JP6988707B2 - Material structure analysis method and analysis system - Google Patents

Description

The present invention relates to a technique for analyzing the structural characteristics of a material from a cross-sectional image of the material.

There are many structural features that affect the bulk properties of functional and structural materials, and individual features have a combined effect. Magnetic materials, dielectric materials, etc. are assumed as functional materials, and steel, ceramics, etc. are assumed as structural materials. Taking a sintered magnet as an example, the sintered magnet is formed of magnetic crystal particles called a main phase and a grain boundary phase sandwiched between the main phase and the main phase. The grain boundary phase is divided into a two-particle grain boundary phase sandwiched between two main phase crystal particles and a triple point grain boundary phase surrounded by three or more main phase crystal particles.

The magnetization reversal of the sintered magnet depends not only on the particle size and shape of the main phase but also on the width and magnetization of the grain boundary phase. Conventionally, features such as the average particle size of the main phase and the average width of the grain boundary phase have been measured from the cross-sectional image of the sample, and the correlation with the bulk characteristics has been analyzed. However, with the development of machine learning, it has become possible to measure more complicated tissue features from images and analyze their correlation with bulk properties. Therefore, as a result of investigating a technique for measuring the structural features of the main phase and the grain boundary phase together from the image and a technique for measuring features other than the average value and the ratio, the following documents were extracted.

Patent Document 1 describes an R-TB-based permanent magnet containing an R-TB-based compound as a main phase crystal particle, in which R is a rare earth element and T is Fe or Fe and Co. The element, B, is boron, contains a two-particle boundary between two adjacent main phase crystal particles, and the average particle size of the main phase crystal particles is 0.9 μm or more and 2.8 μm or less, and the two particles. It is described that good coercive force and magnetizing characteristics can be obtained by setting the grain boundary thickness to 5 nm or more and 200 nm or less.

Patent Document 2 describes a _{sintered magnet including a main phase of R 2} T ₁₄ B crystal particles and _{a grain boundary phase between the R 2} T ₁₄ B crystal particles, and the main phase in any cross section thereof. When the cross-sectional area distribution of the crystal particles is evaluated with a histogram, the crystal particles having a large particle size so that the cross-sectional area distribution has at least one peak on each side of the average value of the cross-sectional area. It is stated that it controls crystal particles with a small particle size.

Japanese Unexamined Patent Publication No. 2017-183710 JP-A-2015-388950

As a method for increasing the coercive force of the RT-B-based permanent magnet, a method of miniaturizing the main phase crystal particles of the RT-B-based permanent magnet is well known. However, when the main phase crystal particles are miniaturized, there is a problem that the magnetizing characteristics deteriorate, and it is necessary to reduce the area ratio of the crystal particles having a particle size of 1.8 μm or less to 5% or less. In Patent Document 1, it is effective to thicken the two-particle grain boundaries existing between the main phase crystal particles in order to obtain good magnetizing characteristics even when the main phase crystal particles having a particle size of 1.8 μm or less are present. It is stated that. It is described that when the average particle size of the main phase crystal particles is 0.9 μm or more and 2.8 μm or less and the grain boundary thickness of the two particles is 5 nm or more and 200 nm or less, the coercive force and the magnetizing characteristics are good. .. However, it is the two-particle grain boundary phase adjacent to the main phase crystal grains having a particle size of 1.8 μm or less that needs to be thickened. The thickness of the two-particle grain boundaries required for the main-phase crystal grains with a particle size of 1.8 μm or less is not described, and the two-particle grain boundary phase adjacent to the main-phase crystal grains with a particle size of 1.8 μm or less is not described. The film thickness measuring method is also not described.

In Patent Document 2, when the structure is such that the small crystal particles surround the large crystal particles, a magnetic splitting effect is given to the crystal particles having a large particle size, and the crystal particles having a small particle size are treated. It is stated that by reducing the surface area, the probability of occurrence of nuclei that form reverse magnetic regions can be reduced, and suppression of the high temperature demagnetization rate is achieved. With the above structure, when the cross-sectional area distribution of the main phase crystal particles is evaluated with a histogram in any cross section, the cross-sectional area distribution is at least one on each side of the average cross-sectional area. It is stated that the distribution has two peaks. However, even in a structure in which an aggregate of large crystal particles and an aggregate of small crystal particles are formed, the distribution has at least one peak on each side of the average cross-sectional area. In the histogram of the cross-sectional area distribution, it is not possible to quantify what percentage of the large crystal particles are surrounded by the small crystal particles. Further, FIG. 2A of Patent Document 2 does not show a structure in which small particles surround the entire large crystal particles. It is also not shown what percentage of the large crystal particles should be surrounded by small crystal particles.

Therefore, if more complicated structural features including the adjacency of the main phase, grain boundary phase, and triple point grain boundary phase can be quantified from the cross-sectional image of the sample, effective findings can be obtained by material analysis. Conceivable.

A preferred aspect of the present invention is a method using an information processing device including a processing device, a storage device, an input device, and an output device. An image input step is performed in which an image of a sample cross section including a main phase, which is a crystal particle, and a grain boundary phase sandwiched between the main phase and the main phase is used as an input image by an information processing apparatus. Further, from the input image, the main phase image showing the main phase in the region, the grain boundary phase image showing the grain boundary phase with a line, and the triple point grain boundary phase which is a part of the grain boundary phase are shown in the region. Perform an image processing step to generate a triple point boundary phase image. In addition, a main phase analysis step is performed in which each main phase of the main phase image is labeled, the organizational characteristics of each main phase are analyzed, and a main phase database in a format associated with the main phase label is generated. In addition, a grain boundary phase analysis step of attaching a label to each grain boundary phase of the grain boundary phase image, analyzing the structural characteristics of each grain boundary phase, and generating a grain boundary phase database in a format associated with the grain boundary phase label. I do. In addition, a label is attached to each triple point grain boundary in the triple point grain boundary image, the structural characteristics of each triple point grain boundary phase are analyzed, and the triple point grain boundary in a format associated with the label of the triple point grain boundary phase. Perform a triple point grain boundary phase analysis step to generate a phase database. In addition, an adjacency analysis step for generating an adjacency database showing the positional relationship between the main phase, the grain boundary phase, and the triple point grain boundary phase is performed. In addition, a database construction process is performed in which the main phase database, the grain boundary phase database, the triple point grain boundary phase database, and the adjacency relationship database are constructed as mutually referenceable databases.

Another preferred aspect of the present invention is to use an information processing device including a processing device, a storage device, an input device, and an output device, the main phase as crystal particles, and the grain boundaries sandwiched between the main phase and the main phase. This is a material structure analysis system that analyzes samples containing phases. The storage device stores the following databases. That is, for the sample, a main phase database in the form of associating the label attached to each main phase with the tissue characteristics obtained by analyzing each main phase, and each grain boundary phase were labeled and each grain boundary phase was analyzed. An adjacency relationship in which a grain boundary phase database in the form of associating structural features, a label attached to the main phase, and a label attached to the grain boundary phase are associated based on the positional relationship between the main phase and the grain boundary phase. With a database. Further, the input device is configured to accept the first classification condition and the second classification condition. Then, the processing device extracts the phase of the first label whose organizational characteristics satisfy the first classification condition based on at least one of the main phase database and the grain boundary phase database, and the first is based on the adjacency relationship database. The phase of the second label corresponding to the phase of the label is extracted, and the tissue characteristics satisfy the second classification condition from the phase of the second label based on at least one of the main phase database and the grain boundary phase database. Extract the phase of the third label. Here, the phase is either a main phase or a grain boundary phase.

From the cross-sectional image of the sample, it becomes possible to quantify more complicated structural features including the adjacency of the main phase, grain boundary phase, and triple point grain boundary phase.

The conceptual diagram which shows the whole picture of the correlation analysis between the tissue characteristic and the bulk characteristic in an Example. A conceptual diagram showing the overall picture of the correlation analysis between conventional tissue characteristics and bulk characteristics. A flowchart showing the entire process of structural feature analysis of a material. The block diagram which shows the whole structure of the information processing system used in an Example. Scanning electron microscope image of a sintered magnet. A binary image in which a scanning electron microscope image is separated into a main phase and a grain boundary phase. A thin line image in which the grain boundary phase part of a binary image is thinned. A grain boundary phase image in which fine line complementation is performed on a thin line image. A principal phase image that combines a binary image and a grain boundary phase image. Triple point grain boundary image created from binary image and thin line image. Explanatory drawing which shows the method of generating a triple point grain boundary phase image from a binary image and a thin line image. Table diagram of output example of the prime minister database. Table diagram of output example of triple point grain boundary phase database. A table diagram of an output example of a grain boundary phase database in which the structural characteristics of the grain boundary phase are described for each point of the thin line. A table diagram of an output example of a grain boundary phase database in which the structural characteristics of the grain boundary phase are described for each fine line. Table diagram of output example of adjacency database. Flow chart of adjacency analysis process. Explanatory diagram of adjacency analysis method. An image diagram of a display example of the adjacency relationship between the grain boundary phase and the main phase. An image diagram of a display example of the adjacent relationship between the grain boundary phase and the triple point grain boundary phase. A flowchart of a statistical analysis process that analyzes measurement items under classification conditions including adjacency. A table diagram of an example of a combination of classification conditions including adjacencies and measurement items. The table diagram of the output example of the analysis result of the classification condition including the adjacency relation and the measurement item in a certain sample. The table diagram of the output example of the analysis result of the classification condition and the measurement item including the adjacency relationship in a plurality of samples. Graph diagram of correlation analysis example between classification conditions including adjacency, analysis results of measurement items, and bulk characteristics. Explanatory drawing which shows the geometric arrangement of an incident electron beam and a sample surface in SEM-EBSD image taking. Explanatory drawing which shows the geometric arrangement of an incident electron beam and a sample surface in a scanning electron microscope image taking. A flowchart showing a field of view matching process between different types of images. A flowchart showing a process of generating a main phase image, a grain boundary phase image, and a triple point grain boundary phase image from a composite image of different types of images. Table diagram of output example of the main phase database including tissue features obtained from heterogeneous images.

Hereinafter, embodiments will be described in detail with reference to the drawings. However, the present invention is not limited to the description of the embodiments shown below. It is easily understood by those skilled in the art that a specific configuration thereof can be changed without departing from the idea or purpose of the present invention.

Further, in the configuration of the invention described below, the same reference numerals may be commonly used between different drawings for the same parts or parts having similar functions, and duplicate explanations may be omitted. Notations such as "first", "second", and "third" in the present specification and the like are attached to identify components, and do not necessarily limit the number, order, or contents thereof. is not it. In addition, the numbers for identifying the components are used for each context, and the numbers used in one context do not always indicate the same composition in the other contexts. Further, it does not prevent the component identified by a certain number from functioning as the component identified by another number. The position, size, shape, range, etc. of each configuration shown in the drawings and the like may not represent the actual position, size, shape, range, etc. in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the position, size, shape, range and the like disclosed in the drawings and the like.

In this embodiment, in order to analyze the correlation between the structural characteristics of the material and the bulk characteristics, a method of creating a database of structural characteristics from the cross-sectional image of the sample and analyzing the measurement items under various classification conditions from the database is performed. offer.

1A and 1B show conceptual diagrams of correlation analysis between tissue characteristics and bulk characteristics. FIG. 1B is a comparative example and shows the concept of a conventional analysis method. As shown in FIG. 1B, the measurement items that affect the bulk characteristics were analyzed from the image, and the correlation between the analysis results and the bulk characteristics was verified. For example, Patent Document 1 measures the average value of the main phase particle size and the average value of the grain boundary phase width, and Patent Document 2 measures the main phase area histogram.

The outline of the embodiment described below will be described. In the embodiment, an information processing device including a processing device, a storage device, an input device, and an output device is used. Then, an image input step of inputting an image of a sample cross section including a main phase which is a crystal particle and a grain boundary phase sandwiched between the main phase and the main phase is executed. In addition, the main phase image showing the main phase with particles from the input image, the grain boundary phase image showing the grain boundary phase with lines, and the triple point grain boundary phase which is a part of the grain boundary phase are shown with particles. Perform an image processing step to generate a triple-point grain boundary phase image. In addition, each principal phase of the principal phase image is labeled, the organizational characteristics of each principal phase are analyzed, and a principal phase analysis step of generating a principal phase database in a format associated with the label of the principal phase is executed. In addition, a grain boundary phase analysis step of attaching a label to each grain boundary phase of the grain boundary phase image, analyzing the structural characteristics of each grain boundary phase, and generating a grain boundary phase database in a format associated with the grain boundary phase label. To execute. In addition, a label is attached to each triple point grain boundary in the triple point grain boundary image, the structural characteristics of each triple point grain boundary phase are analyzed, and the triple point grain boundary in a format associated with the label of the triple point grain boundary phase. Perform the triple point grain boundary phase analysis step to generate the phase database. In addition, the adjacency analysis step of generating the adjacency database of the main phase, the grain boundary phase, and the triple point grain boundary phase is executed. In addition, the classification conditions and measurement items are input, and a statistical analysis step is executed in which the measurement items are analyzed for each classification condition from the main phase database, grain boundary phase database, triple point grain boundary phase database, and adjacency relationship database. In addition, the statistical analysis result output process of outputting the obtained statistical analysis result in a format that can be collated with the classification conditions is executed.

In addition to the main phase database, grain boundary phase database, and triple point grain boundary phase database, by using the adjacency database, the adjacent main phase, grain boundary phase, and triple point grain boundary phase can be separated into organizational characteristics including the adjacency relationship. It will be possible to quantify with. For example, the main phase having a particle size equal to or less than the threshold value is extracted as the main phase of the first label, the grain boundary phase adjacent to the main phase of the first label is extracted as the grain boundary phase of the second label, and the second label is extracted. It is possible to output the average value of the width distribution of the grain boundary phase of the label. Further, the main phase having an area equal to or larger than the first threshold value is extracted as the main phase of the first label, and the grain boundary phase adjacent to the main phase of the first label is extracted as the grain boundary phase of the second label. The main phase adjacent to the grain boundary phase of the second label is extracted as the main phase of the second label. The main phase of the second label is the main phase surrounding the main phase of the first label, and it is possible to output a ratio whose area is equal to or less than the second threshold value.

FIG. 1A is a conceptual diagram of an embodiment described below. In this embodiment, the main phase in the image is shown by particles, each main phase is labeled, the measurement items of each main phase are analyzed, and the main phase database (DB) in the form associated with the label of each main phase. Create 101. The triple point grain boundary phase, which is also a part of the grain boundary phase, is indicated by particles, each triple point grain boundary phase is labeled, the measurement items of each triple point grain boundary phase are analyzed, and the label of each grain boundary phase is analyzed. Create a triple point grain boundary phase database 102 in the format associated with. The grain boundary phase is indicated by a thin line, each grain boundary phase is labeled, the measurement items of each grain boundary phase are analyzed, and the grain boundary phase database 103 in a format associated with the label of each grain boundary phase is created. In addition to these databases, an adjacency database 104 of a main phase, a grain boundary phase, and a triple point grain boundary phase is created. The adjacency data is the label of each grain boundary phase, the labels of the two main phases sandwiching the grain boundary phase, the coordinates of both ends of the line segment indicating the grain boundary phase, and the triple point grain boundaries located at those coordinates. A database containing phase labels. A database 100 including these can be generated, stored in a storage device, and used.

By creating the adjacency database 104, the characteristics of the main phase and the grain boundary phase in a close relationship can be associated and extracted. For example, the main phase having a particle size equal to or less than the threshold value is extracted as the main phase of the first label, the grain boundary phase adjacent to the main phase of the first label is extracted as the grain boundary phase of the second label, and the second label is extracted. It becomes possible to measure the width of the grain boundary phase of the label and calculate the average value. Further, the main phase having an area equal to or larger than the first threshold value is extracted as the main phase of the first label, and the grain boundary phase adjacent to the main phase of the first label is extracted as the grain boundary phase of the second label. The main phase adjacent to the grain boundary phase of the second label is extracted as the main phase of the second label. The main phase of the second label is the main phase surrounding the main phase of the first label, and it is possible to output a ratio whose area is equal to or less than the second threshold value.

In the first embodiment, details of how to analyze the tissue characteristics of a sintered magnet of a scanning electron microscope (S canning E lectron M icroscope) image.

FIG. 2A shows a flowchart of the entire analysis process. Image input step (S1) for inputting SEM image used for analysis, image processing step (S2) for generating main phase image, grain boundary phase image, triple point grain boundary phase image from SEM image, SEM image, main phase image, Main phase database, grain boundary phase database, which analyzes the structural features of the main phase, grain boundary phase, and triple point grain boundary phase from the grain boundary phase image and triple point grain boundary image and saves them in a format associated with the label. It consists of a three-priority grain boundary phase database, an individual analysis step (S3) for generating an adjacency database, and a statistical analysis step (S4) for inputting classification conditions and measurement items and analyzing the measurement items under each classification condition from the database. ..

<S1. Image input process>
First, in order to take an SEM image, the cross section of the sample to be taken is exposed. Since many functional materials have anisotropy in the sample structure, it is better to process the sample so that the plane orientation of the sample can be confirmed. For example, in a magnetic material, the axis of easy magnetization is called the c-axis. Since the ab plane orthogonal to the c-axis is photographed this time, the sample is cut into a rectangular parallelepiped whose upper surface is the ab plane. Further, if the cross section to be photographed has irregularities, an image intensity distribution depending on the irregularities is observed in the SEM image, so it is better to start imaging after flattening the surface to be imaged by polishing. A focused ion beam processing device may be used for flat processing.

Insert the processed sample into the SEM, set the acceleration voltage and lens mode, and adjust the detector amplifier, electro-optical system misalignment, astigmatism, and focus according to the device adjustment procedure manual. If the SEM has an automatic adjustment function for a detector or an electro-optical system, the automatic adjustment function may be used. When automatically photographing multiple fields of view, it is better to align the sample stage. After adjusting the SEM, the SEM image required for analysis is photographed and stored in a recording medium.

The captured SEM image is input to the information processing device (S11).

FIG. 2B shows an overall block diagram of the information processing apparatus of this embodiment. As the information processing device 2000, for example, a server including a processing device 2001, a storage device 2002, an input device 2003, and an output device 2004 is used. In the present embodiment, functions such as calculation and control are realized by executing the program stored in the storage device 2002 by the processing device 2001, thereby performing the defined processing in cooperation with other hardware. A program executed by a computer, its function, or a means for realizing the function may be referred to as a "function", a "means", a "part", a "unit", a "module", or the like. For convenience, these functions are shown in FIG. 2B as functional blocks within the processing apparatus 2001.

The above configuration may be configured by a single computer, or any part of the input device 2003, the output device 2004, the processing device 2001, and the storage device 2002 may be configured by another computer connected by a network. You may. The hardware configuration of the server is well known and is not shown here. The database 100 described above is stored in the storage device 2002 included in the information processing device 2000, or in a storage device accessible from the information processing device via a network or the like.

<S2. Image processing process>
The image processing step and thereafter are executed by the information processing apparatus 2000. The image processing step shall be executed by the image processing unit 2011.

FIG. 3A is a grayscale SEM image 301 which is an input image input from the input device 2003. FIG. 3B is a binary image 302 separated into a main phase 10 and a grain boundary phase 20 (S21) by applying a predetermined threshold value to the contrast of the SEM image, and FIG. 3C is a thin line of the grain boundary phase portion of the binary image (S22). ) Is the thin line image 303.

Since there are grain boundary phases having a narrow width and grain boundary phases having a small difference in image intensity from the main phase, there are also grain boundary phases that cannot be extracted by general image processing such as binarization processing based on a threshold value. In that case, the thin line complement processing (S23) for complementing between the line segments is applied. The thin line complement processing may be automated by using image processing. The grain boundary phase that could not be extracted by image processing may be manually corrected.

FIG. 3D is an image in which fine lines are complemented in FIG. 3C, and this is used as a grain boundary phase image 304 (S25). In the grain boundary phase image 304, the thin line is regarded as the center line 23 of the grain boundary phase, and the grain boundary phase is expressed by a limited point (for example, coordinates).

The main phase image 305 shown in FIG. 3E is a composite image of the grain boundary phase image 304 of FIG. 3D and the binary image 302 of FIG. 3B, and is logical when the main phase 10 is 1 and the grain boundary phase 20 is 0. It is calculated by the product operation (S24). The logical product image is calculated after applying image processing such as expanding the width of the thin line, smoothing the unevenness of the thin line, and smoothing the unevenness of the boundary between the main phase and the grain boundary phase in the binary image. You can do it.

Further, by using the following method, the grain boundary phase can be separated into a two-particle grain boundary phase 21 sandwiched between two main phases and a triple-point grain boundary phase 22 surrounded by three or more main phases. .. First, each point of the fine line of the grain boundary phase image 304 is classified into the following three types, that is, an end point where one thin line exists in eight adjacent pixels, a passing point where two exist, and a branch point where three or more exist. do. For each branch point, a circle with a radius R = 1 pixel centered on the branch point is drawn on the binary image. Initially, all pixels on the circumference become grain boundary phases. The radius R is increased, and the increase of the radius R is stopped when the ratio of the grain boundary phase (that is, the point having the same value as the branch point) on the circumference becomes a threshold value, for example, 50% or less.

FIG. 4 shows a diagram in which a circle centered on a branch point is drawn on a binary image. For the sake of explanation, the main phase 10 is displayed in black, the grain boundary phase 20 is displayed in white, and the center line 23 of the grain boundary phase is displayed. The grain boundary phase on the inner side of the circle is referred to as the triple point grain boundary phase 22, and the grain boundary phase on the outer side is referred to as the two-particle grain boundary phase 21 to separate the two. This method is described in detail in Japanese Patent Application No. 2017-132117 by the applicant (unpublished at the time of filing the present application).

FIG. 3F shows a triple point grain boundary phase image 306 created (S26) from the binary image 302 of FIG. 3B and the grain boundary phase image 304 of FIG. 3D according to the above-mentioned method.

The method of creating the main phase image 305, the grain boundary phase image 304, and the triple point grain boundary phase image 306 from the SEM image 301 is not limited to the above method. For example, image processing by machine learning may be used.

<S3. Individual analysis process>
Here, from the main phase image 305, the grain boundary phase image 304, the triple point grain boundary phase image 306, and the SEM image 301 generated in the image processing step (S2), the main phase database 101, the triple point grain boundary phase database 102, The process of generating the grain boundary phase database 103 and the adjacency relationship database 104 will be described.

FIG. 5A shows an example of the main phase database 101 generated by the main phase analysis unit 2012. The main phase database 101 regards the main phase on the main phase image 305 as particles, and assigns a particle number 1011 to each particle (S31). The particle number of the main phase may be saved as a labeled main phase image. In this case, the intensity of the pixels other than the particles is set to zero, and the particle number is recorded in the pixels of the particles. The shape characteristics of each particle are measured and output as a table corresponding to the particle number (S34). The shape characteristics of the particles 1012 are the area, the x-coordinate of the center of gravity, the y-coordinate of the center of gravity, the perimeter, the long axis of the ellipse that most closely resembles, the minor axis of the ellipse that most closely resembles, and the long axis and the X axis of the image. Angle, roundness (= 4π x area / circumference squared), aspect ratio (= major axis / minor axis), consistency density (= area / area of convex package), etc.

FIG. 5B shows an example of the triple point grain boundary phase database 102 generated by the triple point grain boundary phase analysis unit 2014. The triple point grain boundary phase database 102 also regards the triple point grain boundary phase 22 on the triple point grain boundary phase image 306 as particles, and assigns a particle number 1021 to each particle (S33). The particle number of the triple-point grain boundary phase may also be saved as a labeled triple-point grain boundary phase image. The shape feature 1022 of each particle is measured and output as a table corresponding to the particle number (S36). In the triple-point grain boundary phase database, in addition to the shape feature 1022, image intensity information 1023 such as the average value and the mode value of the intensity of the SEM image 301 may be measured for each particle and added to the database.

The grain boundary phase database 103 divides the fine lines on the grain boundary phase image 304 into line segments connecting the branch point-branch point or the branch point-end point, assigns a line number to each line segment (S32), and is on each line segment. The texture characteristics of the grain boundary phase in are summarized in a table corresponding to the line number and output as a database (S35). The following two types are assumed as the format of the grain boundary phase database.

FIG. 6A shows the grain boundary phase database 103A of the first type generated by the grain boundary phase analysis unit 2013. A line number 1031 is attached to each line segment, a point number 1032 is attached to each point on the line segment, and the coordinates x1033 and the coordinates y1034 are associated with each other. The organizational characteristics of the grain boundary phase on each point are summarized in a table corresponding to the line number and the point number, and output as the grain boundary phase database 103A.

The items of the tissue characteristics are the type 1035 indicating whether the grain boundary phase specified by the point is the two-particle grain boundary phase or the triple-point grain boundary phase, and it is calculated by linearly approximating the thin lines of several pixels before and after the point. The angle 1036 formed by the x-axis of the direction, the width of the grain boundary phase 1037 calculated by extracting the line profile in the direction orthogonal to the calculated direction from the SEM image and function-fitting the line profile, and the inside of the width. An average image intensity of 1038, a dispersion of 1039, and the like can be used.

FIG. 6B shows a second type of grain boundary phase database 103B generated by the grain boundary phase analysis unit 2013. The grain boundary phase database 103B is a format described by a line number 1041 indicating a line segment and a tissue characteristic of the line segment. Items of organizational characteristics include x-coordinate 1042 at the midpoint of the line segment, y-coordinate 1043 at the midpoint, length 1044, area 1045, width average value 1046, angle average value 1047, curvature average value 1048, and strength. An average value of 1049, a strength dispersion of 1050, and the like can be used.

FIG. 7 shows an example of the adjacency database 104 generated in the adjacency database generation step (S37) by the adjacency analysis unit 2015. The line number 1071 of the grain boundary phase, the particle numbers 1072, 1073 of the two main phases (adjacent particles A and adjacent particles B) adjacent to the grain boundary phase, and the xy coordinates of both ends of the fine line (end point a and end point b) and their portions. The grain numbers 1074 and 1075 of the triple point grain boundary phase in the coordinates are summarized in one line.

It is also possible to combine the adjacency database 104 into two tables instead of combining them into one table. For example, in the first table, the grain boundary phase line number 1071, the particle numbers 1072, 1073 of the two main phases (adjacent particle A and adjacent particle B) adjacent to the grain boundary phase, and both ends of the fine line (end point a). The xy coordinates of the end point b) and their coordinates are recorded, and the xy coordinates of the branch point and the particle number of the triple point grain boundary phase at the branch point are recorded in the second table. When it is desired to refer to the particle number of the triple point grain boundary phase at the end point a and the end point b, a branch point having the same xy coordinates is extracted, and the label number of the triple point particle at the branch point is output.

FIG. 8 shows the details of the process of generating the adjacency database 104 by the adjacency analysis unit 2015. First, the grain boundary phase image 304, the main phase database 101, and the grain boundary phase database 103 are input (S371).

The grain boundary phase for which the adjacency is to be analyzed is selected (S372), the distance between the midpoint of the line segment and the center of gravity of the surrounding main phase is calculated, and about 10 main phases having a short distance are extracted (S373).

A line profile connecting the midpoint and the center of gravity is obtained from the grain boundary phase image, and a line profile that does not cross other grain boundary phases is extracted from the line profile (S374).

FIG. 9 shows a grain boundary phase image 304 in which the line number of the grain boundary phase is added near the midpoint of the line segment and the particle number of the main phase is added near the center of gravity of the main phase. When the midpoint of the grain boundary phase of line number 80 and the center of gravity of the 10 extracted main phases are connected by a dotted line, two main phases (particle number 27 and particle number) adjacent to the grain boundary phase of line number 80 are connected. It can be seen that the dotted lines cross other grain boundary phases except for 28). If they are adjacent to each other, the values of the line profile with the main phase are considered to be all zero.

However, since the width of the thin line is 1, the value may remain zero even if the line profile crosses the thin line, so the following measures were taken. After expanding the thin line by one pixel, 5 pixels near the midpoint are deleted from the line profile connecting the midpoint and the center of gravity, and a line profile in which the values of the remaining line profiles are all zero is extracted. When two line profiles are extracted, it is determined to be the main phase on both sides of the main phase (S376).

In the case of 3 or more, the direction vector a of the thin line and the vector b from the midpoint to the center of gravity are obtained from the pixel array of 5 pixels before and after the midpoint, and the angle between the vector a and the vector b is calculated to make the angle 90 degrees. Two close main phases are extracted and determined to be the adjacent main phases (S377). After the adjacency analysis is completed for all the grain boundary phases, the adjacency analysis result may be displayed on the image shown in FIG. 10 to confirm that the analysis is correct.

FIG. 10A is an example showing the adjacent relationship between the main phase and the grain boundary phase. The main phase label and the grain boundary phase label are added to the main phase image 305, and the adjacent main phase and the grain boundary phase are connected by a line. I'm out. FIG. 10B is an example showing the adjacent relationship between the grain boundary phase and the triple point grain boundary phase, and the outline and the particle number of the particles showing the triple point grain boundary phase are shown on the grain boundary phase image 304 in the same region as in FIG. 10A. It has been added.

<S4. Statistical analysis process>
In this step, classification conditions and measurement items are input (S41), analysis is performed according to the classification conditions and measurement items using the database 100 (S42), and analysis results are output in a format associated with the classification conditions (S43).

FIG. 11 shows the details of the process of analyzing the database by the statistical analysis unit 2016 according to the classification conditions including the adjacency relationship and the measurement items. The classification conditions are divided into the first classification condition, the adjacency relationship, and the second classification condition. A step of extracting the phase of the first label that meets the first classification condition (S421), a step of extracting the phase of the second label that is adjacent to the phase of the first label (S422), and the second step. It is composed of a step of extracting a phase of a third label conforming to the second classification condition from the phase of the label (S423) and a step of analyzing the measurement items of the phase of the third label (S424). Depending on whether the main phase, grain boundary phase, or triple point grain boundary phase is selected for the first classification condition and the measurement item, the phase for which the adjacency relationship is obtained differs.

FIG. 12 shows a table summarizing the adjacency relationship by the combination of phases. When the adjacency relationship is one stage, the phase type of the second classification condition matches the phase type of the measurement item. Hereinafter, the details of the analysis process in each combination will be described.

First, when the grain boundary phase is classified by the first classification condition (S421-2) and the main phase is analyzed by the measurement item (S424-1), the adjacency relationship is the main phase (S422-1) adjacent to the grain boundary phase. ). The grain boundary phase of the first line number that meets the first classification condition is extracted from the grain boundary phase database 103. In the adjacency database 104, the particle number of the adjacent main phase A and the particle number of the adjacent main phase B described in the row of the grain boundary phase of the first line number are the first of the main phases adjacent to the grain boundary phase of the line number. It becomes the particle number of 2. After the main phase of the second particle number is classified by the second classification condition (S423-1) in the main phase database 101, the measurement items are analyzed (S424). The second classification condition can be set to all the main phases having the second particle number.

Next, when the grain boundary phase is classified according to the first classification condition (S421-2) and the triple point grain boundary phase is analyzed by the measurement item (S424-3), the adjacency relationship is the triple point adjacent to the grain boundary phase. It becomes a grain boundary phase (S422-2). The grain boundary phase of the first line number matching the first classification condition is extracted from the grain boundary phase database 103, and the row of the grain boundary phase of the first line number is extracted using the adjacency relationship database 104. The particle number of the triple point grain boundary phase at the end point a of the row and the particle number of the triple point grain boundary phase at the end point b are the second particles of the triple point grain boundary phase adjacent to the grain boundary phase of the first line number. Become a number. In the triple-point grain boundary phase database 102, the triple-point grain boundary phase of the second particle number is classified by the second classification condition (S423-3), and then the measurement items are analyzed (S424).

Next, when the main phase is classified according to the first classification condition (S421-1) and the grain boundary phase is analyzed by the measurement item (S424-2), the adjacency relationship is the grain boundary phase adjacent to the main phase (S422-). 3). The main phase of the first particle number that meets the first classification condition is extracted from the main phase database 101. Using the adjacency relationship database 104, a line containing the main phase of the first particle number is extracted in the adjacent main phase A or the adjacent main phase B, and the line number of the grain boundary phase of the row is output. There are three or more grain boundary phases adjacent to one main phase. In addition, the output line numbers are duplicated. The line number excluding the overlap is output as the second line number of the grain boundary phase adjacent to the main phase of the first particle number. In the grain boundary phase database 103, the grain boundary phase of the second line number is classified under the second classification condition (S423-2), and the measurement items are analyzed (S424).

Next, when the main phase is classified according to the first classification condition (S421-1) and the main phase is analyzed in the measurement item (S424-1), the adjacency relationship is the adjacent main phase (S422) with the grain boundary phase in between. -4). The main phase of the first particle number that meets the first classification condition is extracted from the main phase database 101. The adjacency database 104 is used to extract rows containing the primary phase of the first particle number in adjacent principal phase A or adjacent principal phase B. Output the pair of the line number of the grain boundary phase in that row and the particle number of the other adjacent main phase (which is not the first particle number). The pair obtained by removing the duplication from the output pair is the second line number of the grain boundary phase adjacent to the main phase of the first particle number and the main phase adjacent to each other with the grain boundary phase of the second line number in between. Let it be the second particle number. The second classification condition and measurement item may be applied to the grain boundary phase of the second line number (S423-2) or the main phase of the second particle number (S423-1). For example, the area range of the main phase of the second particle number is set as the second classification condition using the main phase database 101, and the length ratio of the grain boundary phase of the second line number is set using the grain boundary phase database 103. It may be a measurement item.

Next, when the main phase is classified according to the first classification condition (S421-1) and the triple point grain boundary phase is analyzed by the measurement item (S424-3), the adjacency relationship is the triple point grain boundary adjacent to the main phase. It becomes a phase (S422-5). The main phase of the first particle number that meets the first classification condition is extracted from the main phase database 101, and the main phase of the first particle number is added to the adjacent main phase A or the adjacent main phase B using the adjacency relationship database 104. The row containing the above is extracted, and the particle number of the triple point grain boundary phase of the end point a and the end point b of the row is output. The particle number obtained by removing the duplication from the output particle number is used as the second particle number of the triple point grain boundary phase adjacent to the main phase of the first particle number. In the triple-point grain boundary phase database 102, the triple-point grain boundary phase of the second particle number is classified by the second classification condition (S423-3), and then the measurement items are analyzed (S424-3).

Next, when the triple point grain boundary phase is classified by the first classification condition (S421-3) and the grain boundary phase is analyzed by the measurement item (S424-2), the adjacency relationship is adjacent to the triple point grain boundary phase. It becomes a grain boundary phase (S422-6). The triple point grain boundary phase of the first particle number corresponding to the first classification condition is extracted from the triple-point grain boundary phase database 102. A row containing the triple point grain boundary phase of the first particle number is extracted at the endpoint a or endpoint b of the adjacency database 104, and the line number of the grain boundary phase of that row is output. The line number obtained by removing the duplication from the output line number is used as the second line number of the grain boundary phase adjacent to the triple point grain boundary phase of the first particle number. Using the grain boundary phase database 103, the grain boundary phase of the second line number is classified according to the second classification condition (S423-2), and the measurement items are analyzed (S424).

Next, when the triple point grain boundary phase is classified according to the first classification condition (S421-3) and the main phase is analyzed by the measurement item (S424-1), the adjacent relationship is the main phase adjacent to the triple point grain boundary phase. The case of (S422-7) will be described. The triple point grain boundary phase of the first label that meets the first classification condition is extracted from the triple-point grain boundary phase database 102. Extracts a row containing the triple point grain boundary phase of the first particle number at the end point a or end point b of the adjacency relationship database 104, and outputs the particle number of the adjacent main phase A and the particle number of the adjacent main phase B in that row. Let me. The particle number obtained by removing the duplication from the output particle number is used as the second particle number of the main phase adjacent to the triple point grain boundary phase of the first particle number. The main phase of the second particle number is classified by the second classification condition (S423-1) by the main phase database 101, and the measurement items are analyzed (S424).

In FIG. 13A, the statistical analysis unit 2016 classifies the phases according to the first classification condition, the adjacency relationship, and the second classification condition, and the measurement results in the classified phases are output in a tabular format by the statistical analysis result output unit 2017. An example is shown. The first classification condition is the area range of the main phase, and the main phase corresponding to the first classification condition is extracted from the area data of the organizational feature 1012 of the main phase database 101. Next, referring to the adjacency database 104, the adjacency is the grain boundary phase of the second line number adjacent to the main phase of the first particle number and the second'adjacent to the grain boundary phase of the second line number. These are specified as the main phase of the particle number of. The second classification condition was the area range of the main phase of the second particle number, and the measurement items corresponding to these were the length ratio of the grain boundary phase of the second line number. By setting the classification condition including the adjacency relationship, it is possible to quantify the ratio in which the main phase having a large area A and the main phase having a small area B are adjacent to each other.

FIG. 13B is another output example by the statistical analysis result output unit 2017. Although FIG. 13A summarizes the analysis results of one image in one table, it is also possible to output the analysis results of a plurality of images in one table as shown in FIG. 13B. Here, the first classification condition is the area range of the main phase, and the adjacency relationship is the grain boundary phase of the second line number adjacent to the main phase of the first particle number and the grain boundary phase of the second line number. The main phase of the adjacent 2'particle number is used, the second classification condition is not set, and the measurement items are the average value of the width of the grain boundary phase of the second line number and the main phase of the second'particle number. It is the average value of the area of the phase.

FIG. 14 is a display example of the correlation analysis between the obtained tissue characteristics and the bulk characteristics by the statistical analysis result output unit 2017. It is shown that the correlation between the structure characteristics and the bulk characteristics shown in Patent Document 1, that is, the thicker two-particle grain boundary phase around the main phase crystal particles having a small particle size improves the magnetizing characteristics. It can be seen that the magnetizing characteristics are improved when the average width of the two-particle grain boundary phase around the main phase crystal particles having a small particle size A is increased, and the correlation between the two is strong. On the other hand, when the average width of the two-particle grain boundary phase around the main phase crystal particles having a large particle size A increases, the magnetizing characteristics improve, but when the width exceeds a certain level, the magnetizing characteristics do not improve. Further, it can be seen that the correlation between the two is weaker than when the particle size A is small. By analyzing how much difference occurs in the measurement results depending on the classification conditions including the adjacency relationship, it becomes possible to accurately evaluate the tissue characteristics that affect the bulk characteristics.

As described above, in this embodiment, the main phase database 101, the grain boundary phase database 103, the triple point grain boundary phase database 102, and the adjacency relationship database 104 are constructed as the mutually referable database 100. It is possible to analyze the multifaceted material structure considering the arrangement relationship of each phase. In the information processing apparatus 2000 of this embodiment, the construction of the database 100 and the analysis using the database 100 are performed by the same information processing apparatus, but the construction of the database and its use may be performed by a separate apparatus.

The tissue characterization of materials other than SEM image, backscattered electron diffraction (E lectron B ack S catter D ifraction) dated system SEM (SEM-EBSD), energy dispersive X-ray spectroscopy (E nergy- D ispersive X - An SEM (SEM-EDX) with a ray Spectroscope system, an optical microscope (light microscope), or the like is used. In Example 2, a method of analyzing tissue characteristics using an SEM in the same field of view and a heterogeneous image taken by a device different from the SEM, for example, SEM-EBSD will be described. The processing basically follows the flow shown in FIG. 2, but the peculiar part will be described below.

<S1. Image input process>
When different types of images are used, a step of aligning the fields of view between the images using geometric transformation is required. SEM-EBSD incidents an electron beam at a shallow angle with respect to the sample surface, for example, 10 to 20 degrees, takes an electron diffraction image of the electron beam reflected from the sample surface with a camera, and crystallizes the sample from the electron diffraction image. It is a device that analyzes the orientation (FIG. 15A). A crystal orientation map can be created by scanning the incident electron beam and analyzing the crystal orientation of each measurement point. It is also possible to classify the grain boundary phase into several crystal structures from the electron diffraction image of the grain boundary phase and create a crystal phase map.

Since the SEM is usually taken by injecting an electron beam into the sample surface at an angle of about 90 degrees (Fig. 15B), after taking the SEM image, the sample is tilted on the sample stage and then the SEM-EBSD image is taken. .. Since the SEM image and the SEM-EBSD image have different geometrical arrangements of the incident electron beam and the sample, it is necessary to geometrically transform one image and match it with the other field of view in order to obtain an image of the same field of view.

FIG. 16 shows a flowchart of geometric transformation correction. This time, the SEM image is input as a reference image and the SEM-EBSD image is input as a correction image (S51). A common structure, for example, a grain boundary phase having a characteristic shape is extracted from the SEM image and the SEM-EBSD image as a control point (S52). Control points may be extracted automatically or manually entered. Assuming that the coordinates of the control points on the reference image are (x ₁ , y ₁ ) and the coordinates of the control points on the corrected image are (x ₂ , y ₂ ), the two control points are related by the geometric transformation matrix H.

About 10 control points are extracted, and the geometric transformation matrix H is obtained by the least squares method or the like (S53). The SEM-EBSD image is geometrically transformed with the obtained geometric transformation matrix H (S54). After geometric transformation, a common field of view is cut out as needed (S55), and the field of view is used for analysis.

When the geometric transformation matrix can be estimated from known information such as shooting conditions, the corrected image may be roughly corrected by the geometric transformation matrix created from the known information, and the control points may be extracted by the coarsely corrected image. Since the positional relationship between the incident electron beam and the sample surface in the SEM image and the SEM-EBSD image is arranged as shown in FIG. 15, the SEM-EBSD image is compressed by sin θ in the x direction with respect to the SEM image. In this case, the following can be used as the geometric transformation matrix for coarse correction.

Images can be input to the information processing apparatus 2000 in the image input step described in the first embodiment except for the field of view alignment between the images.

<S2. Image processing process>
The main phase image 305, the grain boundary phase image 304, and the triple point grain boundary phase image 306 can be generated not only from the SEM image but also from the SEM-EBSD image.

FIG. 17 shows the flowchart. In the EBSD system manufactured by TSL, when the crystal orientation of the sample is analyzed from the electron diffraction image, a map of the reliability index (CI value) which is an index of the reliability of the analysis result is output. Since the CI value is a low value in the main phase in contact with the grain boundary phase, the width of the grain boundary phase is very narrow, so that the position of the grain boundary phase, which is not visualized in the SEM image, may be visualized in the CI image. A composite image of a binarized CI image (S212) and a binarized SEM image (S211) is generated (S213), and this is converted into a thin line image to reduce the number of thin line complements. be able to.

The main phase image and grains in the image processing step described in Example 1 except that a composite image of different types of images can be used to generate the main phase image 305, the grain boundary phase image 304, and the triple point grain boundary phase image 306. It is possible to generate a field phase image and a triple point grain field phase image.

<S3. Individual analysis process>
In addition to the tissue features extracted from the SEM image, the tissue features extracted from the SEM-EBSD image can be added to the main phase database 101, the triple point grain boundary phase database 102, and the grain boundary phase database 103.

FIG. 18 shows an example of the main phase database 101a in which the tissue characteristics obtained from SEM-EBSD are added to the main phase database 101 of the tissue features obtained from the main phase image 305. The coordinates of the region of each particle number are output using the labeled main phase image. Three angles (α, β, γ) indicating the direction of the c-axis in the output coordinates are output from the EBSD, the average value or mode in the region is calculated, and the tissue characteristics obtained from the EBSD in the database. Add as 1012a. When the analysis results are summarized for each main phase in the analysis software of the EBSD system, the correspondence between the particle number of the main phase in the analysis software of the EBSD system and the particle number of the main phase assigned in the generation of the main phase database. A table may be created and the EBSD measurement results may be added to the main phase database 101 based on the correspondence table.

The analysis results of other devices can be added not only to the main phase database 101 but also to the grain boundary phase database 103 and the triple point grain boundary phase database 102. For example, SEM-EBSD can analyze the crystal structure of the triple point grain boundary phase and add it to the database. It is also possible to analyze the composition of the grain boundary phase with SEM-EDX and add it to the grain boundary phase database 103.

<S4. Statistical analysis process>
In the statistical analysis step, classification conditions and measurement items are input, analysis is performed according to the classification conditions and measurement items using the database 100, and analysis results are output in a format associated with the classification conditions. This step is performed in substantially the same steps as in the first embodiment.

As described in the above examples, by the above embodiment, more complicated structural features including the adjacency of the main phase, the grain boundary phase, and the triple point grain boundary phase can be quantified from the cross-sectional image of the sample. become. By analyzing the obtained tissue features using machine learning or the like, it becomes possible to efficiently extract tissue features that are highly correlated with the bulk properties, and research and development of bulk property improvement will be accelerated. In addition, this analysis method can be automated, and it is also possible to analyze a large amount of data. It not only improves bulk characteristics by tissue control, but also contributes to countermeasures against product defects caused by tissue.

10 ... Main phase, 20 ... Grain boundary phase, 21 ... Two-particle grain boundary phase, 22 ... Triple point grain boundary phase, 23 ... Center line of grain boundary phase

Claims (15)

Using an information processing device equipped with a processing device, a storage device, an input device, and an output device,
An image input step in which an image of a sample cross section including a main phase, which is a crystal particle, and a grain boundary phase sandwiched between the main phase and the main phase is used as an input image.
From the input image, the main phase image showing the main phase in the region, the grain boundary phase image showing the grain boundary phase with lines, and the triple point grain boundary phase that is a part of the grain boundary phase are shown in the region. Image processing step to generate grain boundary phase image,
A main phase analysis step of attaching a label to each main phase of the main phase image, analyzing the organizational characteristics of each main phase, and generating a main phase database in a format associated with the main phase label.
A grain boundary phase analysis step of attaching a label to each grain boundary phase of the grain boundary phase image, analyzing the structural characteristics of each grain boundary phase, and generating a grain boundary phase database in a format associated with the grain boundary phase label.
Each triple point grain boundary phase in the triple point grain boundary phase image is labeled, the structural characteristics of each triple point grain boundary phase are analyzed, and the triple point grain boundary phase in the form associated with the label of the triple point grain boundary phase. Triple point grain boundary phase analysis process to generate a database,
Adjacency analysis process to generate an adjacency database showing the positional relationship between the main phase, grain boundary phase, and triple point grain boundary phase,
A method for analyzing a material structure, which comprises a database construction step of constructing the main phase database, the grain boundary phase database, the triple point grain boundary phase database, and the adjacency relationship database as a mutually referenceable database. The adjacency analysis step is
The label of each grain boundary phase, the label of the two main phases sandwiching the grain boundary phase, the coordinates of both ends of the line segment indicating the grain boundary phase, and the label of the triple point grain boundary phase located at the coordinates of both ends. , The process of identifying,
The method for analyzing a material structure according to claim 1, further comprising. The triple point grain boundary phase analysis step is
When the branch point of the line of the grain boundary phase of the grain boundary phase image is extracted and a circle centered on the branch point is assumed on the main phase image, the value is the same as the branch point of the point on the circumference. The radius determination process to find the radius where the point ratio is within the specified range,
A triple point grain boundary phase classification step in which a region other than the main phase in the main phase image is defined as a grain boundary phase and a region of the grain boundary phase inside the circle of the radius is classified as a triple point grain boundary phase.
The method for analyzing a material structure according to claim 1, further comprising. The grain boundary phase analysis step is
The branch point and the end point of the line of the grain boundary phase of the grain boundary phase image are extracted, and the line is divided into a branch point and a branch point or a line segment having an end point and a branch point as both end points, and each line segment has a line number. The labeling process, which indicates the grain boundary phase on each line segment by the line number of the line segment.
The method for analyzing a material structure according to claim 1, further comprising. The grain boundary phase analysis step is
The branch point and the end point of the line of the grain boundary phase of the grain boundary phase image are extracted, the line is divided into a branch point and a branch point, or a line segment having an end point and a branch point as both end points, and each line segment has a line number. , A point number is assigned to each point of the line segment, and a labeling step indicating the grain boundary phase on each point by the line number and the point number.
The method for analyzing a material structure according to claim 1, further comprising. Moreover,
Statistical analysis that inputs the classification conditions and measurement items and analyzes the measurement items for each classification condition based on the main phase database, the grain boundary phase database, the triple point grain boundary phase database, and the adjacency relationship database. Process,
The method for analyzing a material structure according to claim 1, further comprising a statistical analysis result output step of outputting the obtained statistical analysis result in a format that can be collated with the classification conditions. The statistical analysis step is
Based on at least one of the main phase database, the grain boundary phase database, and the triple point grain boundary phase database, the phase of the first label satisfying the first classification condition is extracted.
Based on the adjacency database, the phase of the second label corresponding to the phase of the first label is extracted.
A third label phase satisfying the second classification condition is extracted from the second label phase based on at least one of the main phase database, the grain boundary phase database, and the triple point grain boundary phase database. death,
The method for analyzing a material structure according to claim 6, wherein the phase is any one of a main phase, a grain boundary phase, and a triple point grain boundary phase. The statistical analysis step is
The method for analyzing a material structure according to claim 7, wherein an analysis relating to the measurement item is performed on the phase of the third label. A material for analyzing a sample including a main phase, which is a crystal particle, and a grain boundary phase sandwiched between the main phase and the main phase, using an information processing device equipped with a processing device, a storage device, an input device, and an output device. An organizational analysis system
The storage device relates to the sample.
A prime minister database in the form of associating a label attached to each prime minister with the organizational characteristics obtained by analyzing each prime minister.
A grain boundary phase database in which each grain boundary phase is labeled and associated with the texture characteristics obtained by analyzing each grain boundary phase,
An adjacency database in which the label attached to the main phase and the label attached to the grain boundary phase are associated with each other based on the positional relationship between the main phase and the grain boundary phase is stored.
The input device is
It is configured to accept the first classification condition and the second classification condition,
The processing device is
Based on at least one of the main phase database and the grain boundary phase database, the phase of the first label whose tissue characteristics satisfy the first classification condition is extracted.
Based on the adjacency database, the phase of the second label corresponding to the phase of the first label is extracted.
Based on at least one of the main phase database and the grain boundary phase database, the phase of the third label whose tissue characteristics satisfy the second classification condition is extracted from the phase of the second label.
A material structure analysis system, characterized in that the phase is either a main phase or a grain boundary phase. The input device is
It is also configured to accept measurement items
The processing device is
The material structure analysis system according to claim 9, wherein the structural characteristics of the phase of the third label are processed according to the measurement items. The adjacency database is
The material structure analysis system according to claim 9, wherein the label attached to the grain boundary phase defined by the line segment is associated with the label attached to the main phase adjacent to the grain boundary phase. The grain boundary phase database includes a two-particle grain boundary phase database and a triple-point grain boundary phase database.
The two-particle grain boundary database is associated with the tissue characteristics obtained by analyzing each of the two-particle grain boundaries with the label attached to the two-particle boundary phase defined by the line segment.
The triple point grain boundary phase database according to claim 9, wherein the label attached to the triple point grain boundary phase defined in the region is associated with the tissue characteristics obtained by analyzing each triple point grain boundary phase. Material structure analysis system. The adjacency database is
The material according to claim 12, wherein the label attached to the two-particle grain boundary phase defined by the line segment is associated with the label attached to the main phase adjacent to the two-particle grain boundary phase. Tissue analysis system. The adjacency database is
The label attached to the two-particle grain boundary phase defined by the line segment is associated with the label attached to the triple-point grain boundary phase corresponding to the end point of the two-particle grain boundary phase. Item 12. The material structure analysis system according to Item 12. The adjacency database further
The label attached to the two-particle grain boundary phase defined by the line segment is associated with the label attached to the triple-point grain boundary phase corresponding to the end point of the two-particle grain boundary phase. Item 13. The material structure analysis system according to Item 13.

Images