Microstructure digital processing method and system for EBSD result of metal material

Abstract

The invention discloses a microstructure digital processing method and system for a metal material EBSD result, and the method comprises the steps: defining the physical orientation of a material, namely describing the orientation of a crystal through three continuous rotations; performing image processing on the acquired microstructure image to obtain a distribution rule of crystal grains; the microstructure of the material is re-identified and combined, and small crystal grains which cannot be identified in EBSD data processing software are eliminated; assigning the microstructure image obtained by re-identification to obtain a material microstructure segmentation result; and linking the crystal grains with Euler angle orientation exported from EBSD data processing software to obtain orientation distribution data of the microstructure of the material. On the basis of EBSD data processing software, grain distribution and Euler angle orientation information in the EBSD data processing software are extracted, the Euler angle orientation information can be used for subsequent cellular automaton model and theoretical analysis, the resolution can be adjusted according to needs, and the recognition precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of metal material microstructure analysis, and in particular relates to a method and system for digitally processing the microstructure of metal material EBSD results. Background Art

[0002] Euler angles are important parameters used to describe the orientation of a rigid body relative to a fixed coordinate system. They fully define the object's pose in three-dimensional space by continuously rotating it around three orthogonal coordinate axes (usually the X, Y, and Z axes). This representation is not only applicable to the kinematic analysis of rigid bodies in mechanical systems, but also to coordinate transformations of dynamic reference frames in physics and the description of orientations in orthogonal basis vector spaces in linear algebra. In materials science, in addition to expressing the physical orientation of grains using a nine-element orientation matrix or polar stereographic projection, the three-parameter system of Euler angles provides a more intuitive way to quantify the spatial correspondence between the crystal coordinate system and the sample coordinate system. Once the spatial coordinate system of the sample is clearly defined, the three-dimensional coordinates of the grain center and the corresponding Euler angle parameters can be combined to fully reconstruct the spatial pose of the grain within the macroscopic sample. Electron backscatter diffraction (EBSD), a core method for modern materials characterization, can accurately determine the size distribution, morphological characteristics, and preferred orientation of each grain within a metallic material by analyzing Kikuchi diffraction patterns and matching them with crystallographic databases. The acquisition of these key microstructural parameters provides an important experimental basis for in-depth exploration of the anisotropy of the mechanical properties of metal materials, the plastic deformation mechanism, and the texture evolution law during heat treatment, and plays an irreplaceable role in optimizing material processing technology and improving product performance.

[0003] However, raw EBSD image data is large and complex. When analyzed using EBSD data processing software, problems arise, such as inaccurate identification of small grains and difficulty efficiently extracting grain orientation information. Traditional methods for grain identification and orientation analysis often require extensive manual intervention, are inefficient, and prone to human error. Therefore, there is a significant need to develop a highly automated and accurate method for identifying metal grains using EBSD images and outputting Euler angle orientations. Summary of the Invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a method and system for digitally processing the microstructure of EBSD results of metal materials, which solves the problem of inaccurate small grain identification in EBSD data processing software, improves the accuracy and completeness of grain identification, and reduces errors caused by manual intervention.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for digitally processing the microstructure of EBSD results of a metal material, comprising the following steps: Filter the microstructure EBSD image of metal materials and extract the grain edges; Based on the microstructure EBSD image of the metal material after extracting the grain edges, the grains are screened and merged; Based on the microstructure EBSD image of the metal material after screening and merging the grains, a numerical label representing the geometric characteristics and position information of the grain is assigned to each grain, and then the grains with the numerical labels are numbered; Combining the EBSD image of the metal material's microstructure after grain numbering and the Euler angle orientation data, the Euler angle orientation data is associated with the number of each grain to obtain the physical orientation of the metal material's microstructure grains; The acquired data are used as initial boundary conditions to input into the subsequent cellular automaton (CA) evolution model to realize the actual microstructure evolution model based on the CA method.

[0006] Furthermore, filtering the microstructure EBSD image of the metal material and extracting the grain edge includes: importing the collected microstructure EBSD image into image processing software, setting the size and standard deviation of the Gaussian kernel, performing a filtering operation on the microstructure EBSD image, and removing noise in the microstructure EBSD image; The Canny algorithm is used to extract edges and obtain a preliminary distribution pattern image of grains.

[0007] Furthermore, when using the Canny algorithm for edge extraction, the gradient amplitude and direction of the image pixels are calculated and the grain boundaries are determined according to the set high and low thresholds; When determining the high and low thresholds, the adaptive threshold setting method is adopted in combination with the grayscale distribution characteristics of the image.

[0008] Furthermore, based on the microstructure EBSD image of the metal material after extracting the grain edges, the grains are screened and merged, including: Calculate the grain area based on the microstructure EBSD image of the metal material after extracting the grain edge; Grains with an area smaller than the set threshold are identified as small grains that are difficult to accurately identify in EBSD data processing software; The small grains are merged with the largest grains adjacent to the small grains using an area-based merging method.

[0009] Furthermore, merging the small grain with the largest grain adjacent to the small grain using an area-based merging method includes: Determine the adjacent grains of the small grain and determine which grain to merge with by comparing the sizes of the surrounding grains; The spline interpolation algorithm is used to smooth the merged grain boundaries.

[0010] Furthermore, based on the microstructure EBSD image of the metal material after screening and merging the grains, a regional growth-based assignment algorithm is used to assign a numerical label representing the geometric characteristics and position information of each grain. Specifically, different numerical labels are assigned to different grains according to the geometric characteristics and position information of the grains and sorted by grain area. At the same time, the position coordinate information of each grain in the original EBSD image is recorded, including the center of mass coordinates and boundary coordinates.

[0011] Furthermore, associating the Euler angle orientation data with the number of each grain includes: The orientation data of the Euler angle orientation data exported from the EBSD data processing software that exceeds the set value range are marked and corrected, and a one-to-one correspondence between the grain number and the Euler angle orientation data is established. The number of each grain is associated with the corresponding Euler angle orientation data to obtain the orientation distribution data of the material microstructure.

[0012] Furthermore, the acquired data is used as initial boundary conditions to input into the subsequent cellular automation (CA) evolution model including: The merged orientation distribution data is output in npy and txt file formats, each data point of the CA model is assigned a value, and then imported into the CA model for microstructure evolution calculation.

[0013] In a second aspect, the present invention provides a system for metal material microstructure grain recognition and physical orientation output suitable for cellular automata, comprising a filtering module, a merging module, an assignment numbering module, and an association module; The filtering module is used to filter the microstructure EBSD image of metal materials and extract the grain edges; The merging module screens and merges grains based on the microstructure EBSD image of the metal material after extracting the grain edges; The numbering module assigns a numerical label representing the geometric characteristics and position information of each grain based on the microstructure EBSD image of the metal material after screening and merging the grains, and then numbers the grains with the numerical labels; The association module is used to combine the microstructure EBSD image of the metal material after the grains are numbered and the Euler angle orientation data, associate the Euler angle orientation data with the number of each grain, and obtain the physical orientation of the microstructure grains of the metal material.

[0014] In a third aspect, the present invention may also provide a computer device comprising a processor and a memory. The memory is used to store a computer executable program, and the processor reads and executes the computer executable program from the memory. When the processor executes the computer executable program, the method for metal material microstructure grain identification and physical orientation output applicable to cellular automata described in the present invention can be implemented.

[0015] At the same time, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method of metal material microstructure grain identification and physical orientation output suitable for cellular automata described in the present invention can be implemented.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: the present invention processes and reconstructs the data of the material microstructure characterization results by using the Euler angle, a method for representing the physical orientation of grains, so that the Euler angle orientation data can be used as an input parameter for other models; It effectively solves the problem of inaccurate small grain recognition in EBSD data processing software, improves the accuracy and completeness of grain identification, and reduces errors caused by manual intervention through a series of automated processing steps.

[0017] The present invention can quickly and accurately link grains with Euler angle orientation data, output orientation distribution data of the material microstructure, provide reliable data support for subsequent theoretical analysis of metal materials, and contribute to in-depth research on the relationship between the microstructure and performance of metal materials.

[0018] The method described in this paper offers high flexibility. Parameters in each step, such as the edge extraction threshold, the area threshold for small grain merging, and the orientation difference threshold, can be adjusted according to actual needs. This adapts the EBSD image analysis of different types of metal materials, improving the method's versatility. Furthermore, the recognition accuracy can be adjusted to meet the precision requirements of different research objectives. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Extract a flow chart for microstructure; Figure 2 This is a comparison chart before and after edge extraction processing; Figure 3 This is the result diagram of assignment and numbering; Figure 4 Boundary refinement map; Figure 5 This is the final result image of region re-identification and merging. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] The present invention provides a method for digitally processing the microstructure of metal material EBSD results, comprising the following steps: Filter the microstructure EBSD image of metal materials and extract the grain edges; Based on the microstructure EBSD image of the metal material after extracting the grain edges, the grains are screened and merged; Based on the microstructure EBSD image of the metal material after screening and merging the grains, a numerical label representing the geometric characteristics and position information of the grain is assigned to each grain, and then the grains with the numerical labels are numbered; Combining the EBSD image of the metal material's microstructure after grain numbering and the Euler angle orientation data, the Euler angle orientation data is associated with the number of each grain to obtain the physical orientation of the metal material's microstructure grains; The acquired data are used as initial boundary conditions to input into the subsequent cellular automaton (CA) evolution model to realize the actual microstructure evolution model based on the CA method.

[0022] First, through grain edge extraction and optimization, the structural recognition accuracy of EBSD images is enhanced, providing reliable basic data for subsequent analysis. Second, the geometric feature encoding is innovatively integrated with crystallographic orientation data, and a multi-dimensional grain feature database is constructed through a numerical labeling system. Finally, through the design of a standardized data output interface, a seamless connection between experimental characterization data and cellular automaton models is achieved, effectively solving the problem of idealized boundary conditions in traditional simulations. This full-chain processing method significantly improves the simulation fidelity of microstructural evolution. While maintaining the original topological structural characteristics of the material, it achieves the coordinated transmission of multi-physics field data such as grain orientation and geometric morphology, providing a more accurate digital foundation for material property prediction.

[0023] refer to Figure 1 The present invention provides a method and system for digitally processing the microstructure of metal material EBSD results, comprising the following steps: Step 1: Edge extraction processing After obtaining the microstructure EBSD image of the metal material, the microstructure EBSD image is preprocessed, and the Gaussian filtering algorithm is used to remove the noise in the microstructure EBSD image to improve the image quality. Specifically, the collected microstructure EBSD image is imported into the image processing software, the size of the Gaussian kernel is set to 3×3, the standard deviation is set to 1.0, and the microstructure EBSD image is filtered. The Canny algorithm is used for edge extraction. The algorithm determines the grain boundary according to the set high and low thresholds by calculating the gradient amplitude and direction of the image pixels. When determining the high and low thresholds, the grayscale distribution characteristics of the image are combined, and an adaptive threshold setting method is used. For example, the threshold is dynamically adjusted according to the mean and standard deviation of the image grayscale. After many experiments, the high threshold is determined to be 0.8 and the low threshold is determined to be 0.3, so as to accurately obtain the preliminary distribution law image of the grains, laying the foundation for subsequent analysis, reference Figure 2 .

[0024] EBSD data includes grain distribution information, physical orientation, and the phase to which the grains belong. Physical orientation is the Euler angle, which is a group of three independent angular parameters used to uniquely determine the position of a fixed-point rotating rigid body. Effective processing of EBSD images of metal materials is achieved through Gaussian filtering and Canny edge detection algorithms. Gaussian filtering smoothes image noise while maintaining the integrity of the grain boundary structure by adjusting the kernel size and standard deviation parameters, providing clear input data for subsequent edge detection. Combined with the Canny algorithm, through gradient calculation, non-maximum suppression and double threshold processing, it accurately locates the grain boundary edge while suppressing residual noise interference, generating a high-contrast grain distribution map, optimizing the problems of noise sensitivity and boundary blur in traditional edge detection, forming a standardized image preprocessing process, and laying a reliable image foundation for quantitative analysis of microstructures.

[0025] Step 2: Re-identification and merging The area threshold is set to 200 pixels. By calculating the area of the grains in the preliminary microstructure image obtained in step 1, the grains with an area smaller than the threshold are identified as small grains that are difficult to accurately identify in the EBSD data processing software. In the image analysis software, the area of each closed area is determined by the connected area marking method, and small areas with an area smaller than the threshold are screened out. The area-based merging method is used to merge the small grains with the largest adjacent grains. Specifically, in the merging process, the adjacent areas of the small area are first determined, and the other areas existing in the neighborhood of all boundary pixels in the area are recorded. By comparing the sizes of these areas, the grain is merged with the largest area, and the spline interpolation algorithm is used to smooth the merged grain boundaries to ensure the continuity and rationality of the grain boundaries and obtain a more accurate microstructure image. Figure 4 .

[0026] Step 3: Assign values and number Assign values to the microstructure image after re-identification in step 2. According to the geometric characteristics and position information of the grains, sort them according to the area size obtained by grain statistics and assign different numerical labels to different grains. During the assignment process, the position coordinate information of each grain in the original EBSD image is recorded at the same time, including the centroid coordinates and boundary coordinates. After the assignment is completed, each grain is individually numbered to build a grain number database, which contains information such as grain number, grain numerical label, position coordinates, etc., to facilitate the subsequent management and analysis of the grains. Figure 3 Standardized management of microstructural features is achieved through numerical labeling and spatial information archiving. A label assignment system based on grain area sorting helps establish visual coding rules for grain size distribution. The synchronous recording of coordinate information binds geometric features with spatial distribution data to form a multi-dimensional spatial dataset that includes centroid positioning and boundary morphology. The construction of a grain numbering database enables rapid retrieval and association analysis of structural characteristic parameters through a structured storage model. The mapping relationship between numerical labels, spatial coordinates, and original image positions provides a topological analysis basis for studies such as grain evolution tracking and regional characteristic comparison.

[0027] Step 4: Link with Euler Angle Orientation Link the grains numbered in step 3 with the Euler angle orientation data exported from the EBSD data processing software. First, verify and correct the exported Euler angle orientation data to check the integrity and rationality of the Euler angle orientation data, such as checking whether the Euler angle value range is within [-180°, 180°] × [-180°, 180°] × [-180°, 180°]. By establishing a one-to-one correspondence between the grain number and the Euler angle orientation data, the number of each grain is associated with the corresponding Euler angle orientation data to obtain the orientation distribution data of the material microstructure. Figure 5 Finally, the data after associating each grain number with the corresponding Euler angle orientation data is stored as a txt format file, so that further theoretical analysis using cellular automata can be carried out, such as studying the relationship between grain orientation and material properties.

[0028] The obtained grain orientation distribution data is analyzed to calculate parameters such as the average size of the grains and the uniformity of the orientation distribution. The data can be input as boundary conditions of the cellular automaton. The results obtained by the method of the present invention are compared with the traditional method. The results show that the method of the present invention has significant improvements in the accuracy of grain identification and the reliability of orientation information extraction. For example, the traditional method has a high misjudgment rate for small grains, while the method of the present invention effectively reduces the misjudgment rate of small grains and improves the accuracy of grain identification through re-identification and merging operations. At the same time, in terms of orientation information extraction, the method of the present invention links the grains with the Euler angle orientation data when outputting the results, which serves as the initial boundary conditions that can be subsequently input into the cellular automaton model for microstructural evolution, providing more specific data support for the microstructural research of metal materials.

[0029] In Example 2, the present invention provides a digital processing system for the microstructure of EBSD results of metal materials, comprising a filtering module, a merging module, a value numbering module, and an association module; The filtering module is used to filter the microstructure EBSD image of metal materials and extract the grain edges; The merging module screens and merges grains based on the microstructure EBSD image of the metal material after extracting the grain edges; The numbering module assigns a numerical label representing the geometric characteristics and position information of each grain based on the microstructure EBSD image of the metal material after screening and merging the grains, and then numbers the grains with the numerical labels; The association module is used to combine the microstructure EBSD image of the metal material after the grains are numbered and the Euler angle orientation data, associate the Euler angle orientation data with the number of each grain, and obtain the physical orientation of the microstructure grains of the metal material.

[0030] The present invention also provides a computer device comprising a processor and a memory. The memory is configured to store a computer-executable program, and the processor reads and executes the computer-executable program from the memory. When the processor executes the computer-executable program, the method for metal material microstructure grain identification and physical orientation output applicable to cellular automata described in the present invention can be implemented.

[0031] On the other hand, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the method of metal material microstructure grain identification and physical orientation output suitable for cellular automaton described in the present invention.

[0032] The computer device may be a laptop computer, a desktop computer or a workstation.

[0033] The processor of the present invention may be a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC) or a readily available field programmable gate array (FPGA).

[0034] The memory of the present invention may be an internal storage unit of a laptop computer, desktop computer or workstation, such as a memory or a hard disk; or an external storage unit, such as a mobile hard disk or a flash memory card.

[0035] Computer-readable storage media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSD) or optical disks, etc. Among them, random access memory may include resistance random access memory (ReRAM) and dynamic random access memory (DRAM).

[0036] In summary, the proposed method for digitally processing the microstructure of metallic materials using EBSD results efficiently and accurately obtains grain information and orientation distribution data, providing a powerful technical tool for the research and application of metallic materials. The method can flexibly adjust parameters based on different experimental requirements and material properties, and has broad application prospects.

[0037] The above content is only for explaining the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for digitally processing the microstructure of metal material EBSD results, characterized in that: The following steps are involved: Filter the microstructure EBSD image of metal materials and extract the grain edges; Based on the microstructure EBSD image of the metal material after extracting the grain edges, the grains are screened and merged; Based on the microstructure EBSD image of the metal material after screening and merging the grains, each grain is assigned a numerical label representing the geometric characteristics of the grain, and then the grains with assigned numerical labels are numbered; Combining the EBSD image of the metal material's microstructure after grain numbering and the Euler angle orientation data, the Euler angle orientation data is associated with the number of each grain to obtain the physical orientation of the metal material's microstructure grains.

2. The method for digitally processing the microstructure of EBSD results of a metal material according to claim 1, characterized in that: Filtering the microstructure EBSD image of a metal material and extracting grain edges includes: importing the collected microstructure EBSD image into image processing software, setting the size and standard deviation of the Gaussian kernel, performing a filtering operation on the microstructure EBSD image, and removing noise from the microstructure EBSD image; The Canny algorithm is used to extract edges and obtain a preliminary distribution pattern image of grains.

3. The method for digitally processing the microstructure of EBSD results of a metal material according to claim 2, characterized in that: When using the Canny algorithm for edge extraction, the grain boundaries are determined by calculating the gradient amplitude and direction of the image pixels according to the set high and low thresholds; When determining the high and low thresholds, the adaptive threshold setting method is adopted in combination with the grayscale distribution characteristics of the image.

4. The method for digitally processing the microstructure of metal material EBSD results according to claim 1, characterized in that: Based on the microstructure EBSD image of the metal material after extracting the grain edges, the grains are screened and merged, which includes: Calculate the grain area based on the microstructure EBSD image of the metal material after extracting the grain edge; Grains with an area smaller than the set threshold are identified as small grains that are difficult to accurately identify in EBSD data processing software; The small grains are merged with the largest grains adjacent to the small grains using an area-based merging method.

5. The method for digitally processing the microstructure of EBSD results of a metal material according to claim 4, characterized in that: Merging the small grain with the largest grain adjacent to the small grain using an area-based merging method includes: Determine the adjacent grains of the small grain and determine which grain to merge with by comparing the sizes of the surrounding grains; The spline interpolation algorithm is used to smooth the merged grain boundaries.

6. The method for digitally processing the microstructure of metal material EBSD results according to claim 1, characterized in that: Based on the microstructure EBSD image of the metal material after screening and merging the grains, a regional growth-based assignment algorithm is used to assign a numerical label representing the geometric characteristics of each grain. Specifically, different numerical labels are assigned to different grains according to the geometric characteristics and position information of the grains and the grain area. While assigning the labels, the position coordinate information of each grain in the original EBSD image is recorded, including the center of mass coordinates and boundary coordinates.

7. The method for digitally processing the microstructure of EBSD results of a metal material according to claim 1, characterized in that: Associating Euler angle orientation data with each grain number involves: Using the Euler angle orientation data exported from the EBSD data processing software, a one-to-one correspondence between the grain number and the Euler angle orientation data is established. The number of each grain is associated with the corresponding Euler angle orientation data to obtain the orientation distribution data of the material microstructure.

8. A microstructure digital processing system for EBSD results of metal materials, characterized by: Including filtering module, merging module, assignment number module and association module; The filtering module is used to filter the microstructure EBSD image of metal materials and extract the grain edges; The merging module screens and merges grains based on the microstructure EBSD image of the metal material after extracting the grain edges; The numbering module assigns a numerical label representing the geometric characteristics and position information of each grain based on the microstructure EBSD image of the metal material after screening and merging the grains, and then numbers the grains with the numerical labels; The association module is used to combine the microstructure EBSD image of the metal material after the grains are numbered and the Euler angle orientation data, associate the Euler angle orientation data with the number of each grain, and obtain the physical orientation of the microstructure grains of the metal material.

9. A computer device, characterized in that: The invention comprises a processor and a memory, the memory is used to store a computer executable program, the processor reads the computer executable program from the memory and executes it, and when the processor executes the computer executable program, it can implement the method for metal material microstructure grain identification and physical orientation output suitable for cellular automaton as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored in a computer-readable storage medium. When the computer program is executed by a processor, the method for digitally processing the microstructure of the EBSD results of a metal material according to any one of claims 1 to 7 can be implemented.

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