Crystal structure analysis apparatus and method

Abstract

A crystal structure analysis apparatus and method are provided. The crystal structure analysis apparatus may include: an EBSD (Electron Backscatter Diffraction) data acquisition module for acquiring EBSD data for a solid material; an image generation module for generating the shape of the solid material as a first image containing a plurality of pixels; a clustering module for performing clustering on the plurality of pixels using the EBSD data so that the first image contains a plurality of clusters each indicating a crystal orientation; an image processing module for processing the first image so that each cluster is displayed in a different color to generate a second image; and a rendering module for rendering the second image onto a display area.

Description

Technical Field

[0001] Cross-reference of related applications This application claims the benefit of priority based on Korean Patent Application No. 10-2023-0075601, filed on June 13, 2023, and all the contents disclosed in the documents of the Korean patent application are incorporated herein by reference.

[0002] The disclosure relates to an apparatus and method for analyzing crystal structures.

Background Art

[0003] EBSD (Electron Backscatter Diffraction) is attached to a scanning electron microscope and can analyze the orientation of a material by detecting electrons (backscattered electrons) that are reflected when accelerated electrons are injected into a sample. That is, EBSD can analyze the crystal structure in an irradiated area using diffraction patterns measured from each crystal. For example, a computer system controls the position of an electron beam, a camera records the pattern generated in the test piece area where the electron beam stays, and the recorded pattern is automatically analyzed to calculate crystallographic information of the test piece area.

Summary of the Invention

Problems to be Solved by the Invention

[0004] One problem to be solved is to provide an apparatus and method for analyzing the crystal structure of a solid material effectively from EBSD data of the solid material.

Means for Solving the Problems

[0005] A crystal structure analyzer according to one embodiment may include: an EBSD (Electron Backscatter Diffraction) data acquisition module for acquiring EBSD data for a solid material; an image generation module for generating a first image containing a plurality of pixels representing the shape of the solid material; a clustering module for performing clustering on the plurality of pixels using the EBSD data so that the first image contains a plurality of clusters each indicating a crystal orientation; an image processing module for generating a second image by processing the first image so that each cluster is displayed in a different color; and a rendering module for rendering the second image onto a display area.

[0006] In some embodiments, the EBSD data includes a plurality of angular data collected for each crystal unit of a predetermined size forming the solid material, and the clustering module can perform clustering on the plurality of pixels using the plurality of angular data.

[0007] In some embodiments, the first image includes a first pixel and a second pixel, the plurality of angular data includes first angular data corresponding to the first pixel and second angular data corresponding to the second pixel, and the clustering module can group the first pixel and the second pixel into the same first pixel group if the difference between the first angular data and the second angular data is less than or equal to a predetermined first threshold.

[0008] In some embodiments, the clustering module determines the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is equal to or greater than a second threshold, and does not need to determine the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is less than the second threshold.

[0009] In some embodiments, the first pixel group includes a third pixel and a fourth pixel, and the clustering module can group the third pixel and the fourth pixel into the same second pixel group if the distance between the positions of the third pixel and the fourth pixel is less than or equal to a predetermined third threshold.

[0010] In some embodiments, the clustering module determines the second pixel group as a valid cluster if the number of pixels grouped in the second pixel group is equal to or greater than the fourth threshold, and does not need to determine the second pixel group as a valid cluster if the number of pixels grouped in the second pixel group is less than the fourth threshold.

[0011] In some embodiments, the clustering module can perform additional clustering on pixels that have not been determined to be part of the valid cluster.

[0012] In some embodiments, the clustering module can calculate the area of ​​the multiple clusters and assign numbers to each of the multiple clusters in ascending or descending order according to the size of the area.

[0013] In some embodiments, the rendering module can render the numbers assigned to each of the multiple clusters onto the second image.

[0014] In some embodiments, the color may include at least one of RGB, HSL, HSV, CMYK, and grayscale colors.

[0015] In some embodiments, the display area includes a first display area and a second display area, and the rendering module can render the first image in the first display area and the second image in the second display area.

[0016] In some embodiments, the crystal structure analyzer may further include a region removal module that removes regions from the first image that do not correspond to the target of analysis of the crystal structure.

[0017] In some embodiments, the region removal module sets a region to be removed from or retained from the first image based on user input to the first image, and the user input may include mouse input or touch input.

[0018] In some embodiments, the region removal module can analyze the first image using a predetermined algorithm and set regions to be removed or retained from the first image.

[0019] A crystal structure analyzer according to one embodiment may include the steps of: acquiring EBSD data for a solid material and including a plurality of angular data collected for each crystal unit of a predetermined size forming the solid material; generating the shape of the solid material as a first image including a plurality of pixels; performing clustering on the plurality of pixels using the EBSD data so that the first image includes a plurality of clusters each indicating a crystal orientation; processing the first image so that each cluster is displayed in a different color to generate a second image; and rendering the second image onto a display area.

[0020] In some embodiments, the first image includes a first pixel and a second pixel, the plurality of angular data includes first angular data corresponding to the first pixel and second angular data corresponding to the second pixel, and the clustering step may include a step of grouping the first pixel and the second pixel into the same first pixel group if the difference between the first angular data and the second angular data is less than or equal to a predetermined first threshold.

[0021] In some embodiments, the clustering step may further include determining the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is equal to or greater than a second threshold, and not determining the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is less than the second threshold.

[0022] In some embodiments, the first pixel group includes a third pixel and a fourth pixel, and the clustering step may further include a step of grouping the third pixel and the fourth pixel into the same second pixel group if the distance between the positions of the third pixel and the fourth pixel is less than or equal to a predetermined third threshold.

[0023] In some embodiments, the clustering step may further include determining the second pixel group as a valid cluster if the number of pixels grouped into the second pixel group is equal to or greater than a fourth threshold, and not determining the second pixel group as a valid cluster if the number of pixels grouped into the second pixel group is less than the fourth threshold.

[0024] A computer-readable medium according to an embodiment may record a program for causing a computer including a processor that executes a program or instructions stored in a memory or a storage device to perform the steps of: obtaining EBSD data for a solid material; generating the shape of the solid material as a first image including a plurality of pixels; performing clustering on the plurality of pixels using the EBSD data such that the first image includes a plurality of clusters each indicating a crystal direction; processing the first image to generate a second image such that the first image is displayed in different colors for each cluster; and rendering the second image in a display area.

Advantages of the Invention

[0025] According to an embodiment, by extracting, visualizing, and providing information regarding how the crystal directions are distributed over the entire particles with respect to the data collected from an EBSD system, the crystal structure of a solid material can be effectively analyzed from the EBSD data for the solid material. Further, when a user adjusts parameter values while the crystal structure is visually represented, the result is immediately reflected, and the connection pattern of crystal directions can be analyzed from the EBSD data provided as two-dimensional data in a general computing environment within a short time.

Brief Description of the Drawings

[0026] [Figure 1] FIG. 1 is a block diagram for explaining a crystal structure analysis apparatus according to an embodiment. [Figure 2] FIG. 2 is a diagram for explaining the operation of a crystal structure analysis apparatus according to an embodiment. [Figure 3A] FIG. 3A is a diagram for explaining the operation of a crystal structure analysis apparatus according to an embodiment. [Figure 3B] FIG. 3B is a diagram for explaining the operation of a crystal structure analysis apparatus according to an embodiment. [Figure 3C]Figure 3C is a diagram illustrating the operation of a crystal structure analyzer according to one embodiment. [Figure 4] Figure 4 is a diagram illustrating one example of a crystal structure analyzer according to one embodiment. [Figure 5] Figure 5 is a flowchart illustrating a crystal structure analysis method according to one embodiment. [Figure 6] Figure 6 is a diagram illustrating one example of a crystal structure analyzer according to one embodiment. [Figure 7] Figure 7 is a diagram illustrating one example of a crystal structure analyzer according to one embodiment. [Figure 8] Figure 8 is a block diagram illustrating a crystal structure analyzer according to one embodiment. [Figure 9] Figure 9 is a diagram illustrating an example of a crystal structure analyzer based on one embodiment. [Figure 10] Figure 10 is a diagram illustrating an example of a crystal structure analyzer based on one embodiment. [Figure 11] Figure 11 is a diagram illustrating a computing device according to one embodiment. [Modes for carrying out the invention]

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings, so that they can be easily implemented by a person with ordinary skill in the art to which the present invention pertains. However, the present invention can be realized in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly illustrate the present invention, unnecessary parts have been omitted from the drawings, and similar parts throughout the specification are denoted by similar reference numerals.

[0028] When a part of the specification and claims “includes” a component, unless otherwise stated, this means that it may further include other components rather than excluding them. Ordinal terms such as “first,” “second,” etc., can be used to describe a variety of components, but such components are not limited by such terms. Such terms are used solely for the purpose of distinguishing one component from another.

[0029] The terms "...part," "...device," and "module" as used in this specification mean a unit capable of performing at least one function or operation as described herein, which can be realized in hardware or circuitry, software, or a combination of hardware or circuitry and software.

[0030] Figure 1 is a block diagram illustrating a crystal structure analyzer according to one embodiment.

[0031] Referring to Figure 1, a crystal structure analyzer 10 according to one embodiment can analyze the crystal structure of a solid material. The crystal structure analyzer 10 can extract and visualize information about how the crystal orientation is distributed relative to the whole particle from the data collected from the EBSD system. In some embodiments, the crystal structure analyzer 10 may be a server serving a web application, which is software that runs in a web browser. This allows the crystal structure analyzer 10 to easily analyze the crystal structure of a solid material using only a web browser, without the need to install any other software on their computer. However, the scope of the present invention is not limited thereto, and the functions provided by the crystal structure analyzer 10 can be implemented as various forms of software.

[0032] The crystal structure analyzer 10 may include an EBSD data acquisition module 110, an image generation module 120, a clustering module 130, an image processing module 140, and a rendering module 150.

[0033] The EBSD data acquisition module 110 can acquire EBSD data for solid materials. The EBSD data may include spatially linked crystallographic orientation and phase information, and the EBSD data acquisition module 110 can acquire data in various formats collected from external EBSD systems. For example, the EBSD data may be two-dimensional data distinguished by separators such as commas, and may be a file generated to conform to the CSV (Comma Separated Values) format. As another example, the EBSD data may be in the form of a compressed file consisting of a collection of multiple CSV files. As yet another example, the EBSD data may be data that has undergone data cleaning using specific software (e.g., AZtecCrystal) that processes data collected using EBSD.

[0034] The image generation module 120 can generate a first image representing the shape of the solid material to be subjected to crystal structure analysis. Here, the first image may be in the form of a pixel image containing multiple pixels. For example, the image generation module 120 can generate the first image as a two-dimensional image that can be displayed on a display device electrically connected to the crystal structure analyzer 10.

[0035] The clustering module 130 can perform clustering on multiple pixels included in the first image using EBSD data acquired by the EBSD data acquisition module 110. In some embodiments, the clustering module 130 uses the EBSD data to make the first image include multiple clusters, and each cluster can be formed according to the crystal orientation.

[0036] For example, EBSD data may include multiple angular data. These multiple angular data may be collected for each crystal unit of a predetermined size that forms the solid material being analyzed. Here, the crystal unit is not defined to a specific size, but may be assumed to be a unit having a predetermined size that can be associated with at least one of the crystal units included in the first image, taking into account the resolution that the crystal structure analyzer according to the embodiment can display in the environment. The clustering module 130 can perform clustering on multiple pixels that represent the first image using the multiple angular data.

[0037] The image processing module 140 can generate a second image showing the results of clustering performed by the clustering module 130. In other words, the image processing module 140 can process the first image generated by the image generation module 120 so that each cluster is displayed in a different color, and output it as a second image. In some embodiments, the color may include at least one of RGB, HSL, HSV, CMYK, and grayscale colors. RGB colors are colors represented using red, green, and blue, and HSL (or HSB) colors may be colors represented using hue, saturation, and lightness or brightness. HSV colors are colors represented using hue, saturation, and value, and CMYK colors may be colors represented using cyan, magenta, yellow, and black.

[0038] The rendering module 150 can render the second image generated by the image processing module 140 onto a display area provided on a display device. This allows the user to visualize the crystal orientation relative to the overall particle at a glance by representing each cluster with a different color, thus enabling effective analysis of the crystal structure from EBSD data collected in an intuition-less format.

[0039] In some embodiments, the clustering module 130 can perform clustering based on a first parameter value specified by the user in an adjustable manner. Here, the first parameter value can represent a threshold that signifies an angle difference that can be determined to be the same crystal orientation, and can be identified, for example, by the parameter name "angle_threshold". The clustering module 130 can group the first pixel and the second pixel into the same first pixel group if the difference between the first angle data and the second angle data is less than or equal to a predetermined first threshold. Here, the predetermined first threshold can correspond to a first parameter value set in an adjustable manner by the user, the first pixel and the second pixel are pixels that constitute the first image, the first angle data may be the angle data corresponding to the first pixel among multiple angle data of the EBSD data, and the second angle data may be the angle data corresponding to the second pixel among multiple angle data.

[0040] Next, the clustering module 130 can determine whether the first pixel group is a valid cluster based on a second parameter value that can be adjusted by the user. Here, the second parameter value can represent a threshold value that indicates the number of pixels with the same crystal orientation required to distinguish whether or not a cluster has been formed, and can be identified, for example, by the parameter name "angle_min_samples". The clustering module 130 determines the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is greater than or equal to the second threshold, but does not determine the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is less than the second threshold. Here, the predetermined second threshold can correspond to a second parameter value that can be adjusted by the user.

[0041] As a result, the clustering module 130 can determine a cluster to be valid only if the number of pixels with angular data differences that do not exceed the range defined by the first threshold is equal to or greater than the number defined by the second threshold. In other words, pixels with angular data that exceeds the range defined by the first threshold cannot belong to a valid cluster, and even pixels with angular data differences that do not exceed the range defined by the first threshold cannot belong to a valid cluster if the number of such pixels is less than the number defined by the second threshold. Pixels that do not belong to a valid cluster may be subjected to additional clustering or sometimes treated as noise.

[0042] On the other hand, the clustering module 130 can perform primary clustering using the first and second parameter values ​​as described above, and then perform secondary clustering using the third and fourth parameter values, which will be described later.

[0043] Specifically, the clustering module 130 can perform clustering based on a third parameter value that can be adjusted by the user. Here, the third parameter value can represent a threshold that indicates the distance between pixels that can be determined to be adjacent, and can be identified, for example, by the parameter name "pos_eps". If, as a result of primary clustering, the first pixel group includes the third and fourth pixels, the clustering module 130 can group the third and fourth pixels into the same second pixel group if the distance between the positions of the third and fourth pixels is less than or equal to a predetermined third threshold. Here, the predetermined third threshold can correspond to a third parameter value that can be adjusted by the user.

[0044] Next, the clustering module 130 can determine whether a second pixel group is a valid cluster based on a fourth parameter value that can be adjusted by the user. Here, the fourth parameter value can represent a threshold that indicates the number of adjacent pixels required to distinguish whether or not a cluster has been formed, and can be identified, for example, by the parameter name "pos_min_samples". If the number of pixels grouped into the second pixel group is greater than or equal to the fourth threshold, the clustering module 130 determines the second pixel group to be a valid cluster. Conversely, if the number of pixels grouped into the second pixel group is less than the fourth threshold, the second pixel group does not need to be determined to be a valid cluster. Here, the predetermined fourth threshold can correspond to a fourth parameter value that can be adjusted by the user.

[0045] As a result, the clustering module 130 can determine a cluster to be valid only if the number of pixels with inter-pixel distances not exceeding the distance determined by the third threshold is equal to or greater than the number determined by the fourth threshold. In other words, pixels with inter-pixel distances exceeding the distance determined by the third threshold cannot belong to a valid cluster, and even pixels with inter-pixel distances not exceeding the distance determined by the third threshold cannot belong to a valid cluster if the number of such pixels does not reach the number determined by the fourth threshold. Pixels that do not belong to a valid cluster may be subjected to additional clustering or sometimes treated as noise.

[0046] Thus, the clustering module 130 can easily change the clustering level according to the values ​​of the first to fourth parameters, which can be specified by the user. This not only makes it easier to visually grasp the crystal orientations extracted from the EBSD data, but also allows for easy exploration of desired crystal orientation patterns from the clustering results and the crystal orientations corresponding to each cluster, which are displayed differently by changing the values ​​of the first to fourth parameters. For example, any crystal orientation linkage pattern that can be formed between adjacent clusters can be used as a significant analysis result, but analyzing such crystal orientation linkage patterns from EBSD data given as two-dimensional data requires a lot of computing resources and considerable work time. In contrast, according to this embodiment, if only the values ​​of the first to fourth parameters are changed while the crystal orientations are visually represented for each cluster, the results are immediately reflected visually, allowing for the exploration of crystal orientation linkage patterns in a short time in a typical computing environment.

[0047] Figures 2 and 3A to 3C illustrate the operation of a crystal structure analyzer according to one embodiment.

[0048] Referring to Figure 2, the clustering module 130 can group pixels with angular differences that can be determined to have the same crystal orientation in the first image IMG into the same first pixel group, using a first threshold corresponding to a first parameter value that can be adjusted by the user. Next, the clustering module 130 can determine whether the first pixel group is a valid cluster or not, using a second threshold corresponding to a second parameter value that can be adjusted by the user. In this way, the clustering module 130 can determine that only the number of pixels with angular data differences that do not exceed the range determined based on the first threshold are equal to or greater than the number determined based on the second threshold, and these will be valid clusters CLS1, CLS2, and CLS3.

[0049] In other words, cluster CLS1 is a group of pixels having angular data differences that do not exceed a range defined based on the first threshold, and which number is greater than or equal to a number defined based on the second threshold; cluster CLS2 is a group of pixels having angular data differences that do not exceed a range defined based on the first threshold, which number is greater than or equal to a number defined based on the second threshold, and whose angular data distribution does not overlap with the angular data distribution of cluster CLS1; and cluster CL3 is a group of pixels having angular data differences that do not exceed a range defined based on the first threshold, which number is greater than or equal to a number defined based on the second threshold, and whose angular data distribution does not overlap with the angular data distribution of clusters CLS1 and CLS2.

[0050] Referring to Figure 3A, the clustering module 130 can treat cluster CLS1, which is the result of primary clustering in Figure 2, as a valid first pixel group using a third threshold corresponding to a third parameter value specified by the user as adjustable, and group it into the same second pixel group having a pixel distance that can be determined to be adjacent. Next, the clustering module 130 can determine whether the second pixel group is a valid cluster or not using a fourth threshold corresponding to a fourth parameter value specified by the user as adjustable. In this way, the clustering module 130 can determine that clusters CLS11, CLS12, CLS13, CLS14, and CLS15 are valid clusters only if the number of pixels having a pixel distance that does not exceed the distance determined by the third threshold is equal to or greater than the number determined by the fourth threshold.

[0051] Similarly, referring to Figure 3B, the clustering module 130 can determine clusters CLS21 and CLS22 from cluster CLS2, which is the result of primary clustering in Figure 2, in a manner similar to that in Figure 3A. Referring to Figure 3C, the clustering module 130 can determine clusters CLS31, CLS32, and CLS33 from cluster CLS3, which is the result of primary clustering in Figure 2, in a manner similar to that in Figure 3A.

[0052] Figure 4 is a diagram illustrating one example of a crystal structure analyzer according to one embodiment.

[0053] Referring to Figure 4, the left side is Image IMG1, an image generated by the image generation module 120 representing the shape of the solid material to be analyzed for crystal structure, and the right side is Image IMG2, an image representing the angle data values ​​in color based on the angle data obtainable from the EBSD data acquired by the EBSD data acquisition module 110. Here, the color may be expressed using hue, saturation, and value based on the Euler angle of the crystal. After clustering is performed by the clustering module 130, each cluster can be represented by a different color, and the color that distinguishes one cluster can be determined, for example, by averaging the hue, saturation, and value of all the angle data belonging to that cluster. This allows the user to visualize the crystal orientation relative to the whole grain at a glance, thus enabling effective analysis of the crystal structure from EBSD data collected in an intuitive form. An example of clusters being displayed in different colors after clustering can be seen in Image IMG3 of Figure 6.

[0054] Figure 5 is a flowchart illustrating a crystal structure analysis method according to one embodiment.

[0055] Referring to Figure 5, a crystal structure analysis method according to one embodiment may include: step S501, which involves acquiring EBSD data for a solid material and including multiple angle data collected for each crystal unit of a predetermined size that forms the solid material; step S502, which involves generating the shape of the solid material as a first image containing multiple pixels; step S503, which involves clustering the multiple pixels using the EBSD data so that the first image contains multiple clusters, each indicating a crystal orientation; step S504, which involves processing the first image so that each cluster is displayed in a different color to generate a second image; and step S505, which involves rendering the second image onto a display area.

[0056] For detailed information regarding the aforementioned method, please refer to the explanations mentioned above in relation to Figures 1 to 4 and the explanations described later in relation to Figures 6 to 10. Therefore, redundant explanations will be omitted here.

[0057] Figure 6 is a diagram illustrating one example of a crystal structure analyzer according to one embodiment.

[0058] Referring to Figure 6, in some embodiments, the clustering module 130 can calculate the area for each of the multiple clusters and assign numbers to each of the multiple clusters in ascending or descending order according to the size of the area. The rendering module 150 can not only render each of the multiple clusters on the image IMG3 so that they are displayed in different colors, but can also render the numbers assigned to each of the multiple clusters on the image IMG3. The numbers assigned to each cluster may be displayed overlapping the center of the corresponding cluster, making it easy to visually grasp the trend toward the crystal orientation extracted from the EBSD data.

[0059] Figure 7 is a diagram illustrating one example of a crystal structure analyzer according to one embodiment.

[0060] Referring to Figure 7, the rendering module 150 can render the second image generated by the image processing module 140 onto a display area 30 provided on a display device. Here, the display area 30 includes a first display area 31 and a second display area 32, and the rendering module 150 can render the first image onto the first display area 31 and the second image onto the second display area 32. This provides the user with the convenience of being able to visually compare the total particles before and after clustering.

[0061] On the other hand, a parameter value adjustment area 33 may be located in the display area 30. The parameter value adjustment area 33 can display a parameter value input control 34 for setting the analysis result unit. The parameter value adjustment area 33 may also include parameter value input controls 35-38 that allow the user to adjust the level of clustering performed by the clustering module 130.

[0062] Here, parameter value input control 35 may be a control that allows input of the value of the first parameter, which can be identified by the parameter name "angle_threshold", parameter value input control 36 may be a control that allows input of the value of the third parameter, which can be identified by the parameter name "pos_eps", parameter value input control 37 may be a control that allows input of the value of the second parameter, which can be identified by the parameter name "angle_min_samples", and parameter value input control 38 may be a control that allows input of the value of the fourth parameter, which can be identified by the parameter name "pos_min_samples". For more detailed information regarding the first to fourth parameters, please refer to the explanation mentioned above in relation to Figure 1.

[0063] By providing users with such controls, they can adjust the values ​​of the first to fourth parameters in a timely manner, making it easy to change the clustering level of the clustering module 130 at any desired time. This not only makes it easier to visually grasp the crystal orientations extracted from the EBSD data, but also allows for easy exploration of desired crystal orientation patterns from the clustering results and the crystal orientations corresponding to each cluster, which are displayed differently by changing the values ​​of the first to fourth parameters.

[0064] Figure 8 is a block diagram illustrating a crystal structure analyzer according to one embodiment.

[0065] Referring to Figure 8, the crystal structure analyzer 10 according to one embodiment may include an EBSD data acquisition module 110, an image generation module 120, a clustering module 130, an image processing module 140, a rendering module 150, and a region removal module 160. For the EBSD data acquisition module 110, the image generation module 120, the clustering module 130, the image processing module 140, and the rendering module 150, please refer to the descriptions above in relation to Figures 1 to 7.

[0066] The region removal module 160 can remove regions from the first image that do not correspond to the target of crystal structure analysis. This prevents clustering from being performed on unwanted regions during crystal structure analysis, thus preventing the unnecessary waste of computing resources.

[0067] In some embodiments, the region removal module 160 can define regions to be removed from or retained from the first image based on user input to the first image. Here, user input may include mouse input or touch input.

[0068] In some embodiments, the region removal module 160 can analyze a first image using a predetermined algorithm to be executed on the crystal structure analyzer 10, and set regions to be removed or retained from the first image.

[0069] Figures 9 and 10 illustrate an example of a crystal structure analyzer based on one embodiment.

[0070] Referring to Figure 9, the left side is image IMG1, generated by the image generation module 120 to represent the shape of the solid material subject to crystal structure analysis, and the right side is image IMG3, obtained by removing the regions from which the crystal structure analysis is not performed in image IMG1. In some embodiments, the region removal module 160 can distinguish between regions to be analyzed and regions not to be analyzed by receiving user input in accordance with the L1 morphology, and set regions to be removed or retained from the first image. Alternatively, the region removal module 160 may analyze the first image using a predetermined algorithm that is executed so that the result is in the L2 morphology, and set regions to be removed or retained from the first image. In some embodiments, the two methods may be used in combination, such as first attempting to remove regions by user input via mouse or touch input, and if this fails a predetermined number of times, second analyzing the first image using a predetermined algorithm to set regions to be removed or retained from the first image.

[0071] Referring to Figure 10, the left side is Image IMG4, which corresponds to a portion of Image IMG1, and the right side is Image IMG5, which is Image IMG4 with the region where the crystal structure analysis is not performed removed. Similar to Figure 9, in some embodiments, the region removal module 160 can distinguish between regions to be analyzed and regions that are not, by receiving user input in accordance with the L1 morphology, and set regions to be removed or retained from the first image. Alternatively, the region removal module 160 may analyze the first image using a predetermined algorithm that is executed so that the result is in the L2 morphology, and set regions to be removed or retained from the first image. In some embodiments, the two methods may be used in combination, such as first attempting to remove regions by user input via mouse or touch input, and if this fails a predetermined number of times, second analyzing the first image using a predetermined algorithm to set regions to be removed or retained from the first image.

[0072] Figure 11 is a diagram illustrating a computing device according to one embodiment.

[0073] Referring to Figure 11, the crystal structure analysis apparatus and method according to the example can be realized using the computing device 50.

[0074] The computing device 50 may include at least one of a processor 510, memory 530, user interface input device 540, user interface output device 550, and storage device 560 that communicate via bus 520. The computing device 50 may also include a network interface 570 that is electrically connected to network 40. The network interface 570 can transmit or receive signals with other devices via network 40.

[0075] The processor 510 can be implemented in various forms such as an MCU (Micro Controller Unit), AP (Application Processor), CPU (Central Processing Unit), GPU (Graphic Processing Unit), or NPU (Neural Processing Unit), and can be any semiconductor device that executes instructions stored in the memory 530 or storage device 560. The processor 510 can be configured to implement the functions and methods described above in relation to Figures 1 to 10.

[0076] The memory 530 and storage device 560 can include various forms of volatile or non-volatile storage media. For example, the memory can include a ROM (read-only memory) 531 and a RAM (random access memory) 532. In this embodiment, the memory 530 can be located inside or outside the processor 510, and the memory 530 can be connected to the processor 510 via various already known means.

[0077] In some embodiments, at least some of the configurations or functions of the crystal structure analysis apparatus and method according to the embodiments can be implemented by a program or software executed on a computing device 50, and the program or software may be stored on a computer-readable medium.

[0078] In some embodiments, at least some of the configurations or functions of the crystal structure analysis apparatus and method according to the embodiments may be implemented using the hardware or circuits of the computing device 50, or by other hardware or circuits that can be electrically connected to the computing device 50.

[0079] As described above, by extracting and visualizing information about how the crystal orientation is distributed across the entire particle from the data collected by the EBSD system, the crystal structure of a solid material can be effectively analyzed from EBSD data. Furthermore, when the user adjusts parameter values ​​while the crystal structure is visually represented, the results are immediately reflected, allowing for the analysis of the crystal orientation linkage pattern from the EBSD data, which is given as two-dimensional data, in a general computing environment in a short amount of time.

[0080] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto. Various modifications and improvements made by persons with ordinary skill in the art to which the present invention pertains, utilizing the basic concepts of the present invention as defined in the following claims, also fall within the scope of the present invention. [Explanation of symbols]

[0081] 10...Crystal structure analyzer 110 ···EBSD data acquisition module 120 ···Image generation module 130 ···Clustering Module 140 ···Image Processing Module 150 ···Rendering Module 20 ···EBSD data 30 ···Display area 31 ···First display area 32 ···Second display area 33 ···Parameter value adjustment area 34 ···Parameter Value Input Control 35 ···Parameter value input control 36 ···Parameter Value Input Control 37 ···Parameter Value Input Control 38 ···Parameter value input control 40 ···Network 50 ···Computing devices 510 ... Processor 520 ···Bus 530 ···Memory 531 ···ROM 532 ···RAM 540 ···User Interface Input Device 550 ···User Interface Output Device 560 ···Storage device 570 ···Network Interface

Claims

1. An EBSD data acquisition module that acquires EBSD (Electron Backscatter Diffraction) data for solid materials, An image generation module that generates the shape of the solid material as a first image containing multiple pixels, A clustering module that performs clustering on the plurality of pixels using the EBSD data such that the first image includes a plurality of clusters each indicating a crystal orientation, An image processing module that processes the first image so that the first image is displayed in a different color for each cluster and generates a second image, A crystal structure analyzer, comprising a rendering module for rendering the second image onto a display area.

2. The EBSD data includes a plurality of angle data collected for each crystal unit of a predetermined size that forms the solid material. The aforementioned clustering module is The crystal structure analyzer according to claim 1, wherein clustering is performed on the plurality of pixels using the plurality of angle data.

3. The first image described above includes a first pixel and a second pixel, The plurality of angle data include a first angle data corresponding to the first pixel and a second angle data corresponding to the second pixel. The aforementioned clustering module is The crystal structure analyzer according to claim 2, wherein if the difference between the first angle data and the second angle data is less than or equal to a predetermined first threshold, the first pixel and the second pixel are grouped into the same first pixel group.

4. The aforementioned clustering module is The crystal structure analyzer according to claim 3, wherein if the number of pixels grouped in the first pixel group is equal to or greater than a second threshold, the first pixel group is determined to be a valid cluster, and if the number of pixels grouped in the first pixel group is less than the second threshold, the first pixel group is not determined to be a valid cluster.

5. The first pixel group includes the third and fourth pixels, The aforementioned clustering module is The crystal structure analyzer according to claim 4, wherein if the distance between the position of the third pixel and the position of the fourth pixel is less than or equal to a predetermined third threshold, the third pixel and the fourth pixel are grouped into the same second pixel group.

6. The aforementioned clustering module is The crystal structure analyzer according to claim 5, wherein if the number of pixels grouped in the second pixel group is equal to or greater than a fourth threshold, the second pixel group is determined to be a valid cluster, and if the number of pixels grouped in the second pixel group is less than the fourth threshold, the second pixel group is not determined to be a valid cluster.

7. The aforementioned clustering module is The crystal structure analyzer according to claim 5, further comprising performing clustering on pixels that have not been determined to be part of the effective cluster.

8. The aforementioned clustering module is The crystal structure analyzer according to claim 1, wherein the area of ​​the plurality of clusters is calculated, and numbers are assigned to each of the plurality of clusters in ascending or descending order according to the size of the area.

9. The rendering module described above is The crystal structure analyzer according to claim 8, which renders the number assigned to each of the plurality of clusters onto the second image.

10. The crystal structure analyzer according to claim 1, wherein the aforementioned color includes at least one of RGB color, HSL color, HSV color, CMYK color, and grayscale color.

11. The display area includes a first display area and a second display area, The rendering module described above is The first image is rendered onto the first display area, The crystal structure analyzer according to claim 1, which renders the second image onto the second display area.

12. The crystal structure analyzer according to claim 1, further comprising a region removal module for removing regions from the first image that do not correspond to the target of crystal structure analysis.

13. The aforementioned region removal module is A region to be removed from or retained from the first image is set based on user input to the first image. The crystal structure analyzer according to claim 12, wherein the user input includes mouse input or touch input.

14. The aforementioned region removal module is The crystal structure analyzer according to claim 12, comprising analyzing the first image using a predetermined algorithm and setting regions to be removed from or retained from the first image.

15. A step of acquiring EBSD data for a solid material, which includes a plurality of angle data collected for each crystal unit of a predetermined size that forms the solid material, The steps include generating the shape of the solid material as a first image containing multiple pixels, The steps include: using the EBSD data, performing clustering on the plurality of pixels such that the first image includes a plurality of clusters each indicating a crystal orientation; A step of generating a second image by processing the first image so that the first image is displayed in a different color for each cluster, A method for analyzing a crystal structure, comprising the step of rendering the second image onto a display area.

16. The first image described above includes a first pixel and a second pixel, The plurality of angle data include a first angle data corresponding to the first pixel and a second angle data corresponding to the second pixel. The step of performing the aforementioned clustering is, The crystal structure analysis method according to claim 15, further comprising the step of grouping the first pixel and the second pixel into the same first pixel group if the difference between the first angle data and the second angle data is less than or equal to a predetermined first threshold.

17. The step of performing the aforementioned clustering is, The crystal structure analysis method according to claim 16, further comprising the step of determining the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is equal to or greater than a second threshold, and not determining the first pixel group as a valid cluster if the number of pixels grouped in the first pixel group is less than the second threshold.

18. The first pixel group includes the third and fourth pixels, The step of performing the aforementioned clustering is, The crystal structure analysis method according to claim 17, further comprising the step of grouping the third pixel and the fourth pixel into the same second pixel group if the distance value between the position of the third pixel and the position of the fourth pixel is less than or equal to a predetermined third threshold.

19. The step of performing the aforementioned clustering is, The crystal structure analysis method according to claim 18, further comprising the step of determining the second pixel group as a valid cluster if the number of pixels grouped in the second pixel group is equal to or greater than a fourth threshold, and not determining the second pixel group as a valid cluster if the number of pixels grouped in the second pixel group is less than a fourth threshold.

20. A computer including a processor that executes programs or instructions stored in memory or storage devices, The step of obtaining EBSD data for solid materials, The steps include generating the shape of the solid material as a first image containing multiple pixels, The steps include: using the EBSD data, performing clustering on the plurality of pixels such that the first image includes a plurality of clusters each indicating a crystal orientation; A step of generating a second image by processing the first image so that the first image is displayed in a different color for each cluster, A computer-readable medium containing a program for performing the step of rendering the second image onto a display area.

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