A quasi-in-situ EBSD data analysis method

Abstract

The present invention provides a quasi-in-situ EBSD data analysis method, and relates to the technical field of EBSD data processing. The target material is subjected to two EBSD detection experiments, the forward scattering detector image and the original EBSD data of the target material are collected, the resolution of the forward scattering detector image is modified and output as EBSD data, the corresponding full Euler image is derived from the EBSD data as a standard image and an image to be corrected; the standard image, the image to be corrected and the corresponding EBSD data are intercepted and geometrically corrected; the EBSD data obtained by intercepting the original data obtained in the first experiment and the EBSD data obtained by intercepting and geometrically correcting the original data obtained in the second experiment are compared, the same crystal grain is selected and the rotation difference matrix of the Euler angle of the crystal grain in the two EBSD detection experiments is calculated, the original data obtained in the second experiment is intercepted and the EBSD data obtained by geometrically correcting are oriented corrected, and the final EBSD data are obtained.

Description

Technical Field

[0001] The invention relates to the technical field of EBSD data processing, and in particular to a quasi-in-situ EBSD data analysis method. Background Art

[0002] In-situ electron backscattered diffraction analysis (Situ EBSD) is an important characterization technique for characterizing the microstructure and microtexture after heat treatment or mechanical treatment on a scanning electron microscope (SEM). Based on the Situ EBSD method, theoretical methods combined with knowledge of slip systems, deformation mechanisms, and phase diagrams can help us deeply understand the key process of microstructure evolution in determining the physical properties of materials. Although there are sample stages specifically used for heating, cooling, and testing for Situ EBSD experiments, the SEM sample chamber has problems such as small space, single deformation mode, single temperature control, EBSD acquisition requires a high-angle environment, and sample size and shape restrictions, which makes it impossible to achieve Situ EBSD detection of all samples. Therefore, Quasi-in-situ Electron Backscattered Diffraction analysis (Quasi-in-situEBSD) came into being. Quasi-in-situEBSD can carry out the process of microstructure evolution outside the SEM chamber, while avoiding the problems of sample drift when the sample is under load or high temperature and surface frost hindering effective measurement during low-temperature experiments. However, Quasi-in-situ EBSD also faces the problems of sample test drift and difficulty in accurate positioning.

[0003] At present, in Quasi-in-situ EBSD testing, because the relative position of the sample and the detector is different during the two tests, the obtained EBSD data will have a slight orientation deviation, which affects the authenticity of the experimental data and is currently ignored; in addition, heat treatment or mechanical treatment will change the data orientation and morphology of the two tests, resulting in a large change in the microstructure of the same test area in the two tests. Therefore, the orientation imaging map cannot be truly aligned in space, hindering accurate experimental analysis; at the same time, the two test areas cannot completely overlap, and redundant data points are counted, which causes errors in data analysis. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a quasi-in-situ EBSD data analysis method in view of the deficiencies of the above-mentioned prior art, eliminate the errors caused by the inconsistent relative positions of the sample and the detector during the test process, realize the pixel-level alignment of the experimental data of the two tests, and capture the maximum area covered in the two test processes, thereby eliminating the interference of redundant data points on the experimental results.

[0005] A quasi-in-situ EBSD data analysis method comprises the following steps:

[0006] Step 1: Perform two EBSD detection experiments on the target material, collect the forward scattering detector image and original EBSD data of the target material, and modify the resolution of the forward scattering detector image obtained from the two EBSD detection experiments to be consistent with the corresponding EBSD data, and output the forward scattering detector image after the modified resolution as modified EBSD data and record it, and export the corresponding full Euler image of the modified EBSD data as the standard image and the image to be corrected;

[0007] The original EBSD data includes the identifiable data point information of the target material; the original EBSD data obtained from the first EBSD detection experiment is recorded as α-EBSD data; the original EBSD data obtained from the second EBSD detection experiment is recorded as β-EBSD data; the EBSD data output from the front scattering detector image with the modified resolution of the first EBSD detection experiment is recorded as a-EBSD data; the EBSD data output from the front scattering detector image with the modified resolution of the second EBSD detection experiment is recorded as b-EBSD data;

[0008] Step 1.1: Record the pasting direction and position of the target material on the sample stage, perform the first EBSD detection experiment on the target material area of ​​interest to obtain the forward scattering detector image and the EBSD data including the identifiable data point information of the target material, and record the EBSD data as α-EBSD data;

[0009] Step 1.2: Take out the target material for heat treatment or mechanical treatment, paste the target material in the pasting direction and position in the first experiment, and conduct a second EBSD detection experiment. The detection step length remains the same as the first time. The forward scattering detector image and the EBSD data of the identifiable data points of the target material are also obtained. The EBSD data are recorded as β-EBSD data;

[0010] Step 1.3: Modify the resolution of the forward scattering detector images obtained from the two EBSD detection experiments to be consistent with their corresponding EBSD data, record the EBSD data output from the forward scattering detector image with the modified resolution in the first EBSD detection experiment as a-EBSD data, and record the EBSD data output from the forward scattering detector image with the modified resolution in the second EBSD detection experiment as b-EBSD data; derive the corresponding full Euler diagrams from the a-EBSD data and the b-EBSD data, set the full Euler diagram derived from the a-EBSD data as the standard diagram, and set the full Euler diagram derived from the b-EBSD data as the diagram to be corrected;

[0011] Step 1.3.1: Check the gratings of α-EBSD data and β-EBSD data, and change the image resolution of the forward scatter detector images obtained from the first EBSD test experiment and the second EBSD test experiment to be consistent with the gratings of α-EBSD data and β-EBSD data respectively;

[0012] Step 1.3.2: extract the three color channel values ​​of the RGB color system of each pixel point in the front scattering detector image obtained from the first EBSD detection experiment and the second EBSD detection experiment after the modification, sort them, and save them as a color value matrix;

[0013] Step 1.3.3: Extract the pixel data from the α-EBSD data and the β-EBSD data respectively and save them as two pixel data numerical matrices, and replace the Euler angles of the two pixel data numerical matrices with the values ​​in the color numerical matrix obtained in step 1.3.2 respectively, and keep the values ​​of other pixel data in the pixel data matrix unchanged to obtain two new numerical matrices, and output the two new numerical matrices as EBSD standard format files, recorded as a-EBSD data and b-EBSD data;

[0014] Step 1.3.4: Export the corresponding full Euler diagrams of a-EBSD data and b-EBSD data respectively, set the full Euler diagram exported from a-EBSD data as the standard diagram, and set the full Euler diagram exported from b-EBSD data as the diagram to be corrected;

[0015] Step 2: Overlap the standard image and the image to be corrected and intercept the overlapping range. According to the interception range, intercept the standard image and the image to be corrected. At the same time, intercept the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data. Align the intercepted standard image and the image to be corrected and calculate the target change matrix.

[0016] Step 2.1: Use image processing software to overlap the standard image and the image to be corrected, obtain the overlapping area of ​​the standard image and the image to be corrected, intercept the area where the standard image and the image to be corrected completely overlap, obtain the intercepted standard image and the image to be corrected, and intercept the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data according to this interception range;

[0017] Step 2.1.1: Normalize the coordinates of the data points in the standard image and the image to be corrected;

[0018] Find a common data point in the standard image and the image to be corrected as the origin, and record the coordinates of the origin as a(x,y) and b(x,y) respectively. Then extract all the data points in the two images. The coordinate set of the data points in the standard image is recorded as Li(x,y), and the coordinate set of the data points in the image to be corrected is recorded as Lj(x,y). Subtract the coordinates of the origin a(x,y) and b(x,y) from the coordinates of the data points in the standard image and the image to be corrected, respectively, to obtain the normalized coordinate sets of the data points in the standard image and the image to be corrected, which are recorded as Li-a(x,y) and Lj-b(x,y) respectively.

[0019] After the above calculation, the data point coordinates of the standard image and the image to be corrected can be normalized, and the data point coordinates in the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data can also be normalized;

[0020] Step 2.1.2: Calculate the maximum and minimum values ​​of the horizontal and vertical coordinates of the overlapping area of ​​the standard image and the image to be corrected, and obtain the range of the overlapping area as the interception range;

[0021] The maximum value of the horizontal coordinates in the normalized data point coordinate sets Li-a(x,y) and Lj-b(x,y) in the standard image and the image to be corrected is obtained respectively, and the minimum value between the two is taken as the maximum value of the horizontal coordinates of the overlapping area; the minimum value of the horizontal coordinates in the normalized data point coordinate sets Li-a(x,y) and Lj-b(x,y) in the standard image and the image to be corrected is obtained respectively, and the maximum value between the two is taken as the minimum value of the horizontal coordinates of the overlapping area; the maximum and minimum values ​​of the vertical coordinates of the overlapping area are obtained in the same way as the horizontal coordinates; the range of the overlapping area is obtained by using the maximum and minimum values ​​of the horizontal and vertical coordinates of the overlapping area of ​​the standard image and the image to be corrected, and the range of the overlapping area is taken as the interception range of the standard image and the image to be corrected;

[0022] Step 2.1.3: Based on the interception range, the overlapping area of ​​the standard image and the image to be corrected is intercepted to obtain the intercepted standard image and the image to be corrected. Similarly, the normalized α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data are intercepted based on the interception range;

[0023] Step 2.2: grayscale the intercepted standard image and the image to be corrected, use the image registration technology to identify the data points in the intercepted standard image and the image to be corrected, and match the feature data points of appropriate quantity and quality by setting the quality threshold of the feature points, the spatial scale of the detection, and the detection accuracy, and align the feature data points;

[0024] Step 2.3: Match the geometric transformation between the intercepted standard image and the image to be corrected according to the aligned feature data points, and calculate the target transformation matrix;

[0025] Step 2.3.1: Screen the valid feature data points in the intercepted standard image and the image to be corrected;

[0026] Step 2.3.2: Obtain the coordinates of the effective feature data points in the intercepted image to be corrected that are aligned with the effective feature data points in the intercepted standard image, and calculate the target change matrix using the transformation relationship between the coordinates and the coordinates of the effective feature data points in the intercepted standard image;

[0027] Step 3: Use the target transformation matrix to geometrically correct the data points of the intercepted b-EBSD data to obtain the intercepted and geometrically corrected b-EBSD data, export the full Euler diagram of the intercepted and geometrically corrected b-EBSD data, compare it with the intercepted standard diagram, and check whether the two full Euler diagrams completely overlap. If not, return to step 2 to recalculate the target change matrix, and use the updated target change matrix to geometrically correct the data points of the intercepted b-EBSD data until the contours completely correspond; then use the updated target transformation matrix to geometrically correct the intercepted β-EBSD data to obtain the intercepted and geometrically corrected β-EBSD data;

[0028] Step 4: Compare the intercepted α-EBSD data obtained in step 2 with the intercepted and geometrically corrected β-EBSD data obtained in step 3, find a grain cluster with an orientation deviation within the threshold at the same position, select a grain in the grain cluster and record the Euler angle of the grain in the two EBSD detection experiments, calculate the rotation difference matrix of the Euler angle of the grain in the two EBSD detection experiments, perform the same orientation correction on the intercepted and geometrically corrected β-EBSD data obtained in step 3, and obtain the final β-EBSD data;

[0029] Step 4.1: Compare the α-EBSD data intercepted in step 2.1.3 with the b-EBSD data intercepted and geometrically corrected in step 3, and compare and observe at the same position of the two data to find the grain cluster whose orientation deviation is within the threshold value, select any grain in the grain cluster of the intercepted α-EBSD data, and select any data point in the grain, record the coordinates and Euler angle of the data point, and set it as the target Euler angle; and find the data point with the same coordinates in the intercepted and geometrically corrected β-EBSD data, and record the Euler angle of the data point, and set it as the initial Euler angle;

[0030] Convert the target Euler angle and the initial Euler angle into an orientation transformation matrix, and calculate the rotation difference matrix between the target Euler angle and the initial Euler angle. The specific calculation process is as follows:

[0031] The orientation transformation matrix of the target Euler angle is as follows:

[0032] ;in, is the orientation transformation matrix of the target Euler angle, is the target Euler angle 1, is the target Euler angle 2, is the target Euler angle 3;

[0033] The calculation method of the orientation conversion matrix of the initial Euler angle is the same as that of the target Euler angle. The orientation conversion matrix of the initial Euler angle is shown as follows:

[0034] ;in, is the orientation transformation matrix of the initial Euler angles, is the initial Euler angle 1, is the initial Euler angle 2, is the initial Euler angle 3;

[0035] Calculate the rotation difference matrix between the target Euler angle and the initial Euler angle. The calculation formula is as follows:

[0036] ;in, is the rotation difference matrix;

[0037] Step 4.2: The β-EBSD data obtained in step 3 after being intercepted and geometrically corrected is multiplied by the rotation difference matrix to complete the orientation correction and obtain the final β-EBSD data, which completely corresponds to the data points of the intercepted α-EBSD data;

[0038] The orientation conversion matrix of the initial Euler angle is oriented corrected using the rotation difference matrix, and the initial Euler angle is converted into the orientation-corrected Euler angle. The calculation formula of the orientation-corrected matrix is ​​as follows:

[0039] ;in, is the matrix after orientation correction;

[0040] The rotation difference matrix is ​​used to perform orientation correction on the intercepted and geometrically corrected β-EBSD data, so that the final β-EBSD data completely corresponds to the data points of the intercepted α-EBSD data.

[0041] The beneficial effects of adopting the above technical solution are:

[0042] (1) When aligning in-situ EBSD data analysis, the present invention intercepts the same test area twice, so that the data points at the same spatial position are at the same coordinates, making it possible to deeply understand the evolution of the microstructure;

[0043] (2) The present invention eliminates the shape distortion and orientation deviation caused by the inconsistency of the relative positions of the sample and the detector during the test process, and realizes the pixel-level alignment of the test experimental data;

[0044] (3) The corrected EBSD data of the present invention has no repeated data points, thereby improving the authenticity and accuracy of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A main flow chart of a quasi-in-situ EBSD data analysis method provided by an embodiment of the present invention;

[0046] Figure 2 The forward scattering detector images recorded by the Aztec system in two EBSD detection experiments provided in the embodiments of the present invention;

[0047] Figure 3 A forward scatter detector image intercepted by the method of the present invention and a corrected forward scatter detector image provided in an embodiment of the present invention;

[0048] FIG4 (a) is an orientation image of a sample detected by the first EBSD detection experiment and the Euler angle information of the grain provided in an embodiment of the present invention; FIG4 (b) is an orientation image of a sample detected by the second EBSD detection experiment and the Euler angle information of the same grain as FIG4 (a) and the orientation deviation thereof; FIG4 (c) is an orientation image and the Euler angle information of the grain after orientation alignment. DETAILED DESCRIPTION

[0049] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0050] In this embodiment, a quasi-in-situ EBSD data analysis method is used, such as Figure 1As shown, the following steps are included:

[0051] Step 1: Perform two EBSD detection experiments (Quasi-in-situ EBSD) on the target material, collect the forward scatter detector image (FSD) and original EBSD data of the target material in the EBSD detection experiment, and modify the resolution of the forward scatter detector image obtained from the two EBSD detection experiments to be consistent with its corresponding EBSD data, and output the forward scatter detector image after the modified resolution as the modified EBSD data and record it, and export the corresponding AllEuler image (AllEuler) of the modified EBSD data as the standard image and the image to be corrected;

[0052] The original EBSD data includes the identifiable data point information of the target material; through the above process, the macroscopic morphology in the front scattering detector image and the microstructure in the orientation imaging image can be mapped at the pixel level; the front scattering detector image with modified resolution is output as EBSD data, in order to distinguish it from the EBSD data obtained from the EBSD test experiment; the original EBSD data obtained from the first EBSD detection experiment is recorded as α-EBSD data; the original EBSD data obtained from the second EBSD detection experiment is recorded as β-EBSD data; the EBSD data output from the front scattering detector image with modified resolution in the first EBSD detection experiment is recorded as a-EBSD data; the EBSD data output from the front scattering detector image with modified resolution in the second EBSD detection experiment is recorded as b-EBSD data;

[0053] Step 1.1: Record the pasting direction and position of the target material on the sample stage, perform the first EBSD detection experiment on the target material area of ​​interest, obtain the forward scattering detector image, and the EBSD data including the identifiable data point information of the target material, and record the EBSD data as α-EBSD data;

[0054] Step 1.2: Take out the target material for heat treatment or mechanical treatment, paste the target material in the pasting direction and position in the first experiment, and conduct a second EBSD detection experiment. The detection step length remains the same as the first time. The forward scattering detector image and the EBSD data of the identifiable data points of the target material are also obtained. The EBSD data are recorded as β-EBSD data;

[0055] The forward scattering detector image is a macroscopic image. It will retain the macroscopic morphological features of the sample after heat treatment or mechanical treatment, but the microstructure will change. Therefore, the β-EBSD data obtained from the second EBSD test experiment is corrected according to the macroscopic morphological features. The sample after heat treatment or mechanical treatment can be corrected to be consistent with the spatial orientation relationship between the detector and the sample in the first EBSD test experiment.

[0056] Step 1.3: Use image processing software to modify the resolution of the forward scattering detector images obtained from the two EBSD detection experiments to be consistent with their corresponding EBSD data, record the EBSD data output from the forward scattering detector image with the modified resolution in the first EBSD detection experiment as a-EBSD data, and record the EBSD data output from the forward scattering detector image with the modified resolution in the second EBSD detection experiment as b-EBSD data. Then use EBSD analysis software to export the corresponding AllEuler diagrams (AllEuler) of a-EBSD data and b-EBSD data, set the AllEuler diagram (AllEuler) exported from a-EBSD data as the standard diagram, and set the AllEuler diagram (AllEuler) exported from b-EBSD data as the diagram to be corrected;

[0057] Step 1.3.1: Use EBSD data analysis software to view the gratings of α-EBSD data and β-EBSD data (the matrix size of the grating is , where m and n are the number of horizontal and vertical elements of the matrix respectively). The image resolution of the forward scattering detector images obtained from the first EBSD detection experiment and the second EBSD detection experiment were changed to the grating resolution of the α-EBSD data and the β-EBSD data respectively using image processing software ( ) are consistent, so that each pixel in the modified forward scatter detector image can uniquely correspond to a data point in the α-EBSD data;

[0058] Step 1.3.2: extract the three color channel values ​​of the RGB color system of each pixel point in the front scattering detector image obtained from the first EBSD detection experiment and the second EBSD detection experiment after the modification, sort them, and save them as a color value matrix;

[0059] The sorting rule is: save it as a three-column numerical matrix in the coordinate order of (1,0), (2,0)...(m,0), (1,1), (2,1), (m,1)...(m,n), each column contains the same numerical values;

[0060] Step 1.3.3: Use EBSD analysis software (MTEX or Channel5) to extract pixel data from α-EBSD data and β-EBSD data respectively and save them as two pixel data numerical matrices, and replace the Euler angles of the two pixel data numerical matrices with the values ​​in the color numerical matrix obtained in step 1.3.2 respectively, and keep the values ​​of other pixel data in the pixel data matrix unchanged to obtain two new numerical matrices, and then use EBSD data analysis software to output the two new numerical matrices into EBSD standard format files, recorded as a-EBSD data and b-EBSD data;

[0061] In this embodiment, the pixel data in the α-EBSD data includes index (Index), phase (Phase), data point abscissa (x), data point ordinate (y), three Euler angles ( ), most significant distortion (Most apparent distortion, MAD), picture quality (BC), band slope (BS), number of bands (Bands), error code (Error); the numerical matrix is ;

[0062] Thus, the EBSD standard format file converted from the forward scattering detector images of the two EBSD detection experiments can be obtained. The converted EBSD data corresponds one-to-one with each data point of the collected data including the identifiable data point information of the target material, realizing the alignment of the macro and micro pixel levels;

[0063] Step 1.3.4: Use the EBSD analysis software to export the corresponding AllEuler diagrams of a-EBSD data and b-EBSD data respectively, set the AllEuler diagram exported from a-EBSD data as the standard diagram, and set the AllEuler diagram exported from b-EBSD data as the diagram to be corrected;

[0064] Step 2: Overlap the standard image and the image to be corrected and intercept the overlapping range. According to the intercepted range, intercept the standard image and the image to be corrected. At the same time, intercept the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data. Use the image registration technology to register the intercepted standard image and the image to be corrected and calculate the target change matrix.

[0065] Step 2.1: Use image processing software to overlap the standard image and the image to be corrected, obtain the overlapping area of ​​the standard image and the image to be corrected, intercept the area where the standard image and the image to be corrected completely overlap, obtain the intercepted standard image and the image to be corrected, and intercept the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data according to this interception range;

[0066] Step 2.1.1: Normalize the coordinates of the data points in the standard image and the image to be corrected;

[0067] Find a common data point in the standard image and the image to be corrected as the origin, and record the coordinates of the origin as a(x,y) and b(x,y) respectively. Then extract all the data points in the two images. The coordinate set of the data points in the standard image is recorded as Li(x,y), and the coordinate set of the data points in the image to be corrected is recorded as Lj(x,y). Subtract the coordinates of the origin a(x,y) and b(x,y) from the coordinates of the data points in the standard image and the image to be corrected, respectively, to obtain the normalized coordinate sets of the data points in the standard image and the image to be corrected, which are recorded as Li-a(x,y) and Lj-b(x,y) respectively.

[0068] After the above calculation, the data point coordinates of the standard image and the image to be corrected can be normalized, and the data point coordinates in the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data can also be normalized.

[0069] Step 2.1.2: Calculate the maximum and minimum values ​​of the horizontal and vertical coordinates of the overlapping area of ​​the standard image and the image to be corrected, and obtain the range of the overlapping area as the interception range;

[0070] The maximum value of the horizontal coordinates in the normalized data point coordinate sets Li-a(x,y) and Lj-b(x,y) in the standard image and the image to be corrected is obtained respectively, and the minimum value between the two is taken as the maximum value of the horizontal coordinates of the overlapping area; the minimum value of the horizontal coordinates in the normalized data point coordinate sets Li-a(x,y) and Lj-b(x,y) in the standard image and the image to be corrected is obtained respectively, and the maximum value between the two is taken as the minimum value of the horizontal coordinates of the overlapping area; the maximum and minimum values ​​of the vertical coordinates of the overlapping area are obtained in the same way as the horizontal coordinates; the range of the overlapping area is obtained by using the maximum and minimum values ​​of the horizontal and vertical coordinates of the overlapping area of ​​the standard image and the image to be corrected, and the range of the overlapping area is taken as the interception range of the standard image and the image to be corrected;

[0071] The process of obtaining the range of the overlapping area is as follows:

[0072] Get the normalized data point coordinate set in the standard graph: Li-a(x,y)=Li(x,y)-a(x,y);

[0073] Get the normalized data point coordinate set in the image to be corrected: Lj-b(x,y)=Lj(x,y)-b(x,y);

[0074] Get the maximum value of the horizontal coordinate in the normalized data point coordinate set in the standard graph: max(Li-a(x));

[0075] Get the maximum value of the horizontal coordinate in the normalized data point coordinate set in the image to be corrected: max(Lj-b(x));

[0076] Compare the maximum value of the horizontal coordinate in the normalized data point coordinate set in the standard image with the maximum value of the horizontal coordinate in the normalized data point coordinate set in the image to be corrected, and take the minimum value of the two as the maximum value of the horizontal coordinate in the overlapping area: min(max(Li-a(x)),max(Lj-b(x)));

[0077] Get the minimum value of the horizontal coordinate in the normalized data point coordinate set in the standard graph: min(Li-a(x));

[0078] Get the minimum value of the horizontal coordinate in the normalized data point coordinate set in the image to be corrected: min(Lj-b(x));

[0079] Compare the minimum value of the horizontal coordinate in the normalized data point coordinate set in the standard image with the minimum value of the horizontal coordinate in the normalized data point coordinate set in the image to be corrected, and take the maximum value of the two as the minimum value of the horizontal coordinate of the overlapping area: max(min(Li-a(x)),min(Lj-b(x)));

[0080] Get the maximum value of the ordinate in the normalized data point coordinate set in the standard graph: max(Li-a(y));

[0081] Get the maximum value of the ordinate in the normalized data point coordinate set in the image to be corrected: max(Lj-b(y));

[0082] Compare the maximum value of the ordinate in the normalized data point coordinate set in the standard image with the maximum value of the ordinate in the normalized data point coordinate set in the image to be corrected, and take the minimum value of the two as the maximum value of the ordinate in the overlapping area: min(max(Li-a(y)),max(Lj-b(y)));

[0083] Get the minimum value of the ordinate in the normalized data point coordinate set in the standard graph: min(Li-a(y));

[0084] Get the minimum value of the ordinate in the normalized data point coordinate set in the image to be corrected: min(Lj-b(y));

[0085] Compare the maximum value of the ordinate in the normalized data point coordinate set in the standard image with the maximum value of the ordinate in the normalized data point coordinate set in the image to be corrected, and take the minimum value of the two as the maximum value of the ordinate in the overlapping area: max(min(Li-a(y)),min(Lj-b(y)));

[0086] Step 2.1.3: Based on the interception range, the overlapping area of ​​the standard image and the image to be corrected is intercepted to obtain the intercepted standard image and the image to be corrected. Similarly, the normalized α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data are intercepted based on the interception range;

[0087] The standard image and the image to be corrected are intercepted according to the horizontal coordinates max(min(Li-a(x)), min(Lj-b(x))), min(max(Li-a(x)), max(Lj-b(x))), and the vertical coordinates max(min(Li-a(y)), min(Lj-b(y))), min(max(Li-a(y)), max(Lj-b(y))) to obtain a completely corresponding spatial relationship between the standard image and the image to be corrected;

[0088] Step 2.2: Use image processing software to perform grayscale processing on the intercepted standard image and the image to be corrected, use image registration technology to identify the data points in the intercepted standard image and the image to be corrected, and match the feature data points of appropriate quantity and quality by setting the quality threshold (MetricThreshold) of the feature points, the spatial scale of detection (NumOctaves) and the detection accuracy (NumOctaves) and align the feature data points;

[0089] Step 2.3: Match the geometric transformation between the intercepted standard image and the image to be corrected according to the aligned feature data points, and calculate the target transformation matrix;

[0090] The target transformation matrix is ​​calculated based on the matchFeatures function, and the specific process is:

[0091] Step 2.3.1: Screen the valid feature data points in the intercepted standard image and the image to be corrected;

[0092] The standard image after interception and the image to be corrected are set as shown in the following formula:

[0093] ;in, and They correspond to the intercepted standard image and the image to be corrected, represents the jth feature descriptor in the i-th image, i=1, 2, j=1, 2, 3...m;

[0094] The standard image after interception Each feature descriptor in Find the closest and second closest feature descriptors , and their distances are and , use the ratio test to determine the feature descriptor Is it a valid feature data point for subsequent calculations? / <threshold, then the feature descriptor is considered In the standard diagram If it is valid, it is counted as a valid feature data point, otherwise the feature descriptor is not recorded ; The image to be corrected after interception The method of screening effective feature data points and the standard graph after interception The screening method is the same;

[0095] Step 2.3.2: Obtain the coordinates of the effective feature data points in the intercepted image to be corrected that are aligned with the effective feature data points in the intercepted standard image, and calculate the target change matrix using the transformation relationship between the coordinates and the coordinates of the effective feature data points in the intercepted standard image;

[0096] Set the change matrix of the intercepted image to be corrected as T, and use the least squares method to evaluate the change matrix. The goal is to find the most accurate transformation matrix T so that , and then get the target transformation matrix;

[0097] in, and is the coordinates of the valid feature data points in the intercepted standard image, and The coordinates of the effective feature data points in the intercepted image to be corrected that are aligned with the effective feature data points in the standard image;

[0098] Step 3: Use the target transformation matrix to geometrically correct the data points of the intercepted b-EBSD data to obtain the intercepted and geometrically corrected b-EBSD data. Use the EBSD analysis software to export the AllEuler diagram of the intercepted and geometrically corrected b-EBSD data, and compare it with the intercepted standard diagram to check whether the two AllEuler diagrams are completely overlapped. If not, return to step 2 to recalculate the target change matrix, and use the updated target change matrix to geometrically correct the data points of the intercepted b-EBSD data until the contours are completely corresponding; then use the updated target transformation matrix to geometrically correct the intercepted β-EBSD data to obtain the intercepted and geometrically corrected β-EBSD data;

[0099] Step 4: Use EBSD processing software to compare the intercepted α-EBSD data obtained in step 2 with the intercepted and geometrically corrected β-EBSD data obtained in step 3, find a grain cluster with an orientation deviation within the threshold at the same position, and randomly select a grain in the grain cluster and record the Euler angle of the grain in the two EBSD detection experiments, calculate the rotation difference matrix of the Euler angle of the grain in the two EBSD detection experiments, perform the same orientation correction on the intercepted and geometrically corrected β-EBSD data obtained in step 3, and obtain the final β-EBSD data;

[0100] Step 4.1: Compare the α-EBSD data intercepted in step 2.1.3 with the intercepted and geometrically corrected b-EBSD data obtained in step 3 through EBSD processing software, and compare and observe the same position of the two data to find the grain cluster with orientation deviation within the threshold (it is generally believed that a set of adjacent data points in EBSD data with Euler angle deviation less than 5° is a grain. In this embodiment, 3 or more adjacent grains are set as a grain cluster). Select any grain in the grain cluster of the intercepted α-EBSD data, select any data point in the grain, record the coordinates and Euler angle of the data point, and set it as the target Euler angle, recorded as Euler1( ); and find the data point with the same coordinates in the intercepted and geometrically corrected β-EBSD data, and record the Euler angle of the data point, which is set as the initial Euler angle, denoted as Euler2( ); In this embodiment, the threshold is set to 3°;

[0101] Convert the target Euler angle and the initial Euler angle into an orientation transformation matrix, and calculate the rotation difference matrix between the target Euler angle and the initial Euler angle. The specific calculation process is as follows:

[0102] The orientation transformation matrix of the target Euler angle is as follows:

[0103] ;in, is the orientation transformation matrix of the target Euler angle, is the target Euler angle 1, is the target Euler angle 2, is the target Euler angle 3;

[0104] The calculation method of the orientation conversion matrix of the initial Euler angle is the same as that of the target Euler angle. The orientation conversion matrix of the initial Euler angle is shown as follows:

[0105] ;in, is the orientation transformation matrix of the initial Euler angles, is the initial Euler angle 1, is the initial Euler angle 2, is the initial Euler angle 3;

[0106] Calculate the rotation difference matrix between the target Euler angle and the initial Euler angle. The calculation formula is as follows:

[0107] ;in, is the rotation difference matrix;

[0108] Step 4.2: The truncated and geometrically corrected β-EBSD data obtained in step 3 is left-multiplied by the rotation difference matrix to complete the orientation correction to obtain the final β-EBSD data, which completely corresponds to the data points of the truncated α-EBSD data and has undergone geometric and orientation correction;

[0109] The orientation conversion matrix of the initial Euler angle is oriented corrected using the rotation difference matrix, and the initial Euler angle is converted into the orientation-corrected Euler angle. The calculation formula of the orientation-corrected matrix is ​​as follows:

[0110] ;in, is the matrix after orientation correction;

[0111] The rotation difference matrix is ​​used to perform orientation correction on the intercepted and geometrically corrected β-EBSD data, so that the final β-EBSD data completely corresponds to the data points of the intercepted α-EBSD data.

[0112] After the EBSD data of the test is obtained by the present invention, since the surface morphology will not change significantly after heat treatment or mechanical treatment, the scattering detector image can be used to record the morphology to align the test results before and after, and the macroscopic morphology can be used to align the microscopic morphology, thereby avoiding shape distortion and orientation deviation caused by inconsistent sample pasting positions, realizing pixel-level alignment of the experimental data of the two tests before and after, and completely eliminating the interference of redundant data points on the experimental results.

[0113] The present invention processes Quasi-in-situ EBSD data to correct the orientation deviation caused by the different relative positions of the sample and the detector in two tests before and after, so that the test sample is completely aligned in the two test processes before and after, eliminates the interference of the spatial position on the data, and on this basis eliminates the influence of redundant interfering data points. Figure 2These are the forward scattering detector images recorded by the Aztec system in two EBSD detection experiments. The left one is the forward scattering detector image obtained from the first EBSD detection experiment, and the right one is the forward scattering detector image obtained from the second EBSD detection experiment. As can be seen from the dotted lines on the figure, there is a lot of morphological information on the surface that can help us align; the overlapping areas collected in the two experiments are inconsistent in space and need to be cropped. It can be seen in the figure that the dotted lines are not horizontal lines and the tilt angles are inconsistent, indicating that the feature points are slightly distorted. This is caused by the different relative positions of the sample and the detector during the two measurements, which will cause slight deviations in the position and orientation of the data points, usually less than 0.01% on average. .

[0114] Figure 3 The front scattering detector image cropped using the method of the present invention and the front scattering detector image after correction show that the morphological information in the two front scattering detector images can correspond, and the dotted lines in the figures are all horizontal lines of the same length, which shows that the distortion caused by the different relative positions of the sample and the detector has been eliminated. After cropping, each pixel point in the two images corresponds one by one, achieving accurate alignment of the data points of the two sample collections in the quasi-in-situ data collection process, and achieving the correspondence between the microscopic and macroscopic morphologies.

[0115] Figure 4(a) shows the orientation image obtained by the first EBSD test of the sample and records the Euler angle of a grain; Figure 4(b) shows the orientation image of the sample after heat treatment in the second EBSD test and records the Euler angle of the same grain, and calculates its orientation deviation from that before heat treatment; Figure 4(c) shows the orientation image after orientation alignment based on Figure 4(b) and records the Euler angle of the same grain. The above orientation images are all intercepted. It can be seen from the figure that the orientation difference between the first test and the last test can reach 1.3°, and the deviation angle is no more than 0.2° after orientation alignment.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A quasi-in-situ EBSD data analysis method, characterized in that: The following steps are involved: Step 1: Perform two EBSD detection experiments on the target material, collect the forward scattering detector image and original EBSD data of the target material, and modify the resolution of the forward scattering detector image obtained from the two EBSD detection experiments to be consistent with the corresponding EBSD data, and output the forward scattering detector image after the modified resolution as modified EBSD data and record it, and export the corresponding full Euler image of the modified EBSD data as the standard image and the image to be corrected; The original EBSD data includes the identifiable data point information of the target material; the original EBSD data obtained from the first EBSD detection experiment is recorded as α-EBSD data; the original EBSD data obtained from the second EBSD detection experiment is recorded as β-EBSD data; the EBSD data output from the front scattering detector image with the modified resolution of the first EBSD detection experiment is recorded as a-EBSD data; the EBSD data output from the front scattering detector image with the modified resolution of the second EBSD detection experiment is recorded as b-EBSD data; Step 2: Overlap the standard image and the image to be corrected and intercept the overlapping range. According to the interception range, intercept the standard image and the image to be corrected. At the same time, intercept the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data. Align the intercepted standard image and the image to be corrected and calculate the target change matrix. Step 3: Use the target transformation matrix to geometrically correct the data points of the intercepted b-EBSD data to obtain the intercepted and geometrically corrected b-EBSD data, export the full Euler diagram of the intercepted and geometrically corrected b-EBSD data, compare it with the intercepted standard diagram, and check whether the two full Euler diagrams completely overlap. If not, return to step 2 to recalculate the target change matrix, and use the updated target change matrix to geometrically correct the data points of the intercepted b-EBSD data until the contours completely correspond; then use the updated target transformation matrix to geometrically correct the intercepted β-EBSD data to obtain the intercepted and geometrically corrected β-EBSD data; Step 4: Compare the intercepted α-EBSD data obtained in step 2 with the intercepted and geometrically corrected β-EBSD data obtained in step 3, find a grain cluster with orientation deviation within the threshold at the same position, select a grain in the grain cluster and record the Euler angle of the grain in the two EBSD detection experiments, calculate the rotation difference matrix of the Euler angle of the grain in the two EBSD detection experiments, perform the same orientation correction on the intercepted and geometrically corrected β-EBSD data obtained in step 3, and obtain the final β-EBSD data.

2. A quasi-in-situ EBSD data analysis method according to claim 1, characterized in that: The specific method of step 1 is: Step 1.1: Record the pasting direction and position of the target material on the sample stage, perform the first EBSD detection experiment on the target material area of ​​interest to obtain the forward scattering detector image and the EBSD data including the identifiable data point information of the target material, and record the EBSD data as α-EBSD data; Step 1.2: Take out the target material for heat treatment or mechanical treatment, paste the target material in the pasting direction and position in the first experiment, and conduct a second EBSD detection experiment. The detection step length remains the same as the first time. The forward scattering detector image and the EBSD data of the identifiable data points of the target material are also obtained. The EBSD data are recorded as β-EBSD data; Step 1.3: Modify the resolution of the forward scattering detector images obtained from the two EBSD detection experiments to be consistent with their corresponding EBSD data, record the EBSD data output from the forward scattering detector image with the modified resolution in the first EBSD detection experiment as a-EBSD data, and record the EBSD data output from the forward scattering detector image with the modified resolution in the second EBSD detection experiment as b-EBSD data; export the corresponding full Euler diagrams from the a-EBSD data and b-EBSD data respectively, set the full Euler diagram exported from the a-EBSD data as the standard diagram, and set the full Euler diagram exported from the b-EBSD data as the diagram to be corrected.

3. A quasi-in-situ EBSD data analysis method according to claim 2, characterized in that: The specific method of step 1.3 is: Step 1.3.1: Check the gratings of α-EBSD data and β-EBSD data, and change the image resolution of the forward scatter detector images obtained from the first EBSD test experiment and the second EBSD test experiment to be consistent with the gratings of α-EBSD data and β-EBSD data respectively; Step 1.3.2: extract the three color channel values ​​of the RGB color system of each pixel point in the front scattering detector image obtained from the first EBSD detection experiment and the second EBSD detection experiment after the modification, sort them, and save them as a color value matrix; Step 1.3.3: Extract the pixel data from the α-EBSD data and the β-EBSD data respectively and save them as two pixel data numerical matrices, and replace the Euler angles of the two pixel data numerical matrices with the values ​​in the color numerical matrix obtained in step 1.3.2 respectively, and keep the values ​​of other pixel data in the pixel data matrix unchanged to obtain two new numerical matrices, and output the two new numerical matrices as EBSD standard format files, recorded as a-EBSD data and b-EBSD data; Step 1.3.4: Export the corresponding full Euler diagrams of a-EBSD data and b-EBSD data respectively, set the full Euler diagram exported from a-EBSD data as the standard diagram, and set the full Euler diagram exported from b-EBSD data as the diagram to be corrected.

4. A quasi-in-situ EBSD data analysis method according to claim 3, characterized in that: The specific method of step 2 is: Step 2.1: Use image processing software to overlap the standard image and the image to be corrected, obtain the overlapping area of ​​the standard image and the image to be corrected, intercept the area where the standard image and the image to be corrected completely overlap, obtain the intercepted standard image and the image to be corrected, and intercept the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data according to this interception range; Step 2.2: grayscale the intercepted standard image and the image to be corrected, use the image registration technology to identify the data points in the intercepted standard image and the image to be corrected, and match the feature data points of appropriate quantity and quality by setting the quality threshold of the feature points, the spatial scale of the detection, and the detection accuracy, and align the feature data points; Step 2.3: Match the geometric transformation between the intercepted standard image and the image to be corrected according to the aligned feature data points, and calculate the target transformation matrix.

5. A quasi-in-situ EBSD data analysis method according to claim 4, characterized in that: The specific method of step 2.1 is: Step 2.1.1: Normalize the coordinates of the data points in the standard image and the image to be corrected; Find a common data point in the standard image and the image to be corrected as the origin, and record the coordinates of the origin as a(x,y) and b(x,y) respectively. Then extract all the data points in the two images. The coordinate set of the data points in the standard image is recorded as Li(x,y), and the coordinate set of the data points in the image to be corrected is recorded as Lj(x,y). Subtract the coordinates of the origin a(x,y) and b(x,y) from the coordinates of the data points in the standard image and the image to be corrected, respectively, to obtain the normalized coordinate sets of the data points in the standard image and the image to be corrected, which are recorded as Li-a(x,y) and Lj-b(x,y) respectively. After the above calculation, the data point coordinates of the standard image and the image to be corrected can be normalized, and the data point coordinates in the α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data can also be normalized; Step 2.1.2: Calculate the maximum and minimum values ​​of the horizontal and vertical coordinates of the overlapping area of ​​the standard image and the image to be corrected, and obtain the range of the overlapping area as the interception range; The maximum value of the horizontal coordinates in the normalized data point coordinate sets Li-a(x,y) and Lj-b(x,y) in the standard image and the image to be corrected is obtained respectively, and the minimum value between the two is taken as the maximum value of the horizontal coordinates of the overlapping area; the minimum value of the horizontal coordinates in the normalized data point coordinate sets Li-a(x,y) and Lj-b(x,y) in the standard image and the image to be corrected is obtained respectively, and the maximum value between the two is taken as the minimum value of the horizontal coordinates of the overlapping area; the maximum and minimum values ​​of the vertical coordinates of the overlapping area are obtained in the same way as the horizontal coordinates; the range of the overlapping area is obtained by using the maximum and minimum values ​​of the horizontal and vertical coordinates of the overlapping area of ​​the standard image and the image to be corrected, and the range of the overlapping area is taken as the interception range of the standard image and the image to be corrected; Step 2.1.3: Based on the interception range, the overlapping area of ​​the standard image and the image to be corrected is intercepted to obtain the intercepted standard image and the image to be corrected. Similarly, the normalized α-EBSD data, β-EBSD data, a-EBSD data and b-EBSD data are intercepted based on the interception range.

6. A quasi-in-situ EBSD data analysis method according to claim 5, characterized in that: The specific method of step 2.3 is: Step 2.3.1: Screen the valid feature data points in the intercepted standard image and the image to be corrected; Step 2.3.2: Obtain the coordinates of the effective feature data points in the intercepted image to be corrected that are aligned with the effective feature data points in the intercepted standard image, and calculate the target change matrix using the transformation relationship between the coordinates and the coordinates of the effective feature data points in the intercepted standard image.

7. A quasi-in-situ EBSD data analysis method according to claim 6, characterized in that: The specific method of step 4 is: Step 4.1: Compare the α-EBSD data intercepted in step 2.1.3 with the b-EBSD data intercepted and geometrically corrected in step 3, and compare and observe at the same position of the two data to find the grain cluster whose orientation deviation is within the threshold value, select any grain in the grain cluster of the intercepted α-EBSD data, and select any data point in the grain, record the coordinates and Euler angle of the data point, and set it as the target Euler angle; and find the data point with the same coordinates in the intercepted and geometrically corrected β-EBSD data, and record the Euler angle of the data point, and set it as the initial Euler angle; Step 4.2: The truncated and geometrically corrected β-EBSD data obtained in step 3 is left-multiplied by the rotation difference matrix to complete the orientation correction and obtain the final β-EBSD data, which completely corresponds to the data points of the truncated α-EBSD data.

8. A quasi-in-situ EBSD data analysis method according to claim 7, characterized in that: The specific method of step 4.1 is: converting the target Euler angle and the initial Euler angle into an orientation conversion matrix, and calculating the rotation difference matrix between the target Euler angle and the initial Euler angle. The specific calculation process is as follows: The orientation transformation matrix of the target Euler angle is as follows: ;in, is the orientation transformation matrix of the target Euler angle, is the target Euler angle 1, is the target Euler angle 2, is the target Euler angle 3; The calculation method of the orientation conversion matrix of the initial Euler angle is the same as that of the target Euler angle. The orientation conversion matrix of the initial Euler angle is shown as follows: ;in, is the orientation transformation matrix of the initial Euler angles, is the initial Euler angle 1, is the initial Euler angle 2, is the initial Euler angle 3; Calculate the rotation difference matrix between the target Euler angle and the initial Euler angle. The calculation formula is as follows: ;in, is the rotation difference matrix.

9. A quasi-in-situ EBSD data analysis method according to claim 8, characterized in that: The specific method of step 4.2 is: use the rotation difference matrix to perform orientation correction on the orientation conversion matrix of the initial Euler angle, and convert the initial Euler angle into the Euler angle after orientation correction. The calculation formula of the matrix after orientation correction is shown as follows: ;in, is the matrix after orientation correction; The rotation difference matrix is ​​used to perform orientation correction on the intercepted and geometrically corrected β-EBSD data, so that the final β-EBSD data completely corresponds to the data points of the intercepted α-EBSD data.

Images