Metal material hole evolution behavior simulation method influenced by multi-structure characteristics

Abstract

The invention discloses a metal material hole evolution behavior simulation method influenced by multi-structure characteristics, and particularly relates to the technical field of metal material deformation damage prediction. The metal material hole evolution behavior simulation method influenced by the multi-structure characteristics comprises the following steps: (1) obtaining metal material microstructure information through characterization, and obtaining metal material hole internal distribution information through industrial CT detection; (2) constructing a representative volume unit containing multi-organization characteristics and hole distribution; (3) applying a periodic boundary condition to the RVE; (4) carrying out deformation behavior simulation calculation on the RVE under different loading conditions; and (5) obtaining the evolution behavior that the hole volume of the metal material is increased along with the deformation. The novel modeling method is developed, the second phase distribution density can be flexibly changed, non-uniform construction is achieved, the real microstructure characteristics of the metal material can be accurately reflected, and optimization of the preparation process of the high-performance metal material is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of metal material deformation damage prediction, and particularly relates to a method for simulating the pore evolution behavior of metal materials affected by multiple tissue characteristics. Background Art

[0002] With the continuous improvement of the requirements for energy conservation, emission reduction, component lightweighting, and service safety in the fields of automobiles, aerospace, ships, and nuclear power, lightweight and high-strength metal materials have attracted much attention and the demand is increasing day by day due to their comprehensive properties such as high strength, high plasticity, and low density. These widely used lightweight and high-strength metal materials are usually composed of multiple component phases. However, when preparing such materials through technologies such as casting, 3D printing, or powder metallurgy, there are often common problems such as non-uniform microstructure and pore defects. Even after multiple process optimizations and subsequent heat treatments, it is difficult to completely eliminate them, resulting in large dispersion and position differences in mechanical properties.

[0003] From the perspective of mesoscopic damage mechanics, the ductile fracture failure process of lightweight and high-strength metal materials is closely related to the nucleation, growth, and aggregation evolution behavior of micro-pores. When there are initial pores in the material, under loading, the initial pores will directly evolve, quickly reach the tolerance limit and crack, resulting in the fracture failure of the material. The evolution behavior of pores is not only affected by the load, but also factors such as the microscopic characteristics of the material around the pores (such as grain size, orientation hardness, etc.) and the characteristics of the pores themselves (such as pore size, shape, etc.) cannot be ignored. Understanding the evolution behavior of internal pores in metal materials under different conditions and its influencing laws is crucial for the accurate prediction of material failure behavior, and also has important guiding significance for regulating the microstructure to achieve service performance regulation.

[0004] At present, although techniques such as quasi-in-situ or in-situ CT can obtain the evolution of internal pores in materials and the change of porosity to a certain extent under simple loading conditions, these methods are costly and usually cannot directly establish a connection with the tissue information around the pores, making it difficult to directly reveal the influence law of the tissue characteristics around the pores on their evolution behavior. Crystal plasticity finite element simulation combines crystal plasticity theory and the finite element method, which is suitable for simulating material behavior at the mesoscopic scale. Using this method for the study of pore damage and ductile fracture can efficiently reveal the deformation characteristics of materials and the pore evolution behavior at the mesoscopic scale. On this basis, the present invention proposes a new crystal plasticity simulation method for the pore evolution behavior of metal materials considering the influence of multiple tissue characteristics, focusing on solving problems such as the construction of multiple tissue characteristics and the determination of material properties. Summary of the Invention

[0005] The object of the present invention is to provide a method for simulating the pore evolution behavior of metal materials affected by multiple tissue characteristics, which can be applied to explore the pore evolution behavior inside various metal materials prepared by technologies such as casting, 3D printing, or powder metallurgy.

[0006] A simulation method for the pore evolution behavior of metal materials affected by multi-tissue characteristics, the simulation method for the pore evolution behavior of metal materials affected by multi-tissue characteristics includes the following steps:

[0007] Step (1): Obtain the metal material microstructure information through characterization and obtain the internal pore distribution information of the metal material through industrial CT detection. The characterization methods are OM and ESBD. The metal material microstructure information includes: tissue morphology, grain size, orientation, and second-phase characteristic information. The second-phase characteristic information includes information on orientation, volume percentage, size, and distribution;

[0008] Step (2): Based on the results of the metal material microstructure information obtained through characterization and the internal pore distribution information of the metal material obtained through industrial CT detection, construct a representative volume element (RVE) that includes multi-tissue characteristics and pore distribution;

[0009] Step (3): Apply periodic boundary conditions to the RVE according to the material mechanics continuity theory;

[0010] Step (4): Combine the crystal plasticity finite element simulation method to carry out simulation calculations on the deformation behavior of the RVE under different loading conditions;

[0011] Step (5): Extract the position coordinates of all nodes on the pore boundary of the metal material during the entire deformation process, read the node coordinate data through a written Python program, and use the Convhull convex function to calculate the volume change enclosed by all nodes to obtain the evolution behavior of the metal material pore volume with the increase of the deformation amount.

[0012] Furthermore, in step (1), the highest resolution of the industrial CT needs to reach 1 μm, OM is a metallographic microscope, and ESBD is electron backscatter diffraction.

[0013] Furthermore, in step (2), the construction of the representative volume element that includes multi-tissue characteristics and pores: First, construct a three-dimensional RVE geometric model with pores according to the internal pore distribution information of the metal material obtained through industrial CT detection. After dividing the mesh of the three-dimensional RVE geometric model with pores, export the node and mesh information; Then, according to the second-phase distribution information, randomly select the corresponding number of single meshes through a running Python program to assign second-phase orientation information, and complete the construction of the second phase in sequence; Finally, according to the matrix grain size and morphology, divide the remaining meshes into different sets using the Voronoi algorithm, each set represents a grain, and assign different matrix grain orientation information to different mesh sets respectively to complete the construction of the representative volume element model that includes multi-tissue characteristics and pores.

[0014] Furthermore, when generating the second phase within the representative volume element containing multi-tissue features and pores, the generation density of the second phase in different regions can be changed according to the actual distribution difference to achieve the construction of non-uniform distribution of the second phase, so as to facilitate the study of the influence law of the change in the second phase distribution on the pore evolution behavior.

[0015] Furthermore, applying the periodic boundary conditions to the RVE in step (3) is achieved by assigning "point-to-point" periodic constraints to the nodal points on the RVE surface elements: First, use the written Python program to read in the coordinate data of all mesh nodes of the RVE; then, identify and sort the top corner nodes, edge nodes, and in-plane nodes on all outer surfaces of the RVE according to the three-dimensional coordinate sizes of the nodes to generate a node set; finally, apply "point-to-point" periodic constraints to the different node sets generated on one side surface and the corresponding node sets generated on the other side surface; specifically, the following constraint formulas are satisfied in the X, Y, and Z directions:

[0016] In the X direction:

[0017] In the Y direction:

[0018] In the Z direction:

[0019] Where: represents the overall deformation displacement component of the RVE in the X direction, represents the overall deformation displacement component of the RVE in the Y direction, represents the overall deformation displacement component of the RVE in the Z direction; u Front-surface 、u Back-surface respectively represent the deformation displacements of the corresponding nodes on the front and back surfaces of the RVE; u Left-surface 、u Right-surface respectively represent the deformation displacements of the corresponding nodes on the left and right surfaces of the RVE; u Top-surface 、u Bottom-surface respectively represent the deformation displacements of the corresponding nodes on the top and bottom surfaces of the RVE.

[0020] Furthermore, in the crystal plasticity finite element simulation method described in step (4), the strain rate-dependent crystal plasticity constitutive model is:

[0021]

[0022] h αβ =qh αα

[0023] In the formula: is the slip rate of α slip, is the slip rate of β slip, is the reference slip rate; m is the strain rate sensitivity coefficient, τ α is the resolved shear stress for the activation of the a slip system; is the accumulated slip resistance on the a slip system, which increases with the increase of strain and macroscopically manifests as the work hardening of the material, is the increment of slip resistance, γ α is the accumulated shear strain on the a slip system; is the self-hardening modulus for the activation of the slip system; is the latent hardening modulus for the activation of the slip system; h0 is the self-hardening coefficient, τ0 is the initial critical shear stress, τ s is the saturated resolved shear stress, q represents the ratio of latent hardening to self-hardening, where h0, τ0, τ s are obtained by fitting the crystal plasticity constitutive relation and the tensile experimental curve.

[0024] Furthermore, the evolution behavior of the pore volume of the metal material with the increase of the deformation amount in step (5) is quantitatively described by the pore volume change rate, as shown in the specific formula:

[0025]

[0026] In the formula: is the initial pore volume size in the RVE containing pores, which is calculated according to the generated sphere size during geometric modeling. For RVEs with different initial porosities, different f0 values correspond; is the pore volume size at each increment step. After the simulation is completed, the change information of all node coordinates on the pore boundary with the increase of the increment step is output. The Python program written is used to read in the node coordinate information and the Convhull convex function is used to calculate the volume change enclosed by all nodes, so as to obtain the evolution behavior relationship of the pore volume of the metal material with the increase of the deformation amount.

[0027] The beneficial effects of the present invention are:

[0028] 1. A three-dimensional polycrystalline material RVE modeling method considering multiple tissue characteristics is developed, which can flexibly change the second-phase distribution density, realize non-uniform construction, accurately reflect the true microstructure characteristics of metal materials, provide a reliable model basis for in-depth study of pore evolution behavior, enrich the research means of metal material damage and fracture, and provide new ideas and methods for the research in related fields.

[0029] 2. Combining the crystal plasticity finite element simulation method, it can efficiently and low-costly explore the influence laws of various microstructural characteristics and external loading changes on material deformation and pore evolution behavior from the mesoscopic scale, provide a guiding basis for regulating the size, distribution and initial pores of the second phase, help optimize the preparation process of high-performance metal materials, and improve the performance and quality of materials. Description of the Drawings

[0030] Figure 1 Flow chart of a simulation method for the pore evolution behavior of metallic materials considering the influence of multi-tissue characteristics.

[0031] Figure 2 Microstructure characterization results of A356 cast aluminum alloy in the embodiment.

[0032] Among them: (a) is the OM image of A356 cast aluminum alloy, mainly for observing and statistically analyzing the distribution and size of eutectic Si phase; (b) is the EBSD image of A356 cast aluminum alloy, from which information such as grain size and orientation can be obtained.

[0033] Figure 3 Characterization of the initial pore distribution inside A356 cast aluminum alloy by industrial CT in the embodiment.

[0034] Figure 4 Different representative volume elements (RVEs) containing multi-tissue characteristics and pores constructed in the embodiment.

[0035] Among them: it contains eutectic Si particles, polycrystalline Al matrix and initial pores. In (a), the eutectic Si phase particles are evenly distributed; in (b), the eutectic Si phase particles are unevenly distributed, and the distribution density in the area shown by the black dotted line is twice that of the other half area.

[0036] Figure 5 Results of the Mises equivalent stress and true strain distributions of the RVE with evenly distributed Si particles at a 6% tensile deformation.

[0037] Figure 6 Initial pore evolution behavior of RVEs with different tissue characteristics under uniaxial tensile loading. Specific implementation mode

[0038] The present invention will be further described below in conjunction with the embodiments and the drawings of the specification, but not limited thereto.

[0039] A simulation method for the pore evolution behavior of metallic materials affected by multi-tissue characteristics. The specific flow chart is shown in Figure 1 as follows, and the specific steps are as follows:

[0040] Step (1): Obtain the microstructure information of the metallic material through OM and EBSD characterizations and obtain the internal pore distribution information of the metallic material through industrial CT detection. The microstructure information of the metallic material includes: tissue morphology, grain size, orientation and second-phase characteristic information. The second-phase characteristic information includes information such as orientation, volume percentage, size and distribution; the highest resolution of the industrial CT needs to reach 1 μm.

[0041] Step (2): First, based on the characterization results of the pore distribution characteristics from high-resolution CT, a three-dimensional RVE geometric model with pores is constructed in finite element software according to the pore information to be studied. After meshing it, the node and mesh information is exported. Then, according to the second-phase orientation, volume fraction, size, and distribution information, a written Python program is used to select individual meshes with corresponding volume ratios and assign orientation information, and the construction of the second phase is completed in sequence. According to the matrix grain size, the number and morphology of grains contained in the three-dimensional RVE geometric model are calculated. The remaining meshes are divided into different sets with corresponding numbers using the Voronoi algorithm, and each set represents a grain. Different orientation information is assigned to different mesh sets according to the orientation information to complete the construction of the RVE model. When generating the second phase in the representative volume element containing multi-tissue characteristics and pores, the generation density of the second phase in different regions can be changed according to the actual distribution difference to achieve the construction of non-uniform distribution of the second phase, so as to facilitate the study of the influence law of the change of the second-phase distribution on the pore evolution behavior.

[0042] Step (3): According to the material mechanics continuity theory, periodic boundary conditions are applied to the RVE. The written Python program is run to read in all the mesh node coordinate data of the RVE. According to the three-dimensional coordinate sizes of the nodes, all the top corner nodes, edge nodes, and in-plane nodes on all the outer surfaces of the RVE are identified and sorted in sequence to generate a node set. For the different node sets generated on one side surface and the corresponding node sets generated on the other side surface, "point-point" periodic constraints are applied to complete the application of the periodic boundary conditions of the RVE.

[0043] Step (4): Crystal plasticity finite element simulations of the deformation behavior of the RVE under different loading conditions are carried out, where the crystal plasticity material model parameters, matrix material, and second-phase parameters are obtained by fitting the crystal plasticity constitutive relationship and the tensile experimental curve multiple times.

[0044] Step (5): After the simulation calculations under different loading conditions are completed, by extracting the position coordinates of all the nodes on the pore boundary before and after the entire deformation, the written Python program is used to read in the node coordinate information and calculate the volume change enclosed by all the nodes using the Convhull convex function to obtain the evolution behavior of the pore volume with the increase of the deformation amount.

[0045] Example 1

[0046] In this example, A356 cast aluminum alloy is selected as the research object. A356 cast aluminum alloy belongs to the Al-Si alloy system, and its microstructure mainly consists of α-Al matrix grains and eutectic Si phases, and there are easily casting pores inside the casting. The details are as follows:

[0047] First, select positions on a certain low-pressure casting A356 aluminum alloy wheel for sampling. Grind and polish the samples taken. Under non-corroded conditions, observe and take metallographic photos through OM as shown in Figure 2 (a). The statistical range of eutectic Si particle size distribution is 1μm - 30μm. Further electro-polish the samples and conduct EBSD observation, as shown in Figure 2 (b). After processing the EBSD data, the average grain size is obtained as 300μm, and the orientations are random. In addition, take samples for high-resolution CT layer scanning detection to obtain the initial internal pore distribution information, as shown in Figure 3 . The number of pores decreases significantly with the increase of the equivalent diameter, and the proportion of pores with an equivalent diameter less than 150μm is 95%.

[0048] Arbitrarily select initial pores with an equivalent diameter of 150μm for research. In the pre-processing module of the finite element software, construct a three-dimensional RVE geometric model with a 150μm diameter pore in the center, with dimensions of 1mm×1mm×1mm. Select hexahedral meshes with a size of 20μm and export the node and mesh information. Then, according to the Si phase volume fraction of 7% in the A356 alloy, use the written Python program script to randomly select the corresponding number of individual meshes and assign Si phase orientations. Each individual mesh represents a Si phase particle to complete the construction of the Si phase. Immediately afterwards, according to the average grain size of 300μm, it can be calculated that the three-dimensional RVE contains approximately 37 grains. Divide the remaining meshes into 37 sets using the Voronoi algorithm, with each set representing a grain, and randomly assign different crystal orientation information to complete the RVE construction, as shown in Figure 4 (a). In addition, when constructing the Si phase in the RVE, the distribution density can be adjusted according to the difference in the distribution of Si phase particles, as shown in Figure 4 (b). The distribution density of the Si phase in the RVE constructed in the area shown by the red dashed line is twice that of the other half area.

[0049] Next, use the written Python program script to read in all the mesh node information of the RVE. According to the three-dimensional coordinate sizes of the nodes, identify and sort the top corner nodes, edge nodes, and in-plane nodes on the outer surface of the RVE in sequence to generate a node set, and apply "point-point" periodic constraints to complete the application of the periodic boundary conditions of the RVE.

[0050] Next, apply a unidirectional tensile load to the constructed cast aluminum RVE with pores and considering multiple tissue characteristics, and carry out crystal plasticity finite element simulation. The crystal plasticity material model parameters are shown in Table 1. The material parameters of the Al matrix are obtained by fitting the crystal plasticity constitutive relationship and the tensile test results. The strength and hardness of eutectic Si are much higher than those of the Al matrix. It can be considered that only elastic deformation occurs during room temperature deformation, and much higher material plasticity model parameters than those of the Al matrix are given to the eutectic Si phase. The simulation results are as followsFigure 5 As shown, it is observed that after applying the periodic boundary conditions, the relative displacement between any node on one surface of the RVE and the corresponding point on the opposite side is restricted by the macroscopic uniform deformation of the material, which ensures that the RVE can consider non-uniform but periodically continuous substructures.

[0051] Table 1 Material parameters for crystal plasticity finite element simulation of A356 cast aluminum alloy

[0052]

[0053] Finally, after the simulation calculation is completed, by exporting the position coordinate information of all nodes on the hole boundary before and after the entire deformation, the coordinate data is read into a written Python program and the Convhull convex function is used to calculate the volume enclosed by all nodes forming the hole at each increment step, and the evolution behavior relationship of the hole volume with the increase of the deformation amount is obtained. As Figure 6 shown, for the hole evolution behavior under different Si phase particle distributions, it is found that the presence of eutectic Si leads to a faster hole growth rate under the same external conditions, and the non-uniform distribution of the Si phase results in an even more rapid hole growth rate.

[0054] Matters not covered by the present invention are well-known techniques.

[0055] The above embodiments are only used to illustrate the technical concept and features of the present invention, and their purpose is to enable those familiar with this technology to understand the content of the present invention and implement it accordingly, and should not be used to limit the protection scope of the present invention. All equivalent changes or modifications made according to the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for simulating the evolution behavior of metal material holes affected by multi-organization characteristics, characterized in that: The method for simulating the evolution behavior of metal material holes affected by multiple tissue characteristics comprises the following steps: Step (1): obtaining the microstructure information of the metal material by characterization and obtaining the internal distribution information of the pores of the metal material by industrial CT detection. The characterization methods are OM and ESBD. The microstructure information of the metal material includes: organizational morphology, grain size, orientation and second phase characteristic information. The second phase characteristic information includes orientation, volume percentage, size and distribution information; Step (2): Based on the results of characterizing the microstructure information of the metal material and the internal distribution information of the pores of the metal material obtained by industrial CT detection, a representative volume unit including multi-tissue characteristics and pore distribution is constructed, and the representative volume unit is RVE; Step (3): According to the continuity theory of material mechanics, periodic boundary conditions are applied to the RVE; Step (4): Combined with the crystal plasticity finite element simulation method, the deformation behavior of RVE under different loading conditions is simulated and calculated; Step (5): Extract the position coordinates of all nodes on the boundary of the metal material hole during the entire deformation process, read the node coordinate data through the written Python program, and use the Convhull convex function to calculate the volume change surrounded by all nodes to obtain the evolution behavior of the metal material hole volume as the deformation amount increases.

2. The method for simulating the evolution behavior of metal material holes affected by multi-organizational features according to claim 1 is characterized in that: In step (1), the highest resolution of the industrial CT needs to reach 1 μm, OM is a metallographic microscope, and ESBD is electron backscatter diffraction.

3. The method for simulating the evolution behavior of metal material holes affected by multi-organizational characteristics according to claim 1, characterized in that: The construction of a representative volume unit containing multi-tissue features and holes in step (2) is as follows: first, the internal distribution information of the holes in the metal material is obtained according to the industrial CT detection, and a three-dimensional RVE geometric model containing holes is constructed. The three-dimensional RVE geometric model containing holes is meshed and the node and mesh information is exported; then, according to the second phase distribution information, a corresponding number of single meshes are randomly selected by running the written Python program to assign the second phase orientation information, and the construction of the second phase is completed in sequence; finally, according to the size and morphology of the matrix grains, the remaining meshes are divided into different sets using the Voronoi algorithm, each set represents a grain, and different matrix grain orientation information is assigned to different mesh sets, thereby completing the construction of a representative volume unit model containing multi-tissue features and holes.

4. The method for simulating the evolution behavior of metal material holes affected by multi-organizational features according to claim 3 is characterized in that: When the second phase is generated in a representative volume unit containing multi-tissue features and pores, the second phase generation density in different regions can be changed according to the actual distribution differences to achieve the uneven distribution construction of the second phase, so as to facilitate the study of the influence of the second phase distribution change on the pore evolution behavior.

5. The method for simulating the evolution behavior of metal material holes affected by multiple tissue features according to claim 1, characterized in that: In step (3), the periodic boundary condition imposed on the RVE is achieved by assigning a "point-to-point" periodic constraint to the surface unit nodes of the RVE: first, the coordinate data of all grid nodes of the RVE are read in by using a written Python program; then, according to the size of the three-dimensional coordinates of the nodes, the vertex nodes, edge nodes and in-plane nodes on all the outer surfaces of the RVE are identified and sorted in sequence to generate a node set; finally, a "point-to-point" periodic constraint is imposed on the different node sets generated on one side of the surface and the corresponding node sets generated on the other side of the surface; specifically, the following constraint formulas are met in the three directions of X, Y and Z: X direction: Y-direction: Z direction: in: represents the overall deformation displacement component of RVE along the X direction, represents the overall deformation displacement component of RVE along the Y direction, represents the overall deformation displacement component of RVE along the Z direction; u Front-surface 、u Back-surface Respectively represent the corresponding node deformation displacements on the two surfaces before and after RVE; u Left-surface 、u Right-surface Respectively represent the corresponding node deformation displacements on the left and right surfaces of RVE; u Top-surface 、u Bottom-surface Represent the corresponding node deformation displacements on the top and bottom surfaces of the RVE respectively.

6. The method for simulating the evolution behavior of metal material holes affected by multi-organizational characteristics according to claim 1, characterized in that: In the crystal plasticity finite element simulation method described in step (4), the strain rate-dependent crystal plasticity constitutive model is: h αβ =qh αα Where: is the slip rate of a slip, is the slip rate of β slip, is the reference slip rate; m is the strain rate sensitivity coefficient, τ α is the shear stress of the a-slip system; is the cumulative slip resistance on the a slip system, which increases with the increase of strain, and manifests itself as work hardening of the material on a macro scale. is the slip resistance increment, γ α is the accumulated shear strain on the slip system a; h is the self-hardening modulus of the slip system; αβ is the latent hardening modulus of the slip system; h0 is the self-hardening coefficient, τ0 is the initial critical shear stress, τ s is the saturated shear stress, q represents the ratio of latent hardening to self-hardening, where h0, τ0, τ s It is obtained by fitting the crystal plastic constitutive relationship and the tensile test curve.

7. The method for simulating the evolution behavior of metal material holes affected by multi-organizational characteristics according to claim 1, characterized in that: The evolution of the pore volume of the metal material as the deformation increases in step (5) is quantitatively described by the pore volume change rate, as shown in the formula: Where: is the initial void volume size in the RVE containing voids, which is calculated based on the size of the sphere generated during geometric modeling. Different f0 values ​​correspond to RVEs with different initial porosities. The volume of the hole at each incremental step is output after the simulation is completed, and the coordinate change information of all nodes on the hole boundary is output as the incremental step increases. The Python program written by the application reads the node coordinate information and uses the Convhull convex function to calculate the volume change enclosed by all nodes, and the evolutionary behavior relationship of the hole volume of the metal material with the increase of deformation is obtained.

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