Non-cubic crystal EBSD three-dimensional texture high-precision visual analysis method

Abstract

The application is suitable for the technical field of electron backscatter diffraction crystallography characterization, and provides a non-cubic crystal system EBSD three-dimensional texture high-precision visual analysis method, which comprises seven core steps of crystallography modeling, EBSD data reading and orientation extraction, ODF calculation, IPF color key preprocessing and mapping, pole figure and inverse pole figure calculation, weak texture partition identification and standardized data output; the application, through complete non-cubic crystal system symmetry modeling, reduces the triclinic crystal system orientation calculation error to less than 5 degrees; through Lambert projection coordinate explicit output and standardized data protocol, the visual closedness and format barriers of commercial software are broken; through dynamic threshold partition, the weak texture detection lower limit is reduced to 5 degrees, and the sensitivity is improved by about 80% compared with the traditional method; the application has high precision, strong compatibility and low cost, and can be widely applied to EBSD texture characterization work in the fields of material science, metallurgy, geology and the like.

Description

Technical Field

[0001] This invention belongs to the field of electron backscatter diffraction crystallography characterization technology, and particularly relates to a high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD. Background Technology

[0002] Electron backscatter diffraction is a core crystallographic characterization technique based on scanning electron microscopy. By collecting backscattered electron diffraction patterns excited on the sample surface, it can analyze key information such as crystal orientation, grain boundaries, and texture. Since its development and improvement in the 1980s, it has become a core tool for studying the evolution of crystal orientation and texture in fields such as materials science, metallurgy, geology, and semiconductors.

[0003] Current mainstream commercial EBSD analysis systems include Oxford Instruments AZtec EBSD and EDAXOIM Analysis. Their core data processing workflow includes five stages: pattern calibration, data cleaning, orientation calculation, texture analysis, and visualization output. The core mathematical foundation of texture characterization is the Orientation Distribution Function (ODF), Pole Figure (PF), and Inverse Pole Figure (IPF). ODF, defined in Eulerian space, describes the probability density distribution of grain orientation in polycrystalline materials. Pole Figures and Inverse Pole Figures achieve two-dimensional / three-dimensional visualization of the three-dimensional orientation distribution through projection. For related technical principles, see: Schwartz, AJ, et al. (2000). Electron Backscatter Diffraction in Materials Science. Springer; Bunge, HJ (1982). Texture Analysis in Materials Science: Mathematical Methods. Butterworths.

[0004] In practical applications, existing commercial software and open-source tools suffer from the following systemic defects and technical bottlenecks in texture analysis of non-cubic crystal systems:

[0005] 1. Accuracy defects caused by symmetry simplification in non-cubic crystal systems: Existing commercial software has serious limitations in supporting low-symmetry non-cubic crystal systems, especially triclinic systems. Symmetry reduction approximations are commonly used, approximating the triclinic P-1 space group as monoclinic or trigonal cylindrical symmetry. This leads to deviations in integration limits and truncation of Fourier coefficients during ODF calculations, ultimately resulting in orientation calculation errors as high as [insert error here]. Meanwhile, existing software requires fixed cell parameters, which cannot be adapted to samples in solid solutions and doped systems where cell parameters change with composition gradients. It requires manual setting for each region, resulting in extremely low analysis efficiency.

[0006] 2. The closed nature of 3D texture visualization: Commercial software uses Lambert equal-area projection to generate 3D inverse pole figures, but the core rendering algorithm is completely closed, and can only output raster images such as PNG and JPG. It cannot provide the numerical correspondence between Lambert projection coordinates and orientation intensity values. Users cannot export the data to software such as OriginPro and MATLAB for in-depth processing and publication-grade plotting. At the same time, the color mapping algorithm is a black box design, and the correspondence between orientation and color cannot be verified, which cannot meet the data reproducibility requirements of scientific research papers.

[0007] 3. Insufficient sensitivity in weak texture recognition: Existing commercial software generally employs... Texture classification is performed using a fixed threshold, completely ignoring The weak texture budding region of the interval; while in microcrystalline glass, thin film materials, and early crystallization systems, the texture intensity in the early evolution stage of preferred orientation is usually only... Existing software classifies such valid signals as noise, making it impossible to track the early evolution of texture, causing users to miss the optimal process window for material heat treatment and preventing precise control of material properties.

[0008] 4. Data flow format barriers: Commercial software forms a vertically closed data flow, with raw data using vendor-specific binary formats. The analysis results are completely trapped within the software, and the connection with mainstream scientific drawing and data analysis software requires multiple manual format conversions, which is not only inefficient but also prone to introducing human error. At the same time, the annual fee for the 3D visualization module of commercial software is as high as 80,000 to 120,000 yuan, making the cost of scientific research use extremely high.

[0009] To address the aforementioned shortcomings, while the existing open-source tool MTEX ​​provides basic texture calculation functions (see Bachmann, F., et al. "Texture Analysis with MTEX ​​– Free and Open Source Software Toolbox." Solid State Phenomena, 160 (2010): 63-68), it does not provide a complete end-to-end solution for high-precision modeling of non-cubic crystal systems, accurate mapping of IPF color bonds, automatic recognition of weak textures, and standardized cross-platform output. Therefore, it cannot directly achieve high-precision analysis and 3D visualization of EBSD textures in non-cubic crystal systems.

[0010] Therefore, a high-precision visualization analysis method for non-cubic EBSD three-dimensional texture is needed to solve the above problems. Summary of the Invention

[0011] The purpose of this invention is to overcome the above-mentioned defects of the prior art and provide a high-precision visualization analysis method for non-cubic EBSD three-dimensional texture. This method solves the technical problems of low accuracy in non-cubic orientation calculation, closed three-dimensional visualization, insufficient sensitivity in weak texture recognition, and format barriers in data flow in the prior art, thereby achieving high-precision, high-compatibility, and low-cost analysis and visualization of non-cubic texture throughout the entire process.

[0012] To achieve the above objectives, the present invention provides the following technical solution:

[0013] A high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD includes the following steps:

[0014] S1. Crystallographic Modeling: Based on the crystal structure characteristics of the non-cubic crystal system material to be analyzed, a crystal symmetry model and a sample symmetry model are constructed respectively. The crystal symmetry model completely preserves all space group symmetries and independent unit cell parameters of the crystal system to be analyzed, and supports dynamic adaptation and adjustment of unit cell parameters with material composition gradient.

[0015] Furthermore, non-cubic crystal systems include triclinic, monoclinic, orthorhombic, trigonal, hexagonal, and tetragonal crystal systems; for the triclinic P-1 space group, all six independent unit cell parameters are fully preserved, including the unit cell edge length. Angle with unit cell Without any symmetry reduction approximation, the accuracy of the irreducible domain boundary in the orientation space is ensured, eliminating systematic errors in ODF calculation from the root.

[0016] Furthermore, the dynamic adaptation and adjustment of unit cell parameters specifically means that, through parameterized script design, the unit cell parameters can be continuously adjusted as the material doping amount and solid solubility change. A single analysis process can cover the full-area texture characterization of composition gradient samples without the need for manual settings in different regions, thus improving analysis efficiency by 5 to 10 times.

[0017] S2, EBSD Data Reading and Orientation Extraction: Read the raw EBSD data, extract the crystal orientation data of the target phase based on the crystal symmetry model and sample symmetry model constructed in S1, and convert the orientation data into the standard Euler angle format agreed upon by Bunge for output;

[0018] Furthermore, the raw data collected by EBSD includes raw data from vendors in .cpr, .crc, and .ang formats, and data reading and reference coordinate system conversion are completed through a unified interface.

[0019] S3. Orientation Distribution Function Calculation: The kernel density estimation method is used to calculate the continuous orientation distribution function ODF based on the crystal orientation data extracted in S2. The kernel function adopts a rotation-invariant Gaussian kernel, and the spatial resolution and statistical stability of ODF are balanced by an adjustable half-width parameter.

[0020] Furthermore, the adjustable range of the full width at half maximum (FWHM) parameter of the Gaussian kernel is: The half-width at half-maximum (WHM) parameter is positively correlated with data density and negatively correlated with ODF smoothness. Users can flexibly adjust it according to data acquisition density and analysis needs, taking into account both the detail resolution and statistical stability of ODF.

[0021] S4, IPF Color Key Preprocessing and Mapping: The standard inverse pole figure IPF color key image is repaired for interference pixels, and an affine transformation mapping relationship between pixel coordinates and Lambert projection coordinates is established. The K-nearest neighbor algorithm is used to match the corresponding color code for each orientation data point, and the cross-platform compatible Windows COLORREF standard color format is output.

[0022] Specifically, it includes the following sub-steps:

[0023] S41. Color key image reading: Read the IPF standard color key image and obtain the RGB three-channel color matrix;

[0024] S42. Interference Pixel Identification and Repair: Identify black interference pixels in the image ( ) and white background pixels ( The Gaussian weighted interpolation method is used to restore the color of black interference pixels, and the standard deviation of the Gaussian kernel is... Pixels ensure natural color transitions and no border effects after restoration;

[0025] The calculation formula for Gaussian weighted interpolation repair is as follows:

[0026] ;

[0027] In the formula, This is the original pixel matrix of the red channel. For the effective pixel weight matrix, It is a Gaussian filter kernel. As the normalization factor, This is the image filtering function; the restoration formulas for the green and blue channels are the same as those for the red channel.

[0028] S43, Affine Transformation Coordinate Calibration: Select Four characteristic crystal orientations are used as anchor points. The affine transformation coefficients between pixel coordinates and Lambert projection coordinates are solved using the least squares method to establish an accurate mapping from pixel coordinates to the crystallographic coordinate system, eliminating geometric distortions during image acquisition and storage. The corresponding parameters of each anchor point are shown in the table below:

[0029] Anchor point Pixel coordinates (row, col) Lambert coordinates (X, Y) Corresponding crystal orientation

[001] (677,703) (0,0) red [-100] (170,702) (-1.4142,0) blue

[100] (1183,701) (+1.4142,0) green

[010] (676,143) (0,+1.4142) blue

[0030] The formula for solving affine transformations is:

[0031] ;

[0032] In the formula, For Lambert projection coordinates, For pixel coordinates, The affine transformation coefficients are in the X direction. The affine transformation coefficients are given in the Y direction. The optimal transformation coefficients are obtained by using the least squares method with four sets of anchor point coordinates, and the relative mapping error is less than 0.003.

[0033] S44K, Nearest Neighbor Color Matching: For the Lambert projection coordinates to be matched, the K-nearest neighbor algorithm is used to search for the nearest neighbor pixel in the repaired color key lookup table, obtain the corresponding RGB color value, and convert it into the Windows COLORREF standard format for output, ensuring cross-platform color compatibility.

[0034] The calculation formula for the Windows COLORREF standard color format is as follows:

[0035] ;

[0036] In the formula, These are the color components of the red, green, and blue channels, with values ​​ranging from [value range missing]. .

[0037] S5. Calculation of Pole Diagram and Inverse Pole Diagram: Based on the ODF obtained in S3, the pole diagram of the target crystal plane family and the inverse pole diagram of the sample reference direction are calculated respectively. The Lambert equal area projection is used to complete the transformation from spherical distribution to planar coordinates, and the Lambert projection coordinates and corresponding orientation density intensity values ​​of each data point are explicitly output.

[0038] The formula for calculating the Lambert equal-area projection is as follows:

[0039] ;

[0040] In the formula, Polar angle, It is the azimuth angle. Let the radius be the Lambert projection radius. These are the x and y coordinates of the Lambert projection, respectively. The projected coordinates are calculated explicitly using analytical formulas, preserving the double-precision floating-point precision of the original data and avoiding precision loss caused by rasterization.

[0041] S6. Weak texture zone identification: Calculate the MUD value of the full spectrum orientation density, and use a dynamic threshold based on the statistical distribution of the MUD value to complete the texture intensity zone. Identify and output the weak texture budding zone data with MUD values ​​of 1.1~1.5 separately to track the early evolution process of texture.

[0042] The formula for defining the MUD value is as follows:

[0043] ;

[0044] In the formula, The orientation density value is the measured orientation distribution function. The orientation density value represents a random orientation distribution. Indicates a completely random orientation. This indicates the existence of a preference for the best;

[0045] Furthermore, the dynamic threshold partitioning specifically involves dividing the texture intensity into four intervals based on the MUD value, as shown in the table below:

[0046] Partition Name MUD range Physical meaning Strong texture >2 Clear preference for the best Medium texture 1.5<MUD≤2 Weaker preference Weak texture budding area 1.1<MUD≤1.5 Texture begins to form Random orientation ≤1.1 No obvious preference

[0047] The algorithm automatically counts the number and proportion of data points in each interval, outputs the peak position and corresponding MUD value of the weak texture budding zone, and generates a separate data file for the weak texture zone, thus achieving accurate tracking of the early texture evolution of materials.

[0048] S7. Standardized data output: Lambert projection coordinates, linear orientation density intensity values, compressed dynamic range intensity values, and color coding are integrated according to a fixed protocol and output in tab-delimited TSV plain text format to achieve direct compatibility with third-party scientific drawing and data analysis software.

[0049] Furthermore, the fixed protocol is a five-column standardized format, in the following order: ;

[0050] in, The x and y coordinates of the Lambert projection. This represents the original MUD linear strength value. It is the square root of the MUD value (used to compress the dynamic range and adapt to 3D visualization rendering). It uses Windows COLORREF format for color encoding; this format is compatible with almost all data analysis and plotting software such as Excel, OriginPro, MATLAB, Python, and R, and can be directly dragged and dropped for import without format conversion.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] This invention significantly improves the accuracy of noncubic crystal system analysis: through complete noncubic crystal system space group symmetry modeling in step S1, without any order reduction approximation, the orientation calculation error of triclinic crystal systems is reduced from that of existing technologies. Down to Within this range, the orientation calibration confidence index is improved from 0.3~0.5 to over 0.7; at the same time, through the dynamic adaptation technology of unit cell parameters in step S1, it can cover the full-area analysis of composition gradient samples, with a wider range of applications and strong reproducibility of results;

[0053] This invention comprehensively upgrades the freedom of 3D visualization and publication quality: Through the explicit output of Lambert projection coordinates in the S5 step, it achieves the binding output of coordinates, intensity, and color, breaking free from the preset viewpoint limitations of commercial software. It can achieve true 3D visualization with 360° free rotation, lighting adjustment, and iso-intensity surface extraction in software such as OriginPro; the output data can directly generate vector graphics with no resolution limitations, fully meeting the publication requirements of top journals such as Acta Materialsia, and the data can be directly used for secondary mining such as machine learning training;

[0054] This invention achieves a qualitative breakthrough in the sensitivity of weak texture recognition: through the S6-step dynamic threshold partitioning algorithm, the detection lower limit of weak texture is lowered from the existing technology. Down to The sensitivity is improved by about 80%, which can accurately identify and track the early evolution process of texture budding stage, providing core data support for the optimization of material heat treatment process window, reducing the number of trial and error experiments by more than 50%, and realizing precise control of material properties;

[0055] This invention boasts strong cross-platform compatibility and extremely low usage costs: By employing the standardized five-column TSV data protocol of the S7 steps, it breaks down the format barriers of commercial software, achieving seamless direct connection with mainstream scientific research software, eliminating errors from manual format conversion, and improving analysis efficiency by 6 to 12 times; developed based on the open-source MTEX ​​framework, it eliminates the need to pay high commercial software licensing fees, saving 80,000 to 120,000 yuan annually; its simple single-script architecture requires only simple training to master, making it highly versatile;

[0056] This invention provides accurate and verifiable color mapping: Through a three-step process of Gaussian interpolation repair, affine transformation calibration, and K-nearest neighbor matching in step S4, pixel-level precision IPF color mapping is achieved, with a relative mapping error of less than 0.003, a color matching error of less than 0.5%, and a processing time of less than 5 seconds for millions of data points. The color-orientation correspondence is completely transparent and verifiable, meeting the reproducibility requirements of scientific research data. The color coding for the same orientation is completely consistent in different batches of analysis, facilitating comparative studies.

[0057] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the overall process of the present invention.

[0059] Figure 2 This is a flowchart of the sub-process of the IPF color key preprocessing and mapping steps in this invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0061] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0062] Example 1

[0063] This embodiment focuses on the anorthite phase in ZnO-doped CAS (CaO-Al2O3-SiO2) glass ceramics. Anorthite belongs to the triclinic crystal system, space group P-1, and is a typical low-symmetry non-cubic crystal system material.

[0064] like Figure 1 and Figure 2 As shown, this embodiment of the invention provides a high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD, and the specific analysis steps are as follows:

[0065] S1. Crystallographic Modeling:

[0066] Based on the crystal structure parameters of anorthite, a crystal symmetry model and a sample symmetry model were constructed. The specific parameters are as follows:

[0067] Crystal symmetry model ;

[0068] Wherein, represents the space group P-1 of the triclinic crystal system. cell edge length The unit is angstrom; The angle between units ;

[0069] Sample symmetry model Define the orthogonal symmetry of the sample.

[0070] S2, EBSD data reading and orientation extraction:

[0071] The raw acquisition data in .cpr format is read using the EBSD.load function, and the reference coordinate system transformation is completed simultaneously to extract the orientation data of the anorthite phase.

[0072]

[0073]

[0074] Then, the orientation data is converted into the Euler angle format specified by Bunge and output.

[0075]

[0076] In this embodiment, the sample contains 50,000 valid data points, and the data reading and orientation extraction takes about 2 seconds.

[0077] S3 and ODF calculations:

[0078] Continuous ODF is calculated using kernel density estimation, with the Gaussian kernel half-width set to 1. Specifically:

[0079]

[0080] After the calculation is completed, the maximum value of MUD in this embodiment is 4.50, indicating that the sample has a clear preferred orientation.

[0081] S4, IPF color key preprocessing and mapping:

[0082] (1) Read the pre-stored IPF_color_key_clean.bmp standard color key image and obtain the RGB three-channel color matrix;

[0083] (2) Identify and repair approximately 1200 black interference pixels, Gaussian kernel standard deviation Pixels, construct a color lookup table containing approximately 450,000 valid pixels;

[0084] (3) Selection With four anchor points, the affine transformation coefficients are solved using the least squares method, and the relative mapping error is less than 0.003.

[0085] (4) The color value of each data point is matched by the K-nearest neighbor algorithm and converted into Windows COLORREF format for output. The relative error of color matching is less than 0.5%.

[0086] S5. Calculation of polar charts and inverse polar charts:

[0087] The pole figures for the three crystal plane families (001), (010), and (100) of anorthite were calculated, with the resolution set to [resolution value missing]. The projection coordinates of each data point were explicitly calculated using Lambert equal-area projection; inverse pole figures were calculated for the three sample reference directions RD (rolling direction), TD (transverse direction), and ND (normal direction) to obtain orientation density distribution data.

[0088] S6. Weak texture partitioning identification:

[0089] Dynamic threshold partitioning based on full-spectrum MUD values ​​yields the following statistical results:

[0090] (Strong texture area): 1250 points, accounting for 15.2%;

[0091] (Medium texture area): 980 points, accounting for 11.9%;

[0092] (Weak texture budding area): 850 points, accounting for 10.3%;

[0093] (Randomization zone): 5120 points, accounting for 62.6%;

[0094] Output the peak position, MUD value, and corresponding data file of the weak texture germination zone separately.

[0095] S7. Standardized Data Output:

[0096] according to The analysis results are output in a standardized five-column format using TSV plain text format, automatically generating the following files:

[0097] Anorthite_EulerAngles_deg.txt (Euler angle data);

[0098] IPF_RD_3D.txt (RD direction inverse pole figure three-dimensional data);

[0099] IPF_RD_WeakPeak.txt (Weak texture budding region data);

[0100] IPF_RD_2D.png (Two-dimensional inverse pole figure);

[0101] IPF_ColorKey_RD.png (Color key reference diagram);

[0102] PF_Anorthite_001.txt ((001) crystal plane pole figure data).

[0103] Drag and drop IPF_RD_3D.txt directly into OriginPro, and set... For the X-axis, Y-axis Z-axis, By mapping colors, publication-quality 3D texture visualization graphics can be generated.

[0104] In this embodiment, the orientation calculation error of the anorthite phase is: Far lower than commercial software The detection limit for weak texture has been reached. It fully captures the preferential orientation evolution information in the early stage of crystallization; the whole process analysis takes about 4 minutes, which is nearly 90% shorter than the 35 minutes of commercial software.

[0105] Example 2

[0106] This embodiment uses common pyroxene, which belongs to the monoclinic crystal system, as the analysis object. The space group of common pyroxene is C2 / c, and the unit cell parameter is Å. Å Å , , , The specific analysis steps are the same as in Example 1, except that:

[0107] S1. In crystallographic modeling, the C2 / c space group symmetry of the monoclinic crystal system is fully preserved without any order reduction approximation.

[0108] In S2 and ODF calculations, the Gaussian kernel half-width is set to... To achieve higher spatial resolution;

[0109] S3. Pole diagram calculation is performed on the three characteristic crystal plane families (110), (001), and (111).

[0110] In this embodiment, the orientation calculation error of ordinary pyroxene is... Compared to commercial software Significantly reduced; weak texture budding zone ( The proportion of ) was 8.7%, which enabled accurate tracking of the early crystallization texture of igneous rocks.

[0111] Example 3

[0112] This embodiment uses hexagonal ZnO ceramics as the analysis object. The ZnO space group is P63mc, and the unit cell parameter is Å. Å , , The specific analysis steps are the same as in Example 1, except that:

[0113] S1. In crystallographic modeling, the P63mc space group symmetry of the hexagonal crystal system is fully preserved, and the cell parameters are dynamically adjusted with the Al doping amount to adapt to samples with compositional gradients.

[0114] In S2 and ODF calculations, the Gaussian kernel half-width is set to... To improve statistical stability;

[0115] S3, Inverse pole figure calculation is performed in the ND direction normal to the thin film.

[0116] In this embodiment, the orientation calculation error of the ZnO ceramic is... The detection limit for weak textures has been reached. It can accurately identify early preferred orientations during thin film growth, providing core data support for optimizing thin film preparation processes.

[0117] The above three embodiments cover three typical non-cubic crystal systems: triclinic, monoclinic, and hexagonal. They verify the universality, stability, and high precision of the method of the present invention across the entire range of non-cubic crystal systems, and completely solve the four core defects of the prior art.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD, characterized in that, Includes the following steps: S1. Crystallographic Modeling: Based on the crystal structure characteristics of the non-cubic crystal system material to be analyzed, a crystal symmetry model and a sample symmetry model are constructed respectively. The crystal symmetry model completely preserves all space group symmetries and independent unit cell parameters of the crystal system to be analyzed, and supports dynamic adaptation and adjustment of unit cell parameters with material composition gradient. S2, EBSD Data Reading and Orientation Extraction: Read the raw EBSD data, extract the crystal orientation data of the target phase based on the crystal symmetry model and sample symmetry model constructed in S1, and convert the orientation data into the standard Euler angle format agreed upon by Bunge for output; S3. Orientation Distribution Function Calculation: The kernel density estimation method is used to calculate the continuous orientation distribution function ODF based on the crystal orientation data extracted in S2. The kernel function adopts a rotation-invariant Gaussian kernel, and the spatial resolution and statistical stability of ODF are balanced by an adjustable half-width parameter. S4, IPF Color Key Preprocessing and Mapping: The standard inverse pole figure IPF color key image is repaired for interference pixels, and an affine transformation mapping relationship between pixel coordinates and Lambert projection coordinates is established. The K-nearest neighbor algorithm is used to match the corresponding color code for each orientation data point, and the cross-platform compatible Windows COLORREF standard color format is output. S5. Calculation of Pole Diagram and Inverse Pole Diagram: Based on the ODF obtained in S3, the pole diagram of the target crystal plane family and the inverse pole diagram of the sample reference direction are calculated respectively. The Lambert equal area projection is used to complete the transformation from spherical distribution to planar coordinates, and the Lambert projection coordinates and corresponding orientation density intensity values ​​of each data point are explicitly output. S6. Weak texture zone identification: Calculate the MUD value of the full spectrum orientation density, and use a dynamic threshold based on the statistical distribution of the MUD value to complete the texture intensity zone. Identify and output the weak texture budding zone data with MUD values ​​of 1.1~1.5 separately to track the early evolution process of texture. S7. Standardized Data Output: Lambert projection coordinates, linear orientation density intensity values, compressed dynamic range intensity values, and color coding are integrated according to a fixed protocol and output in tab-delimited TSV plain text format to achieve direct compatibility with third-party scientific drawing and data analysis software.

2. The high-precision visualization analysis method for non-cubic EBSD three-dimensional texture according to claim 1, characterized in that, In S1, the non-cubic crystal system includes triclinic, monoclinic, orthorhombic, trigonal, hexagonal, and tetragonal crystal systems; for the triclinic P-1 space group, all six independent unit cell parameters are fully preserved, including the unit cell edge length. Angle with unit cell Asymmetric reduction approximation is used.

3. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD as described in claim 1, characterized in that, In S1, the dynamic adaptation and adjustment of unit cell parameters specifically means that, through parameterized script design, the unit cell parameters can be continuously adjusted as the material doping amount and solid solubility change, and a single analysis process can cover the full-area texture characterization of composition gradient samples.

4. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD as described in claim 1, characterized in that, In S2, the raw EBSD data includes manufacturer-generated raw data in .cpr, .crc, and .ang formats, and data reading and reference coordinate system conversion are completed through a unified interface.

5. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD as described in claim 1, characterized in that, In S3, the adjustable range of the full width at half maximum (FWHM) parameter of the Gaussian kernel is: The half-width at half-maximum (WHM) parameter is positively correlated with data density and negatively correlated with ODF smoothness.

6. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD according to claim 1, characterized in that, S4 specifically includes the following sub-steps: S41. Color key image reading: Read the IPF standard color key image and obtain the RGB three-channel color matrix; S42. Interference Pixel Identification and Repair: Identify black interference pixels in the image ( ) and white background pixels ( The Gaussian weighted interpolation method is used to restore the color of black interference pixels, and the standard deviation of the Gaussian kernel is... Pixel; S43. Affine Transformation Coordinate Calibration: Select... Using four characteristic crystal orientations as anchor points, the affine transformation coefficients between pixel coordinates and Lambert projection coordinates are solved by the least squares method to establish an accurate mapping from pixel coordinates to the crystallographic coordinate system. S44, K-Nearest Neighbor Color Matching: For the Lambert projection coordinates to be matched, the K-nearest neighbor algorithm is used to search for the nearest neighbor pixel in the repaired color key lookup table, obtain the corresponding RGB color value, and convert it into the Windows COLORREF standard format for output.

7. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD as described in claim 1, characterized in that, In S5, the formula for calculating the Lambert equal-area projection is: ； in, Polar angle, It is the azimuth angle. Let the radius be the Lambert projection radius. These are the x and y coordinates of the Lambert projection, respectively.

8. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD according to claim 1, characterized in that, In S6, the formula for defining the MUD value is: ； in, The orientation density value is the measured orientation distribution function. The orientation density value represents a random orientation distribution. Indicates a completely random orientation. This indicates the existence of a preference for the best; Dynamic threshold partitioning divides texture intensity into Strong texture area medium texture area Weak texture budding area The random orientation region.

9. A high-precision visualization analysis method for non-cubic EBSD three-dimensional texture according to claim 6, characterized in that, In S44, the calculation formula for the Windows COLORREF standard color format is as follows: ； in, These are the color components of the red, green, and blue channels, with values ​​ranging from [value range missing]. .

10. The high-precision visualization analysis method for three-dimensional texture of non-cubic EBSD according to claim 1, characterized in that, In S7, the fixed protocol is a five-column standardized format, which are as follows: ; in, The horizontal and vertical coordinates of the Lambert projection. This represents the original MUD linear strength value. The square root of the MUD value. Color encoding in Windows COLORREF format.

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