based on a dislocation mechanism, build a crystal plasticity finite element model based on the microscopic information of a material sample, and calibrate the material constitutive parameters in the built model based on the basic parameters of the material sample;

[0155] The prediction module is used to perform multi-scale stability analysis on the crystal plasticity finite element model. During the analysis, based on the direct periodic method, the cumulative plastic strain is extracted to determine whether the model is in a stable state, and the stability limit load of the model in a stable state is obtained based on the dichotomy iteration, and the stability limit load is used as the fatigue strength of the material sample.

[0156] It should be noted that the above explanation of the embodiment of the method for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images is also applicable to the device for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images of this embodiment, and will not be repeated here.

[0157] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0158] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0159] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0160] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0161] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0162] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0163] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0164] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for predicting fatigue strength of additively manufactured materials based on microscopic microscopic images, characterized in that: include: Obtaining basic parameters of a material sample, wherein the material sample is manufactured by additive manufacturing technology; Acquire a microscopic image of the surface of the material sample, and determine microscopic information of the material sample based on the microscopic image; A material constitutive model based on dislocation mechanism is adopted to construct a crystal plasticity finite element model based on the microscopic information of the material sample, and the material constitutive parameters in the constructed model are calibrated based on the basic parameters of the material sample; A multi-scale stability analysis is performed on the crystal plasticity finite element model. During the analysis, based on the direct periodic method, the cumulative plastic strain is extracted to determine whether the model is in a stable state, and the stability limit load of the model in a stable state is obtained based on the dichotomy iteration, and the stability limit load is used as the fatigue strength of the material sample.

2. The method according to claim 1, characterized in that The basic parameters of the material sample are obtained, including: Performing a tensile test on the material sample at room temperature using a strain rate control mode to measure the tensile properties of the material and obtain basic parameters of the material sample; The basic parameter indicators of additive manufacturing materials include elastic modulus, Poisson's ratio, ultimate tensile strength and stress-strain curve data under uniaxial tension.

3. The method according to claim 2, characterized in that The step of obtaining a microscopic image of the surface of the material sample comprises: The surface of the material sample was photographed using scanning electron microscopy and electron backscatter diffraction technology to obtain SEM images and EBSD images.

4. The method according to claim 3, characterized in that Determining the microscopic information of the material sample based on the microscopic microscopic image comprises: The pore size distribution and porosity distribution of the material sample are determined according to the SEM image, and the microscopic grain information of the material sample is determined according to the EBSD image.

5. The method according to claim 4, characterized in that Construct a crystal plasticity finite element model, including: Inputting the microscopic grain information into finite element modeling software to construct a polycrystalline RVE model, providing pore size and porosity distribution of the material sample based on the modeling platform to generate pores in the polycrystalline RVE model, and obtaining a finite element model that characterizes the microscopic information of the material sample; Import the finite element model into the finite element calculation software, assign values ​​to the material properties of the model, and set boundary loads.

6. The method according to claim 2, characterized in that Before calibrating the material constitutive parameters in the constructed model based on the basic parameters of the material specimen, it also includes: The parameter range is preliminarily predicted through the tensile results obtained from the experiment. An iterative model is constructed in LS-OPT, and the curve mapping method is used to optimize the fitting of the target curve within the range of the custom parameters to be calibrated until the fitting error is less than the given value to obtain the calibration parameters.

7. The method according to claim 1, characterized in that Determining the fatigue strength of a material sample based on the crystal plasticity finite element model includes: Based on the crystal plasticity finite element model, finite element simulations of different cyclic loads are performed, and the direct periodic method is used for evaluation. During the evaluation, the steady-state response of the model is calculated, and the stable steady state of the model is found by iterative solution through time domain integration of nonlinear materials; The change in equivalent plastic strain during the cycle is extracted, and based on the relationship between the extracted change and the threshold, it is determined whether the model is in a stable state. The stability limit range is narrowed based on the dichotomy method, and the stability limit load that makes the model in an elastic stable state is obtained through multiple iterations, and the stability limit load is used as the fatigue strength of the material sample.

8. The method according to claim 1, characterized in that The method further comprises: The incremental finite element method is used to verify the shakedown limit load, wherein the verification process includes: The shakedown stress level predicted by the direct cycle method is applied to the material specimen using the incremental method for cyclic loading in the crystal plasticity, and the shakedown strength at the microscopic scale of the material specimen is predicted by simulating the complete number of cycles using the finite element incremental method; The incremental method cycle simulation results are compared with those of the direct periodic method to verify the solution results of the direct periodic method.

9. The method according to claim 1, characterized in that The method further comprises: In order to solve the randomness problem caused by random distribution of grains in additive manufacturing materials, random sampling is performed on the microscopic information of material samples, and models are constructed in batches. The stability limit loads of the corresponding models are calculated respectively, and the calculation results are statistically analyzed to verify the predicted fatigue strength of the material samples from a statistical point of view.

10. A fatigue strength prediction device for additive manufacturing materials based on microscopic microscopic images, characterized in that: include: A parameter acquisition module, used to acquire basic parameters of a material sample, wherein the material sample is manufactured by additive manufacturing technology; A microscopic information acquisition module, used to acquire a microscopic image of the surface of the material sample and determine the microscopic information of the material sample based on the microscopic image; A material model building module, which is used to adopt a material constitutive model based on a dislocation mechanism, build a crystal plasticity finite element model based on the microscopic information of a material sample, and calibrate the material constitutive parameters in the built model based on the basic parameters of the material sample; The prediction module is used to perform multi-scale stability analysis on the crystal plasticity finite element model. During the analysis, based on the direct periodic method, the cumulative plastic strain is extracted to determine whether the model is in a stable state, and the stability limit load of the model in a stable state is obtained based on the dichotomy iteration, and the stability limit load is used as the fatigue strength of the material sample.

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