A Binarization-Based EBSD Image Processing and Grain Boundary Extraction Method

Abstract

The present invention discloses a method for EBSD image processing and grain boundary extraction based on binarization. The method comprises: obtaining a target file, performing data analysis on an initial grain image in the target file using target software to obtain the grain size, morphology, and grain orientation of the initial grain image, wherein the initial grain image is an image captured by EBSD; processing the initial grain image to obtain a target grain image, and defining the difference in crystal orientation angles between two adjacent grain regions in the target grain image as the grain orientation difference. The present invention solves the technical problem that the prior art crystal plasticity finite element modeling method based on EBSD data has very high requirements for the polishing and sample preparation process of metal materials, fails to process the breakpoints at discontinuous grain boundaries, and results in low computational efficiency and quality of crystal plasticity finite element simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of finite element numerical simulation, and in particular to an EBSD image processing and grain boundary extraction method based on binarization. Background Art

[0002] With the rapid development of computational geometry and computer graphics technology, as well as in-depth research on the relationship between plastic deformation and grain evolution of metallic materials at the mesoscopic scale, scholars have proposed several types of modeling methods that include grain boundary structures within polycrystals. Currently, the most widely used methods include the Voronoi diagram method and the Monte Carlo method. The Voronoi diagram divides the space into different regions around several seeds by distance. This distance-based division method makes the Voronoi diagram very effective in expressing spatial proximity. The Voronoi diagram has good flexibility and scalability in meshing and can be used to generate various types of mesh structures. This makes the Voronoi method widely used in crystal finite element modeling at home and abroad.

[0003] However, the crystal finite element model constructed using this method differs significantly from the actual grain structure. It relies on seed layout and cannot reflect the complex convex and concave contours of the actual grains, resulting in low reliability in numerical simulation predictions. This results in an inability to obtain the stress and strain results of the metal material's actual grain structure after plastic deformation when performing elastic-plastic mechanics simulations using a crystal finite element model constructed using the Voronoi diagram method. This leads to large systematic deviations between the simulation results and the experimental data, significantly limiting the verification of the authenticity and accuracy of mathematical models of crystal plastic evolution. Therefore, it is particularly necessary to develop new methods for constructing crystal plasticity finite element models to improve the accuracy of numerical calculations of crystal plastic deformation at the mesoscopic scale.

[0004] To address this issue, several researchers have developed a patent titled "A Method and Application for Finite Element Modeling of Crystal Plasticity Based on EBSD Data." Using the Channel 5 platform to analyze EBSD images and process the data in MTEX, they derived grain boundary contour data and imported it into Abaqus to establish a geometric model. This method establishes a crystal model that incorporates the material's true grain size, morphology, and orientation, improving the accuracy of crystal plasticity finite element simulations. However, this approach also has some drawbacks: First, modeling directly uses EBSD images, which places high demands on the polishing and sample preparation process for metal materials. There is no solution for the inevitable scratches that occur during sample preparation, nor for the pores created by material defects during the slicing and etching process. This extremely high demand for specimen surface quality limits the physical size of the modeled area; expanding the modeling surface will inevitably lead to the aforementioned defects. Second, the presence of small-angle grain boundaries within grains in EBSD images, as well as discontinuities at grain boundaries due to poor imaging resolution, is not addressed. These issues hinder the computational efficiency and quality of subsequent crystal plasticity finite element simulations. Summary of the Invention

[0005] The embodiment of the present invention provides an EBSD image processing and grain boundary extraction method based on binarization, which at least solves the technical problems in the prior art of crystal plasticity finite element modeling method based on EBSD data, that is, the method has very high requirements on the polishing and sample preparation process of metal materials, and the breakpoints at the discontinuities of grain boundaries are not processed, resulting in low computational efficiency and quality of crystal plasticity finite element simulation.

[0006] According to one aspect of an embodiment of the present invention, a method for EBSD image processing and grain boundary extraction based on binarization is provided. The method may include: obtaining a target file, performing data analysis on the initial grain image in the target file through target software to obtain the grain size, morphology structure and grain orientation of the initial grain image, wherein the initial grain image is an image taken by EBSD; processing the initial grain image to obtain a target grain image, defining the crystal orientation angle difference between two adjacent grain regions in the target grain image as a grain orientation difference; if the grain orientation difference is less than a preset degree, defining the grain boundary between the two adjacent grain regions in the target grain image as a small-angle grain boundary, traversing the target grain image to remove the small-angle grain boundary; when there is a breakpoint in the grain boundary between two adjacent grain regions in the target grain image, A cellular automaton repairs the breakpoints of the grain boundaries of two adjacent grain regions in the target grain image to obtain a complete target grain image; the row and column numbers of each pixel of each grain boundary and each grain region of the complete target grain image are obtained, a grain boundary unit set is created based on the row and column numbers of each pixel of each grain boundary of the complete target grain image, and a unit set of each grain region is created based on the row and column numbers of each pixel in each grain region of the complete target grain image; information of each grain boundary unit set and each grain region unit set is written into the unit set keyword corresponding to the target model file to obtain each target grain boundary unit set, each target grain region unit set and a crystal finite element model corresponding to the target model file.

[0007] Optionally, the process of processing the initial grain image to obtain the target grain image is: performing a binarization process on the initial grain image after removing noise points and filling pores and scratches to obtain the target grain image.

[0008] Optionally, defining the difference in crystal orientation angles between two adjacent grain regions in the target grain image as the grain orientation difference includes: subtracting the crystal orientation angles of two adjacent grain regions in the target grain image to obtain the grain orientation difference.

[0009] Optionally, the process of repairing the breakpoints of the grain boundary between two adjacent grain regions in the target grain image by cellular automaton to obtain a complete target grain image is: repairing the grain boundary breakpoints between two adjacent grain regions in the target grain image by grain boundary cells and cells within the grain region to obtain a complete target grain image, wherein, during the repair process, at least 5 of the 8 cell states adjacent to the grain boundary cell are cells within the grain region, 1 represents the grain boundary cell, and 0 represents the cell within the grain region.

[0010] Optionally, after obtaining each target grain boundary unit set, each target grain area unit set and crystal finite element model corresponding to the target model file, the method also includes: exporting the simulation results of the macro-basic cutting simulation of the target material, extracting the displacement data of the grid nodes of the simulation results, and filling the displacement data with spline interpolation so that the number of displacement data after interpolation and filling corresponds to the number of grid nodes at the boundary of the crystal finite element model; adding the result after spline interpolation and filling of the displacement data to the boundary condition keyword corresponding to the target model file to obtain the crystal finite element model after adding the boundary conditions.

[0011] Beneficial effects of the present invention:

[0012] The present invention proposes a binarization-based EBSD image processing and grain boundary extraction method, in which a corresponding Nset set is created with the node numbers contained in each grain area, and the node numbers belonging to the same grain are written into the Nset set using MATLAB. Each Nset set represents the creation of a mesh model of a grain, and different Nset sets are created cyclically until the construction of all grain mesh models is completed. The finite element simulation calculation of the crystal structure can solve the problem that the plastic deformation of the material microstructure is difficult to monitor through existing measuring equipment, and can more accurately predict the crystal plastic deformation and microstructure evolution process. However, the difference between the crystal finite element model and the actual grain structure size and morphology affects the accuracy of the finite element numerical calculation results. In view of the limitations of existing crystal finite element modeling, the present invention uses EBSD data to establish a finite element model based on the real polycrystalline structure at the mesoscopic scale of metal materials. The model contains actual grain size, morphology structure and orientation information, which can improve the authenticity and reliability of the crystal plastic finite element simulation calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0014] Figure 1 is a flow chart of a method for EBSD image processing and grain boundary extraction based on binarization according to an embodiment of the present invention;

[0015] Figure 2 is a schematic diagram of a process for constructing a crystal finite element model according to an embodiment of the present invention;

[0016] Figure 3 Schematic diagram of assigning boundary conditions to a finite element model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or devices.

[0019] Example 1

[0020] According to an embodiment of the present invention, a method for EBSD image processing and grain boundary extraction based on binarization is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system containing at least one set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0021] Figure 1 FIG. 1 is a flow chart of a method for EBSD image processing and grain boundary extraction based on binarization according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0022] Step S101, obtaining a target file, performing data analysis on an initial grain image in the target file by target software, and obtaining the grain size, morphology, and grain orientation of the initial grain image, wherein the initial grain image is an image taken by EBSD.

[0023] In the technical solution provided in step S101 of the present invention, the target file is a .ctf file, which is a file containing an initial grain image, and the initial crystal image is a grain metallographic diagram. The target software is AZtecCrystal, and the grain metallographic diagram in the .ctf file is processed by the AZtecCrystal software to obtain the grain size, morphology, and grain orientation of the initial grain image. Figure 2 is a schematic diagram of a process for constructing a crystal finite element model according to an embodiment of the present invention, Figure 2 (a) is the crystal metallographic image taken by Electron Backscattered Diffraction (EBSD).

[0024] Step S102 : Processing the initial grain image to obtain a target grain image, and defining the difference in crystal orientation angles between two adjacent grain regions in the target grain image as a grain orientation difference.

[0025] In the technical solution provided in the above step S102 of the present invention, the grain metallographic image is processed to obtain a target grain image, and the difference in crystal orientation angles between two adjacent grain regions in the target grain image is defined as the grain orientation difference.

[0026] Step S103 : if the grain orientation difference is less than a preset degree, the grain boundary between two adjacent grain regions in the target grain image is defined as a low-angle grain boundary, and the target grain image is traversed to remove the low-angle grain boundaries.

[0027] In the technical solution provided in the above step S103 of the present invention, when the grain orientation difference is less than a preset degree, the preset degree is 10°, the grain boundary between two adjacent grain areas in the target grain image is defined as a small-angle grain boundary, the target grain image is traversed, and all small-angle grain boundaries in the grain image are eliminated.

[0028] Step S104 : when there is a breakpoint in the grain boundary between two adjacent grain regions in the target grain image, the breakpoint in the grain boundary between the two adjacent grain regions in the target grain image is repaired by a cellular automaton to obtain a complete target grain image.

[0029] In the technical solution provided in the above step S104 of the present invention, if there is a breakpoint at the grain boundary between two adjacent grain regions in the target grain image, the breakpoint at the grain boundary between the two adjacent grain regions in the target grain image is repaired by a cellular automaton to obtain a complete target grain image.

[0030] Step S105, obtaining the row and column numbers of each pixel of each grain boundary and each grain region of the complete target grain image, creating a grain boundary unit set based on the row and column numbers of each pixel of each grain boundary of the complete target grain image, and creating a unit set of each grain region based on the row and column numbers of each pixel in each grain region of the complete target grain image.

[0031] In the technical solution provided in step S105 of the present invention, the row and column numbers of each pixel of each grain boundary and each grain region of the complete target grain image are obtained. The row and column numbers are the positions of each pixel of each grain boundary in the complete target grain image. Each pixel can be a small square. The coordinate information of the four vertices of the small square is arranged in a certain order to obtain a grain boundary unit set. The row and column numbers are the positions of each pixel in each grain region in the complete target grain image. Each pixel can be a small square. The coordinate information of the four vertices of the small square is arranged in a certain order to obtain a unit set of each grain region. For example, a grain boundary has one pixel point, and one pixel point has four nodes. The four nodes are written in a certain form, for example:

[0032] *NODE

[0033] 1,0,0,0

[0034] 2,0,a,0

[0035] 3,a,0,0

[0036] 4,a,a,0

[0037] Among them, 1, 2, 3, and 4 represent 4 nodes, (0, 0) represents the coordinates of the first node, (0, a) represents the coordinates of the second node, (a, 0) represents the coordinates of the third node, and (a, a) represents the coordinates of the fourth node. When the node only has x and y coordinates, the last column is 0. When there is a z coordinate, the last column is the z coordinate.

[0038] Rewrite *NODE form into *ELEMENT form:

[0039] *ELEMENT

[0040] 1,2,3,4

[0041] Therefore, a grain boundary has a node set of 1 pixel:

[0042] *NSET

[0043] 1,2,3,4

[0044] The set of grain boundary elements is:

[0045] *ELESET

[0046] (Unit number 1), (Unit number 2) .....(Unit number n)

[0047] Among them, one unit is a pixel point; and so on, the grain boundary unit set and the unit set of each grain area are obtained.

[0048] Step S106 , writing the information of each grain boundary unit set and each grain region unit set into the unit set keyword corresponding to the target model file, and obtaining each target grain boundary unit set, each target grain region unit set and crystal finite element model corresponding to the target model file.

[0049] In the technical solution provided in step S106 of the present invention, the information of each grain boundary unit set and each grain region unit set is written into the Nset keyword corresponding to the inp model file, and each target grain boundary unit set, each target grain region unit set and the crystal finite element model corresponding to the target model file are obtained. Figure 2 (c) is the crystal finite element model.

[0050] The above method of this embodiment is further introduced below.

[0051] As an optional embodiment, in step S102 , the process of processing the initial grain image to obtain the target grain image is as follows: performing noise removal and pore scratch filling on the initial grain image and then performing binarization processing to obtain the target grain image.

[0052] In this embodiment, the initial grain image is subjected to noise removal and pore scratch filling in MATLAB and then binarized to obtain the target grain image. Figure 2 (b) is the target grain image.

[0053] As an optional embodiment, step S102, defining the difference in crystal orientation angles between two adjacent grain regions in the target grain image as a grain orientation difference, includes: subtracting the crystal orientation angles of two adjacent grain regions in the target grain image to obtain the grain orientation difference.

[0054] As an optional embodiment, step S104, the process of repairing the breakpoints of the grain boundary between two adjacent grain regions in the target grain image by cellular automaton to obtain a complete target grain image is: repairing the grain boundary breakpoints between two adjacent grain regions in the target grain image by grain boundary cells and cells within the grain region to obtain a complete target grain image, wherein, during the repair process, at least 5 of the 8 cell states adjacent to the grain boundary cell are cells within the grain region, 1 represents the grain boundary cell, and 0 represents the cell within the grain region.

[0055] As an optional embodiment, step S106, after obtaining each target grain boundary unit set, each target grain area unit set and the crystal finite element model corresponding to the target model file, the method also includes: exporting the simulation results of the macro-basic cutting simulation of the target material, extracting the displacement data of the grid nodes of the simulation results, and filling the displacement data with spline interpolation so that the number of displacement data after interpolation and filling corresponds to the number of grid nodes at the boundary of the crystal finite element model; adding the result after spline interpolation and filling of the displacement data to the boundary condition keyword corresponding to the target model file to obtain the crystal finite element model after adding the boundary conditions.

[0056] In this embodiment, Figure 3 is a schematic diagram of the boundary conditions assigned to the finite element model according to an embodiment of the present invention, Figure 3 The orange-yellow box in the middle is spline interpolation filling, so that the number of displacement data after interpolation filling corresponds to the number of mesh nodes at the boundary of the crystal finite element model. The result of spline interpolation filling of the displacement data is added to the Boundary keyword of the .inp model to complete the boundary condition assignment of the Abaqus finite element model and obtain the crystal finite element model after adding the boundary conditions.

[0057] In an embodiment of the present invention, a target file is obtained, and data analysis is performed on an initial grain image in the target file by target software to obtain the grain size, morphology structure and grain orientation of the initial grain image, wherein the initial grain image is an image taken by EBSD; the initial grain image is processed to obtain a target grain image, and the difference in crystal orientation angles between two adjacent grain regions in the target grain image is defined as a grain orientation difference; if the grain orientation difference is less than a preset degree, the grain boundary between the two adjacent grain regions in the target grain image is defined as a small-angle grain boundary, and the target grain image is traversed to remove the small-angle grain boundary; when a breakpoint exists in the grain boundary between two adjacent grain regions in the target grain image, the breakpoint of the grain boundary between the two adjacent grain regions in the target grain image is repaired by a cellular automaton to obtain a complete target grain image; the row and column numbers of each grain boundary and each pixel of each grain region in the complete target grain image are obtained, and based on the complete target grain image A grain boundary unit set is created based on the row and column numbers of each pixel of each grain boundary, and a unit set of each grain area is created based on the row and column numbers of each pixel in each grain area of ​​the complete target grain image; the information of each grain boundary unit set and each grain area unit set is written into the unit set keyword corresponding to the target model file, and each target grain boundary unit set, each target grain area unit set and crystal finite element model corresponding to the target model file are obtained, which solves the technical problem that the crystal plasticity finite element modeling method based on EBSD data in the existing technology has very high requirements for the polishing and sampling process of metal materials and does not process the breakpoints at the discontinuity of grain boundaries, resulting in low computational efficiency and quality of crystal plasticity finite element simulation, and achieves the technical effect of reducing the requirements of the crystal plasticity finite element modeling method based on EBSD data on the polishing and sampling process of metal materials, processing the breakpoints at the discontinuity of grain boundaries, and improving the computational efficiency and quality of crystal plasticity finite element simulation.

[0058] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0059] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0061] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0062] In addition, the functional units in various embodiments of the present invention may be integrated into a first processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0063] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for EBSD image processing and grain boundary extraction based on binarization, characterized in that: include: Obtaining a target file, performing data analysis on the initial grain image in the target file using target software to obtain the grain size, morphology, and grain orientation of the initial grain image, wherein the initial grain image is an image captured by EBSD; The initial grain image is processed to obtain a target grain image, and the difference in crystal orientation angles between two adjacent grain regions in the target grain image is defined as the grain orientation difference; If the grain orientation difference is less than a preset degree, the grain boundary between two adjacent grain regions in the target grain image is defined as a low-angle grain boundary, and the target grain image is traversed to remove the low-angle grain boundary; When there is a breakpoint in the grain boundary between two adjacent grain regions in the target grain image, the breakpoint in the grain boundary between the two adjacent grain regions in the target grain image is repaired by cellular automaton to obtain a complete target grain image; Acquire the row and column numbers of each pixel of each grain boundary and each grain region of the complete target grain image, create a grain boundary unit set based on the row and column numbers of each pixel of each grain boundary of the complete target grain image, and create a unit set of each grain region based on the row and column numbers of each pixel in each grain region of the complete target grain image; The information of each grain boundary unit set and each grain region unit set is written into the unit set keyword corresponding to the target model file, and each target grain boundary unit set, each target grain region unit set and crystal finite element model corresponding to the target model file are obtained.

2. The method according to claim 1, characterized in that The process of processing the initial grain image to obtain the target grain image is as follows: The initial grain image is subjected to noise removal and pore scratch filling and then binarized to obtain the target grain image.

3. The method according to claim 1, characterized in that The crystal orientation angle difference between two adjacent grain regions in the target grain image is defined as the grain orientation difference, including: The crystal orientation angle difference between two adjacent grain regions in the target grain image is subtracted to obtain the grain orientation difference.

4. The method according to claim 1, wherein The process of patching the breakpoints of the grain boundary between two adjacent grain regions in the target grain image by cellular automaton to obtain a complete target grain image is as follows: The grain boundary breakpoints between two adjacent grain regions in the target grain image are repaired by using grain boundary cells and cells within the grain region to obtain a complete target grain image. During the repair process, at least five of the eight cell states adjacent to the grain boundary cell are cells within the grain region, 1 represents the grain boundary cell, and 0 represents the cell within the grain region.

5. The method according to claim 1, wherein After obtaining each target grain boundary unit set, each target grain region unit set, and the crystal finite element model corresponding to the target model file, the method further includes: Exporting the simulation results of the macro-basic cutting simulation of the target material, extracting the displacement data of the mesh nodes of the simulation results, and filling the displacement data with spline interpolation so that the number of displacement data after interpolation and filling corresponds to the number of mesh nodes at the boundary of the crystal finite element model; The result after the displacement data spline interpolation filling is added to the boundary condition keyword corresponding to the target model file to obtain the crystal finite element model after the boundary condition is added.

6. A computer system, characterized in that include: One or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method of claim 1.

7. A computer-readable storage medium, characterized in that Computer-executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

8. A computer program product, characterized in that The invention comprises computer executable instructions, which are used to implement the method of claim 1 when the instructions are executed.

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