Local morphology difference RVE based on self-programming and modeling method and application thereof

Abstract

The invention discloses a local morphology difference RVE model based on self-programming and a modeling method and application thereof. According to the method, surface residual stress of a target alloy is removed, a target alloy surface crystallography related data set is obtained through a scanning electron microscope, a target alloy characteristic distribution diagram is obtained through visual analysis, and then obtained crystallography data is analyzed to obtain a data set A containing phase numbers and a data set B containing phase positions, numbers and Euler angles; and calling a daask library to read the data set B, outputting crystal grain numbers of the region of interest in the data set B as a data set C through an ASCII format, then reading the data set A and the data set C, and obtaining a data set D with local morphology difference through differentiated editing of the region of interest. The model is used for crystal plasticity finite element simulation of local mechanical response, a comparable local morphology difference RVE model is constructed, and a technical support is provided for exploring the influence of the crystal grain morphology on the material deformation behavior.

Description

Technical Field

[0001] The invention relates to an alloy crystal plasticity model, in particular to a local morphology difference RVE based on self-programming and a modeling method and application thereof, belonging to the technical field of alloy numerical simulation analysis. Background Art

[0002] Crystal plasticity is a theory that describes the deformation mechanism of crystals based on the framework of continuum mechanics, and is an important theoretical basis for finite element analysis of crystal plasticity. Crystal plasticity assumes that crystal materials undergo elastic deformation and rotation, while plastic deformation is caused by the movement of dislocations along the crystal slip system, which associates the deformation mechanism of materials with macroscopic mechanical responses; the finite element analysis method is a numerical technique for solving partial differential equations, which discretizes the study area into multiple grids and solves the approximate solution on each grid point. Finite element analysis of crystal plasticity combines the above two theories and is widely used to simulate the deformation behavior of crystal materials, predict and analyze the microscopic strain, stress value and damage-sensitive area of ​​materials at the grain scale under different loading conditions, such as compression, tension and cyclic loading. At the same time, the finite element method of crystal plasticity can also be combined with other multi-scale simulation methods, such as molecular dynamics simulation and macroscopic mechanical simulation, to achieve multi-scale simulation and analysis from micro to macro.

[0003] Accurate crystal plasticity model and representative volume element (RVE) are the key to crystal plasticity model. Domestic and foreign scholars have conducted extensive research on how to establish a real RVE. At present, there are mainly Voronoi method, open source software method (Neper, DREAM3D) and microstructure image-based method, etc. Among them, the grain morphology output by the first two is mostly regular polygons, and the grain reduction is poor. The microstructure image-based method can more accurately reflect the characteristic information of crystals in the microstructure, such as crystal size, distribution mode, grain boundary state, etc., and therefore has gradually been widely adopted.

[0004] Chinese patent (CN112712860A) discloses a grain finite element modeling method based on the real metallographic structure. The patent extracts the grain position and shape diagram by grayscale binarization of the metallographic photo. However, the image processing process in the patent is too cumbersome, and the distortion of the obtained results is relatively serious. Chinese patent (CN118395786A) discloses a crystal plasticity finite element modeling method and application based on EBSD data. On the basis of the original, the patent derives the grain boundary contour based on EBSD data, and then further stretches and assigns material properties in ABAQUS software. However, although this method can obtain more crystal structure information in the alloy, its modifiability is poor, and it is impossible to effectively differentiate the local alloy, and it is difficult to accurately and truly reflect the influence of the local morphology difference of the alloy on the mechanical response. Summary of the invention

[0005] In view of the problems existing in the prior art, the first object of the present invention is to provide a local morphology difference RVE modeling method based on self-programming. The method is based on the alloy surface crystallography related data set collected by EBSD, and by combining self-programming scripts with open source software, it realizes the local differential editing of the alloy and constructs a comparable RVE model with morphology difference.

[0006] The second object of the present invention is to provide a local morphology difference RVE model based on self-programming. The model obtained by the above method can greatly reduce the operating cost on the one hand, and on the other hand, it also ensures that the microstructure information of the subject comes from the real material, providing technical support for exploring the influence of grain morphology on material deformation behavior.

[0007] The third object of the present invention is to provide an application of a local morphology difference RVE modeling method based on self-programming, which is used for crystal plasticity finite element analysis of local micromechanical response of alloys. Based on the excellent differential editing characteristics of the above method, accurate simulation of microscopic deformation of alloy materials can be achieved, and the model is used for local crystal plasticity finite element analysis of alloys, which can achieve accurate reproduction of micromechanical response of grain scale of alloys with different morphologies under different strain amounts.

[0008] In order to achieve the above technical objectives, the present invention provides a local morphology difference RVE modeling method based on self-programming, comprising:

[0009] Step S1, removing the surface residual stress of the target alloy, obtaining the target alloy surface crystallography related data set through scanning electron microscopy, and then obtaining the target alloy characteristic distribution map through visual analysis;

[0010] Step S2, performing source data analysis on the target alloy surface crystallography related files obtained in step S1, and obtaining a data set A containing phase numbers and a data set B containing phase positions, numbers and Euler angles;

[0011] Step S3, calling the damask library to read the data set B obtained in step S2, and outputting the grain numbers of the region of interest therein as data set C in ASCII format;

[0012] Step S4, read the data set A and the data set C, and obtain the data set D with local morphological differences by differential editing of the region of interest, that is,

[0013] The differential editing method is: replacing or adding specific phase or grain numbers in the region of interest.

[0014] Differential editing is one of the important innovations of the model of the present invention. It keeps the main information from the experimental data unchanged and only changes the local grain or grain boundary state. The RVE constructed by this method not only has morphological differences but also has strong comparability.

[0015] As a preferred solution, the method for removing the surface residual stress is: using 80-3000 mesh sandpaper to grind and polish step by step, and then sequentially performing rough polishing and sub-ion polishing.

[0016] As a preferred solution, the target alloy surface crystallography-related data set is obtained by a SEM electron microscope equipped with an EBSD probe, which consists of .cpr, .crc and .ctf files containing alloy surface grain morphology, grain orientation and grain position.

[0017] As a preferred solution, the target alloy characteristic distribution diagram includes a pole figure, an inverse pole figure and a phase distribution diagram.

[0018] As a preferred solution, the target alloy characteristic distribution map is obtained by visualization through AZtecCrystal analysis software.

[0019] As a preferred solution, before analyzing the source data in step S2, it is also necessary to load the target alloy surface crystallography-related data set through DREAM3D software.

[0020] As a preferred solution, the process of source data analysis is: performing noise reduction processing on the original data and extracting characteristic data such as grains, phases and Euler angles.

[0021] As a preferred solution, the process of calling the damask library to read the data set B is: calling damask.Grid.load_DREAM3D to read the data set B through a self-programmed Python script.

[0022] As a preferred solution, the differential editing process is performed by reading dataset A and dataset C through a self-programmed Matlab script.

[0023] As a preferred solution, the dataset D can also be visually edited, and the visual editing process is: calling damask.Grid.load_DREAM3D through a self-programmed Python script to read the dataset D, and then using AZtecCrystal analysis software to obtain visualization.

[0024] The present invention also provides a local morphology difference RVE model based on self-programming, obtained by any of the modeling methods described above

[0025] The present invention also provides an application of a local morphology difference RVE model based on self-programming, which is used for crystal plasticity finite element analysis of local micromechanical response of alloys.

[0026] Compared with the prior art, the beneficial technical effects of the technical solution of the present invention are:

[0027] 1) The differentiated model provided by the present invention is based on the alloy surface crystallography-related data set collected by EBSD. By combining self-programming scripts with open source software, it realizes the local differentiated editing of the alloy and constructs a comparable RVE model with morphological differences. On the one hand, this model greatly reduces the operating cost, and on the other hand, it ensures that the microstructure information of the subject comes from real materials, providing technical support for exploring the influence of grain morphology on material deformation behavior.

[0028] 2) The technical solution provided by the present invention is based on the excellent differentiated editing characteristics of the above-mentioned model, which can realize the accurate simulation of the microscopic deformation of alloy materials. The model is used for the crystal plasticity finite element analysis of the local microscopic mechanical response of the alloy, which can realize the accurate reproduction of the microscopic mechanical response of grain scale of alloys with different morphologies under different strain amounts. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a characteristic distribution diagram of the target alloy provided in Example 1 of the present invention;

[0030] in, Figure 1 (a) is the IPF diagram, Figure 1 (b) is the phase distribution diagram, Figure 1 (c) is the tungsten phase pole diagram, Figure 1 (d) is the pole figure of the binder phase;

[0031] Figure 2 The three RVE models and their corresponding pole figures provided in Example 1 of the present invention;

[0032] Figure 2 (a) and Figure 2 (d) RVE model and pole figure containing grain boundaries and twin boundaries in the binder phase; Figure 2 (b) and Figure 2 (e) RVE model and pole figure without grain boundaries in the binder phase; Figure 2 (c) and Figure 2 (f) RVE model and pole figure of tungsten particle phase added to the binder phase;

[0033] Figure 3 is the strain ε obtained by CPFEM simulation when the compression strain of alloys with different morphologies is 25% in Example 1 of the present invention VM Distribution cloud map; Figure 3 (a) is the strain ε of GB model VMDistribution cloud map, Figure 3 (b) is the strain ε of the NGB model VM Distribution cloud map, Figure 3 (c) is the strain ε of NGB-A model VM Distribution cloud map;

[0034] Figure 4 is the ε of the tungsten phase and the binder phase of the three alloys with different morphologies under different strains in Example 1 of the present invention. VM Distribution curve graph;

[0035] Figure 4 (a) is the strain value ε when 6% VM Distribution curve graph, Figure 4 (b) is the strain ε when the strain is 12% VM Distribution curve graph, Figure 4 (c) is the strain ε when the strain is 25% VM Distribution curve graph;

[0036] Figure 5 The ε inside the tungsten phase and the binder phase of the three alloys with different morphologies under different strains in Example 1 of the present invention VM Strain line graph;

[0037] Figure 5 (a) ε inside the tungsten phase and the binder phase VM Line graph of maximum strain value, Figure 5 (b) ε inside the tungsten phase and the binder phase VM Line graph of strain mean values. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] The present invention provides a local morphology difference RVE modeling method based on self-programming, comprising:

[0041] Step S1: Cut the tungsten alloy prepared by liquid phase sintering, and grind and polish it with 80#, 200#, 400#, 800#, 1500# and 3000# sandpaper, then perform rough polishing, and then obtain a high-flatness EBSD test sample through sub-ion polishing. Select a 450μm×450μm area in the SEM electron microscope equipped with an EBSD probe, with a scanning step of 0.5μm, and select the detection phases of pure tungsten phase and bonding phase. After the test is completed, export the data file to obtain .cpr, .crc and .ctf files, and use AZtecCrystal analysis software to visualize the following Figure 1 The IPF diagram, phase distribution diagram, and pole figure are shown.

[0042] Step S2: Load the .ctf file through DREAM3D software. Perform screening, noise removal and other analysis on the source data, and output A.txt (containing the phase number) and B.dream3d (containing information such as phase position, number and Euler angle);

[0043] Step S3: Write a Python script, call the damask library, use damask.Grid.load_DREAM3D to read the data in B.dream3d, and output the grain numbers of the area of ​​interest in ASCII format to the C.txt file. The file contains the numbers of different grains of tungsten phase and binder phase in the selected area space.

[0044] Step S4: Write a Matlab script to read A.txt and C.txt, replace all the random grain numbers representing the bonding phase with a single grain number, output the grain number to get the D.txt file, write a Python script, call the damask library, use damask.Grid.load_ASCII to read the data in D.txt respectively, and use Paraview software to visualize the D.vti file, as shown in the following figure: Figure 2 (b) is shown and named as NGB.

[0045] Example 2

[0046] This embodiment is exactly the same as the embodiment 1, except that: Step S4: Write a Matlab script, read A.txt and C.txt, replace all the random grain numbers representing the bonding phase with one grain number, select an appropriate bonding phase region position, set a diameter of appropriate size, set the position as the grain number of the tungsten phase, output the D.txt file, write a Python script, call the damask library, use damask.Grid.load ASCII to read the data in D.txt respectively, and use Paraview software to visualize the D.vti file, as shown in FIG. Figure 2(C) and named NGB-A.

[0047] Comparative Example 1

[0048] This comparative example is exactly the same as Example 1, except that: Step S4: Write a Matlab script to read A.txt and C.txt without deletion or addition, output the grain number to obtain D.txt file, write a Python script, call the damask library, use damask.Grid.load_ASCII to read the data in D.txt respectively, and use Paraview software to visualize D.vti file, as shown in FIG. Figure 2 (A) and named GB.

[0049] By comparison Figure 2 (d) and Figure 1 The pole figures in the figure are consistent, indicating that the established RVE model reproduces the grain orientation and distribution in the experiment. When the random grain numbering of the binder phase is replaced with a single grain, the binder phase orientation becomes single. On this basis, when tungsten grains are added, the pole figures of the two phases are obtained as shown in Figure 2 As shown in (f), the phenomenological crystal plasticity model is used to assign material parameters to the tungsten phase and the binder phase, respectively, and compress them along the horizontal direction.

[0050] Figure 3 is the local Mises strain ε of the three RVE alloys when the compressive strain is 25% VM Cloud diagram, from the results, we can see that there is obvious strain concentration on the bonding phase side near the tungsten / bonding phase interface, and the softer bonding phase bears greater plastic deformation. In GB alloy, due to the smaller grains of the bonding phase, there are a large number of bonding phase grain boundaries and twin boundaries, which hinder the movement of dislocations during deformation, and the strain concentration is more obvious than the latter two.

[0051] Figure 4 The ε in the tungsten phase and binder phase of the three alloys were quantified when the deformation was 6%, 12% and 25%. VM As the deformation progresses, the ε VM The strain distribution changes from a tall and thin type to a short and fat type, and the GB alloy has the greatest degree of heterogeneity.

[0052] Figure 5 The ε in the tungsten phase and binder phase of three alloys (GB, NGB and NGB-A) under different strains are shown in Figure 2. VMThe maximum value (a) and average value (b) of strain. The maximum value follows the rule: tungsten phase GB>NGB-A>NGB, bonding phase GB>NGB-A>NGB. The average value follows the rule: tungsten phase NGB>NGB-A>GB, bonding phase NGB>NGB-A>GB. The existence of bonding phase grain boundaries intensifies the W / γ deformation heterogeneity, and the bonding phase bears more deformation. With the increase of tungsten content, the proportion of bonding phase strain increases, and the local maximum strain increases.

Claims

1. A local morphology difference RVE modeling method based on self-programming, characterized in that: include: Step S1, removing the surface residual stress of the target alloy, obtaining the target alloy surface crystallography related data set through scanning electron microscopy, and then obtaining the target alloy characteristic distribution map through visual analysis; Step S2, performing source data analysis on the target alloy surface crystallography related data obtained in step S1, and obtaining a data set A containing phase numbers and a data set B containing phase positions, numbers and Euler angles; Step S3, calling the damask library to read the data set B obtained in step S2, and outputting the grain numbers of the region of interest therein as data set C in ASCII format; Step S4, read the data set A and the data set C, and obtain the data set D with local morphological differences by differential editing of the region of interest, that is, The differential editing method is: replacing or adding specific phase or grain numbers in the region of interest.

2. The local morphology difference RVE modeling method based on self-programming according to claim 1, characterized in that: The method for removing the surface residual stress is: using 80-3000 mesh sandpaper to grind and polish step by step, and then performing rough polishing and sub-ion polishing in sequence.

3. The local morphology difference RVE modeling method based on self-programming according to claim 1, characterized in that: The target alloy surface crystallography-related data set is obtained by a SEM electron microscope equipped with an EBSD probe, and is composed of .cpr, .crc and .ctf files containing alloy surface grain morphology, grain orientation and grain position.

4. The local morphology difference RVE modeling method based on self-programming according to claim 1, characterized in that: The target alloy characteristic distribution diagram includes a pole figure, an inverse pole figure and a phase distribution diagram; the target alloy characteristic distribution diagram is obtained by visualization through AZtecCrystal analysis software.

5. The local morphology difference RVE modeling method based on self-programming according to claim 1, characterized in that: Before the source data analysis in step S2, it is also necessary to load the target alloy surface crystallography related data set through DREAM3D software; the process of the source data analysis is: denoising the original data and extracting characteristic data such as grains, phases and Euler angles.

6. The local morphology difference RVE modeling method based on self-programming according to claim 1, characterized in that: The process of calling the damask library to read the data set B is: calling damask.Grid.load_DREAM3D to read the data set B through a self-programmed Python script.

7. A self-programming local morphology difference RVE modeling method according to claim 1, characterized in that: The differential editing process is performed by reading dataset A and dataset C using a self-programmed Matlab script.

8. A self-programming local morphology difference RVE modeling method according to claim 1, characterized in that: The dataset D can also be visually edited, and the visual editing process is: calling damask.Grid.load_DREAM3D through a self-programmed Python script to read the dataset D, and then using AZtecCrystal analysis software to obtain visualization.

9. A local morphology difference RVE model based on self-programming, characterized by: Obtained by the modeling method according to any one of claims 1 to 8.

10. The application of the self-programming local morphology difference RVE model according to claim 9, characterized in that: Crystal plasticity finite element analysis for local micromechanical response of alloys.

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