Method for obtaining feature quantities of jagged grain boundaries, information processing device, and program

Abstract

To obtain feature quantities of jagged grain boundaries that affect magnetic properties from images of magnetic materials.SOLUTION: A method for obtaining feature quantities of jagged grain boundaries includes: extracting grain boundaries between crystal particles from an image of a magnetic material containing a plurality of crystal particles (S503), identifying jagged grain boundaries from among the extracted grain boundaries (S504), and performing at least one of the following to acquire the feature quantities of the jagged grain boundaries: (1) counting the number of extreme values of the jagged grain boundaries (S505); (2) calculating the ratio of the length of the jagged grain boundaries to the total length of a plurality of grain boundaries within a predetermined region of the image (S506); (3) calculating an extreme value angle from the slope of a line segment connecting an extreme value and an extreme value of the jagged grain boundaries (S507), or (4) calculating the frequency of occurrence of jagged grain boundaries relative to the number of particles contained within the predetermined region of the image (S508).SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a method for obtaining characteristic quantities of jagged grain boundaries, an information processing apparatus, and a program.

Background Art

[0002] Conventionally, it has been known that the bulk properties of functional materials and structural materials are affected by their microstructure, and the calculation and prediction of bulk properties have been performed from the analysis of crystal microstructure. In particular, since the aggregate structure of magnetic materials is complex, techniques for analyzing tissue characteristics from images have advanced by using machine learning, which has been developed in recent years.

[0003] Patent Document 1 describes that an information processing apparatus generates a database by using, as adjacent relation data, tissue characteristic information such as the area and centroid coordinates of a main phase, which is a crystal particle, and the area and grain boundary width of each adjacent grain boundary phase and triple point grain boundary phase, which are parts of the grain boundary phase, from each image of the main phase, the grain boundary phase sandwiched between the main phases, and the triple point grain boundary phase.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, although Patent Document 1 describes the quantification of characteristic quantities at adjacent main phases, grain boundary phases, and triple points, it does not mention the unique shape of the material that can be a characteristic quantity related to magnetic properties.

[0006] For example, the quality of steel materials can be managed with a mill sheet, but in order to evaluate the magnetic properties and variations of magnetic materials, it is necessary to separately measure the magnetic properties.

[0007] The present invention has been made in view of the above problems, and aims to provide a method for acquiring features of jagged grain boundaries, an information processing device, and a program that can acquire features of jagged grain boundaries that affect magnetic properties from images of magnetic materials. [Means for solving the problem]

[0008] The present invention provides a method for obtaining feature quantities of jagged grain boundaries, which involves extracting grain boundaries between crystal particles from an image of a magnetic material containing multiple crystal particles, identifying jagged grain boundaries from the extracted grain boundaries that have alternating ridges and ridges like the teeth of a saw, and obtaining feature quantities of jagged grain boundaries by performing at least one of the following: (1) counting the number of extrema of the jagged grain boundaries, (2) calculating the ratio of the length of the jagged grain boundaries to the total length of multiple grain boundaries within a predetermined region of the image, (3) calculating the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundaries, or (4) calculating the frequency at which jagged grain boundaries occur relative to the number of particles contained within a predetermined region of the image.

[0009] Furthermore, the information processing apparatus of the present invention comprises a storage means for storing an image of a magnetic material containing a plurality of crystal particles captured by an imaging device, and a processing means having a processor and memory for processing the image stored in the storage means. The processing means extracts grain boundaries between crystal particles from the image, identifies jagged grain boundaries in which the bumps and depressions are arranged alternately like the teeth of a saw, and performs at least one of the following to obtain a feature quantity of the jagged grain boundaries: (1) counting the number of extrema of the jagged grain boundaries, (2) calculating the ratio of the length of the jagged grain boundaries to the total length of a plurality of grain boundaries within a predetermined region of the image, (3) calculating the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundaries, or (4) calculating the frequency in which jagged grain boundaries occur relative to the number of particles contained within a predetermined region of the image.

[0010] Furthermore, the program of the present invention causes a computer, which includes a storage means for storing an image of a magnetic material containing a plurality of crystal particles captured by an imaging device, and a processing means having a processor and memory for processing the image stored in the storage means, to perform at least one of the following to obtain a feature quantity of the jagged grain boundaries: extracting grain boundaries between crystal particles from the image, identifying jagged grain boundaries with alternating bumps and depressions like the teeth of a saw from among the extracted grain boundaries, and (1) counting the number of extrema of the jagged grain boundaries, (2) calculating the ratio of the length of the jagged grain boundaries to the total length of a plurality of grain boundaries within a predetermined region of the image, (3) calculating the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundaries, or (4) calculating the frequency at which jagged grain boundaries occur relative to the number of particles contained within a predetermined region of the image. [Effects of the Invention]

[0011] According to the present invention, it is possible to obtain characteristic quantities of jagged grain boundaries that affect magnetic properties from images of magnetic materials. Other challenges and novel features will become apparent from the description and accompanying drawings in this specification. [Brief explanation of the drawing]

[0012] [Figure 1] This diagram shows the overall configuration of the jagged grain boundary evaluation system 1 of Example 1. [Figure 2] This is a hardware block diagram of the information processing unit 21 in Example 1. [Figure 3] This figure shows the grain boundary image of Example 1. [Figure 4] This is a diagram illustrating the definition of the jagged grain boundary 33 in Example 1. [Figure 5] This is a flowchart showing the method for obtaining feature quantities of the jagged grain boundary in Example 1. [Figure 6A] This figure illustrates the characteristic quantity (number of extreme values) of the jagged grain boundary in Example 1. [Figure 6B] This figure illustrates the characteristic quantities (ratio of jagged grain boundary length) of the jagged grain boundary in Example 1. [Figure 6C] It is a diagram for explaining the characteristic quantity (extreme value angle) of the jagged grain boundary of Example 1. [Figure 6D] It is a diagram for explaining the characteristic quantity (jagged grain boundary occurrence frequency) of the jagged grain boundary of Example 1. [Figure 7] It is a diagram showing GUI700 of Example 1. [Figure 8] It is a flowchart showing the method for obtaining the characteristic quantity of the jagged grain boundary of Example 2. [Figure 9] It is a diagram showing the relationship between the jagged grain boundary occurrence frequency and the coercive force of Example 2. [Figure 10] It is a flowchart showing the method for obtaining the characteristic quantity of the jagged grain boundary of Example 3. [Figure 11] It is a diagram showing GUI1100 of Example 3. [Figure 12] It is a flowchart showing the method for obtaining the characteristic quantity of the jagged grain boundary of Example 4. [Figure 13] It is a diagram showing GUI1300 of Example 4.

Mode for Carrying Out the Invention

[0013] In the following embodiments, when necessary for convenience, they will be divided and described in multiple sections or embodiments. However, unless otherwise specified, they are not unrelated to each other, and one is related to a partial or entire modification example, details, supplementary explanation, etc. of the other.

[0014] Also, in the following embodiments, when referring to the number of elements, etc. (including the number, numerical value, quantity, range, etc.), unless otherwise specified and in cases where it is clearly limited to a specific number in principle, it is not limited to that specific number, and it may be more than or less than the specific number.

[0015] Furthermore, in the following embodiments, it is needless to say that the constituent elements (including element steps, etc.) are not necessarily essential unless otherwise specified and in cases where they are clearly considered essential in principle.

[0016] Similarly, in the following embodiments, when referring to the shape, positional relationship, etc., of components, unless otherwise specifically stated or when it is clearly not the case in principle, it shall include those that substantially approximate or resemble such shapes, etc. The same applies to the numerical values ​​and ranges mentioned above.

[0017] Furthermore, in all the drawings used to illustrate the embodiments, the same reference numerals are generally used for identical components, and repeated explanations of them are omitted. [Examples]

[0018] Figure 1 shows the overall configuration of the jagged grain boundary evaluation system of Example 1. The overall configuration of the jagged grain boundary evaluation system will be explained with reference to Figure 1.

[0019] (Jagged grain boundary evaluation system 1) The jagged grain boundary evaluation system 1 comprises an imaging device 10 that captures a cross-sectional image of a magnetic material, and an information processing device 20 that processes the image captured by the imaging device 10.

[0020] (Imaging device 10) The imaging device 10 may be an optical microscope, a scanning electron microscope (SEM), or a transmission electron microscope (TEM). The imaging device 10 includes a sample stage 11, a light source 12, a half mirror 13, and a detector 14. A sample 30 is placed on the sample stage 11. The sample 30 is a magnetic material, for example, a Permendur alloy of iron (Fe) and cobalt (Co), which is a soft magnetic material. Crystal grains of various sizes appear on the cross-section or surface of the soft magnetic material.

[0021] Light emitted from the light source 12 is reflected by the half mirror 13 in a direction parallel to the optical axis of the detection optical system, and focused and irradiated onto the sample 30 placed on the sample stage 11. The reflected or scattered light generated from the sample 30 is detected by the detector 14 via the half mirror 13. The reflected or scattered light detected by the detector 14 is converted into an electrical signal and output to the information processing device 20.

[0022] (Information processing device 20) The information processing device 20 is a computer that executes programs and performs various information processing according to those programs. The information processing device 20 comprises an information processing unit 21, an information storage unit 22, and an information display unit 23. The information storage unit 22 functions as a storage means for storing images of a sample 30 (magnetic material) containing multiple crystal particles, which are captured by the imaging device 10. The information processing unit 21 functions as a processing means for processing the images stored in the information storage unit 22.

[0023] The information processing unit 21 has one or more processors and one or more memories. Details of the information processing unit 21 will be described later. The information storage unit 22 is, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a combination thereof, and stores various programs executed by the information processing unit 21 (for example, a jagged grain boundary feature acquisition program 24), cross-sectional images of the sample 30, etc. The information display unit 23 displays various information.

[0024] (Information Processing Unit 21) Figure 2 is a hardware block diagram of the information processing unit 21 of Example 1. The hardware configuration of the information processing unit 21 of Example 1 will now be described with reference to Figure 2.

[0025] The information processing unit 21 includes a processor 221, a memory (main memory) 222, an auxiliary storage unit 223, and an input / output interface 224. The processor 221 is a CPU (Central Processing Unit), GPU (Graphics Processing Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), etc. The memory 222 is a DRAM (Dynamic Random Access Memory), etc. The auxiliary storage unit 223 is a ROM (Read Only Memory), etc. The input / output interface 224 is connected to the imaging device 10, the information storage unit 22, and the information display unit 23 so as to be able to communicate with them. The input / output interface 224 receives images of the sample 30 containing multiple crystal grains captured by the imaging device 10, writes various data such as the received images to the information storage unit 22, and reads various data written to the information storage unit 22. I / F stands for interface.

[0026] The processor 221 of the information processing unit 21 executes the jagged grain boundary feature acquisition program 24 to perform image processing (e.g., binarization) on the image of the sample 30 to extract grain boundaries and jagged grain boundaries in the image, and to calculate the feature quantities of the jagged grain boundaries. The information processing unit 21 may also have dedicated circuits such as ASICs specialized for image processing and calculating the feature quantities of jagged grain boundaries.

[0027] Figure 3 shows the grain boundary image of Example 1. Referring to Figure 3, (a) the grain boundary image and (b) the jagged grain boundary extraction image will be explained.

[0028] (Grain boundary image P1) Figure 3(a) is a grain boundary image P1 obtained by optically imaging the surface of a Permendur material. The grain boundary image P1 includes a plurality of crystalline grains 31 and grain boundaries 32 between the crystalline grains 31. In the grain boundary image P1, the white areas are crystalline grains 31, and the black areas are grain boundaries 32. The grain boundaries 32 include jagged grain boundaries in which the irregularities are arranged alternately like the teeth of a saw.

[0029] (Image of extracted jagged grain boundaries P2) Figure 3(b) is a magnified view of the region containing the jagged grain boundary extracted from the grain boundary image P1. The jagged grain boundary extracted image P2 is a magnified view of the rectangular dotted line in the grain boundary image P1. The jagged grain boundary extracted image P2 includes the jagged grain boundary 33, as shown by the elliptical dotted line in Figure 3(b).

[0030] (Definition of jagged grain boundary 33) Figure 4 is a diagram illustrating the definition of the jagged grain boundary 33 in Example 1. Next, the definition of the jagged grain boundary 33 in Example 1 will be explained with reference to Figure 4. The curve in Figure 4 is obtained by taking the jagged grain boundary 33 (33a) from Figure 3(b) and placing a straight line connecting the triple point 34 (34a) and the adjacent triple point 34 (34b) parallel to the X-axis.

[0031] As shown in Figure 4, • In a grain boundary 33(33a) connecting a triple point 34(34a) surrounded by three crystal grains 31 and an adjacent triple point 34(34b), there is a predetermined number (for example, 3) or more of extreme values ​​35. The width W1 between the maximum value 35(35a) and the minimum value 35(35b) is at least a predetermined multiple (for example, 2 times) of the width W2 of the grain boundary 33(33a). We define grain boundary 32 as a jagged grain boundary 33.

[0032] (Method for obtaining features of jagged grain boundaries 33) Figure 5 is a flowchart showing the method for acquiring features of jagged grain boundaries in Example 1. Next, the method for acquiring features of jagged grain boundaries in Example 1 will be explained with reference to Figure 5. Each step in Figure 5 is executed, for example, by the processor 221 of the information processing unit 21 (controller) of the information processing device 20 executing the jagged grain boundary feature acquisition program 24 stored in the information storage unit 22. The jagged grain boundary feature acquisition program 24 includes instruction code that causes the processor 221 to execute S501 to S512, which will be described later.

[0033] The information processing unit 21 receives the cross-sectional image of the magnetic material captured by the imaging device 10 (S501).

[0034] The information processing unit 21 performs a binarization process on the input cross-sectional image and binarizes the cross-sectional image (S502).

[0035] The information processing unit 21 uses the binarized image to extract grain boundaries 32 connecting the triple point 34 surrounded by three crystal grains 31 and adjacent triple points 34 (S503).

[0036] The information processing unit 21 extracts jagged grain boundaries 33 from among the multiple grain boundaries 32 extracted in S503 (S504). Specifically, the information processing unit 21 counts the number of extrema of the grain boundary 32 according to the definition of a jagged grain boundary described above, and calculates the width W1 between the maximum value 35 (35a) and minimum value 35 (35b) of the grain boundary 32 and the width W2 of the grain boundary 32. Then, the information processing unit 21 extracts grain boundaries 32 as jagged grain boundaries 33 if the number of counted extrema is greater than or equal to a predetermined number and the width W1 is greater than or equal to a predetermined multiple of the width W2.

[0037] The information processing unit 21 may also assign labels to the extracted jagged grain boundaries 33 for identification.

[0038] Next, the information processing unit 21 calculates the feature quantities of the jagged grain boundary 33 in S505-S508.

[0039] (Number of extreme values) The information processing unit 21 counts the number of extrema at each jagged grain boundary 33 (S505). For example, the information processing unit 21 approximates the jagged grain boundary 33 with a sine curve using image analysis and counts the number of extrema at the jagged grain boundary 33. As shown in Figure 6A, the information processing unit 21 identifies the extrema 35 at the jagged grain boundary 33 and counts the number of identified extrema 35 (number of extrema). In the example in Figure 6A, the number of extrema = 7.

[0040] (Percentage of jagged grain boundary length) The information processing unit 21 calculates the ratio of the length of the jagged grain boundary 33 to the total length of the multiple grain boundaries 32 within a predetermined region of the cross-sectional image (ratio of jagged grain boundary length) (S506). As shown in Figure 6B, the information processing unit 21 calculates the total length of the multiple grain boundaries 32 within the predetermined region A1 and calculates the length of the jagged grain boundary 33, which is shown by the elliptical dotted line. Then, the information processing unit 21 calculates the ratio of the length of the jagged grain boundary 33 to the calculated total length of the grain boundaries 32.

[0041] (Extreme angles) The information processing unit 21 calculates the extremum angle θ from the slope of the line segment connecting the extremum 35 of the jagged grain boundary 33 with the adjacent extremum 35 (S507). As shown in Figure 6C, the information processing unit 21 defines θ1 as the angle between two lines: line segment L1 connecting the extremum 35 (35a) and the adjacent extremum 35 (35c), and line segment L2 passing through the extremum 35 (35a); and θ2 as the angle between two lines: line segment L3 connecting the extremum 35 (35a) and the adjacent triple point 34 (34a), and line segment L2 passing through the extremum 35 (35a). The sum of θ1 and θ2 is defined as the extremum angle θ, and this extremum angle θ (angle θ between line segment L1 and line segment L3) is calculated. The information processing unit 21 calculates the extremum angle θ (=θ1+θ2) for each extremum 35.

[0042] (Frequency of jagged grain boundary occurrence) The information processing unit 21 calculates the frequency at which jagged grain boundaries 33 occur relative to the number of particles contained within a predetermined region of the cross-sectional image (jagged grain boundary occurrence frequency) (S508). As shown in Figure 6D, the information processing unit 21 counts the number of multiple crystal particles 31 contained within the predetermined region A2 (number of particles) and counts the number of jagged grain boundaries 33 indicated by the elliptical dotted line. Then, the information processing unit 21 calculates the ratio of the number of jagged grain boundaries 33 to the counted number of particles.

[0043] Then, the information processing unit 21 constructs a jagged grain boundary database (S509) which includes the number of extreme values ​​counted in S505, the ratio of jagged grain boundary lengths calculated in S506, the extreme value angle θ calculated in S507, and the frequency of jagged grain boundary occurrence calculated in S508.

[0044] Then, the information processing unit 21 analyzes the information in the constructed jagged grain boundary DB (S510) and outputs the analysis results (S511). Then, it constructs a DB containing the analysis results (S512).

[0045] Note that the above-described analysis (S510) may be performed using other equipment. Furthermore, the information processing unit 21 may acquire a magnetic properties database and analyze the magnetic properties of the sample 30 using the acquired magnetic properties database and the jagged grain boundary database.

[0046] (GUI (Graphical User Interface) 700) Figure 7 shows the GUI 700 of Example 1. The GUI 700 of Example 1 will be described with reference to Figure 7. This GUI 700 is displayed, for example, on the information display unit 23.

[0047] The GUI 700 includes a sample input window 701 for inputting various information about the sample 30, a grain boundary image display window 702 for displaying a grain boundary extraction image 702a in which grain boundaries 32 have been extracted, and a jagged grain boundary image display window 703 for displaying a jagged grain boundary extraction image 703a in which jagged grain boundaries have been extracted. In the jagged grain boundary extraction image 703a, the jagged grain boundaries 33 may be labeled (in the example in Figure 7, the label "1" is assigned), and the selected jagged grain boundaries 33 may be highlighted. The GUI 700 also includes an analysis result display window 704 for displaying the analysis results of the jagged grain boundaries 33, and a registration window 705 for registering the analysis results in the database.

[0048] In Example 1, the number of extreme values ​​(S505), the ratio of the length of the jagged grain boundary (S506), the extreme value angle θ (S507), and the frequency of occurrence of the jagged grain boundary (S508) are obtained as features of the jagged grain boundary 33, and each feature of the jagged grain boundary 33 is displayed in the analysis result display window 704.

[0049] (Effects of Example 1) In Example 1, from an image of a magnetic material, the number of extreme values, the ratio of the length of the jagged grain boundaries 33, the extreme value angle θ, and the frequency of jagged grain boundary occurrence can be obtained as feature quantities of the jagged grain boundaries 33 that affect the magnetic properties. From these feature quantities of the jagged grain boundaries 33, it is possible to predict the magnetic properties of the sample 30. Furthermore, the feature quantities of the jagged grain boundaries 33 and the magnetic properties can be utilized in the field of materials informatics (MI).

[0050] Furthermore, in Example 1, by defining a jagged grain boundary as having three or more extreme values ​​and where the width between the maximum and minimum values ​​is at least twice the width of the jagged grain boundary, the jagged grain boundary 33 can be easily extracted.

[0051] Furthermore, in Example 1, by calculating the feature quantities of four types of jagged grain boundaries, it is possible to obtain a multifaceted set of feature quantities for the jagged grain boundaries 33 that affect the magnetic properties.

[0052] Furthermore, in Example 1, by assigning labels to the extracted jagged grain boundaries 33, it is possible to easily identify the jagged grain boundaries 33 that possess the acquired feature quantities. [Examples]

[0053] Example 2 describes an example of obtaining one of the features of multiple jagged grain boundaries 33. Explanations that overlap with Example 1 will be omitted as appropriate. Figure 8 is a flowchart showing the method for obtaining the features of the jagged grain boundaries in Example 2. The method for obtaining the features of the jagged grain boundaries in Example 2 will be explained with reference to Figure 8.

[0054] The information processing unit 21 in Example 2 executes the processes S801 to S804 in the same manner as the processes S501 to S504 in Example 1.

[0055] The information processing unit 21 in Example 2 executes one of the following processes (S805-S808) for calculating the feature quantities of the jagged grain boundaries 33. Each of the processes S805-S808 corresponds to the processes S505-S508 in Example 1. • Count the number of extreme values ​​(35) for each jagged grain boundary (S805) - Calculate the ratio of the length of a jagged grain boundary 33 to the total length of multiple grain boundaries 32 within a predetermined region of the cross-sectional image (ratio of jagged grain boundary length) (S806) • Calculate the extremum angle θ from the slope of the line segment connecting the extremum 35 of the jagged grain boundary 33 with the adjacent extremum 35 (S807) • Calculate the frequency of occurrence of jagged grain boundaries 33 (jagged grain boundary occurrence frequency) relative to the number of particles contained within a predetermined region of the cross-sectional image (S808)

[0056] In Example 2, an example was described in which any one of the processes for calculating the feature quantities of the jagged grain boundaries 33 (S805-S808) is performed, but the present invention is not limited to this. For example, any two of the processes for calculating the feature quantities of the jagged grain boundaries 33 (S805-S808) may be performed, or any three may be performed.

[0057] Then, the information processing unit 21 in Example 2 executes the processes S809 to S812 in the same manner as the processes S509 to S512 in Example 1. In Example 2, the information processing unit 21 receives a magnetic properties DB in which the feature quantities of the jagged grain boundary 33 and the magnetic properties are associated (S813), and uses this magnetic properties DB to analyze the feature quantities of the jagged grain boundary 33 (S810). For example, the magnetic properties include at least one of coercivity, saturation magnetic flux density, or permeability, and the magnetic properties DB stores data in which the frequency of jagged grain boundary occurrence and coercivity are associated.

[0058] (Relationship between the frequency of jagged grain boundary occurrence and coercivity) Figure 9 shows the relationship between the frequency of jagged grain boundary occurrence and coercivity, which is an example of the magnetic properties DB of Example 2. The relationship between the frequency of jagged grain boundary occurrence and coercivity will now be explained.

[0059] In the graph in Figure 9, the horizontal axis represents the frequency of jagged grain boundary occurrence (jagged grain boundary occurrence frequency), and the vertical axis represents coercivity (Hc(A / m)). As shown in the graph in Figure 9, it can be seen that there is a proportional relationship between the jagged grain boundary occurrence frequency and coercivity.

[0060] (Effects of Example 2) By using the relationship between the frequency of jagged grain boundary occurrence and coercivity, it is possible to predict the coercivity of sample 30 from the frequency of jagged grain boundary occurrence calculated in S808.

[0061] Furthermore, in Example 2, only the necessary features from the jagged grain boundary features can be calculated. Other effects are the same as in Example 1, so their explanation will be omitted. [Examples]

[0062] Example 3 describes an example of obtaining at least the extreme angle among the feature quantities of multiple jagged grain boundaries 33. Explanations that overlap with Example 1 will be omitted as appropriate. Figure 10 is a flowchart showing the method for obtaining the feature quantities of the jagged grain boundaries in Example 3. The method for obtaining the feature quantities of the jagged grain boundaries in Example 3 will be explained with reference to Figure 10. The flowchart of Example 3 is performed to evaluate the heat treatment state of the sample 30.

[0063] The information processing unit 21 in Example 3 executes the processes S1001 to S1004 in the same manner as the processes S501 to S504 in Example 1.

[0064] Then, the information processing unit 21 calculates the extremum angle θ from the slope of the line segment connecting the extremum 35 of the jagged grain boundary 33 and the adjacent extremum 35, similar to S507 in Example 1 (S1007).

[0065] Then, the information processing unit 21 of Example 3 executes one of the following processes (S1005, S1006, or S1008) for calculating the feature quantities of the jagged grain boundaries 33. Each of the processes S1005, S1006, or S1008 corresponds to the processes S505, S506, or S508 of Example 1. • Count the number of extreme values ​​for each jagged grain boundary 33 (S1005) - Calculate the ratio of the length of a jagged grain boundary 33 to the total length of multiple grain boundaries 32 within a predetermined region of the cross-sectional image (S1006) • Calculate the frequency of occurrence of jagged grain boundaries 33 (jagged grain boundary occurrence frequency) relative to the number of particles contained within a predetermined region of the cross-sectional image (S1008)

[0066] In Example 3, an example was described in which one of the processes for calculating the feature quantities of the jagged grain boundaries 33 (S1005, S1006, or S1008) is performed, but the present invention is not limited to this. For example, any two of the processes for calculating the feature quantities of the jagged grain boundaries 33 (S1005, S1006, or S1008) may be performed.

[0067] Then, the information processing unit 21 in Example 3 executes the processes S1009 to S1012 in the same manner as the processes S509 to S512 in Example 1. In Example 3, the information processing unit 21 receives a magnetic properties DB in which the feature quantities of the jagged grain boundary 33 and their magnetic properties are stored in association (S1013), and analyzes the feature quantities of the jagged grain boundary 33 using the magnetic properties DB (S1010).

[0068] Annealing the sample 30 causes smaller particles adjacent to the grain boundaries 32 of larger particles to be absorbed. If the annealing is insufficient, the smaller particles adjacent to the grain boundaries 32 of larger particles will not be absorbed and will remain, resulting in the formation of jagged grain boundaries 32 that correlate with the extreme angle. Furthermore, if smaller particles are not absorbed by larger particles, particles of smaller size will remain, affecting the coercivity, which is dependent on particle size.

[0069] By using this relationship between the extreme angle and coercivity, it is possible to evaluate whether the annealing treatment is sufficient based on the extreme angle calculated in S1007 and other feature quantities of the jagged grain boundary 33. It is also possible to predict the coercivity of the sample 30.

[0070] (GUI (Graphical User Interface) 1100) Figure 11 shows the GUI 1100 of Example 3. The GUI 1100 of Example 3 will be described with reference to Figure 11. This GUI 1100 is displayed, for example, on the information display unit 23. Descriptions that overlap with the GUI 700 of Example 1 will be omitted as appropriate.

[0071] GUI1100 includes a sample input window 1101, a grain boundary image display window 1102 that displays a grain boundary image 1102a, and a jagged grain boundary image display window 1103 that displays a jagged grain boundary extraction image 1103a in which jagged grain boundaries have been extracted. Furthermore, GUI1100 of Example 3 includes an analysis result display window 1104 that displays the analysis results 1104a of the jagged grain boundaries 33 and the distribution of the analysis results 1104b.

[0072] In Example 3, the extreme value angle θ is calculated (S1007), and the extreme value angle θ for each extreme value is displayed in the analysis results display window 1104. In the example in Figure 11, the analysis results display window 1104 displays the analysis results for the jagged grain boundary 33 that is labeled "1" and displayed in the jagged grain boundary image display window 1103.

[0073] Furthermore, in the distribution 1104b of Example 3, information corresponding to the magnitude of the number of extreme values ​​counted for each region of the divided image is mapped so that regions with a high number of extreme values ​​in sample 30 can be identified.

[0074] (Effects of Example 3) In Example 3, by calculating the extreme angle θ, it is possible to evaluate the annealing treatment conditions and the effects of heat treatment on the material.

[0075] Furthermore, in Example 3, by mapping information according to the magnitude of the number of extreme values, it is possible to grasp variations in the heat treatment state and coercivity. Other effects are the same as in Example 1, so their explanation will be omitted. [Examples]

[0076] Example 4 describes an example of obtaining at least the frequency of occurrence of jagged grain boundaries from the feature quantities of the jagged grain boundaries 33. Explanations that overlap with Example 1 will be omitted as appropriate. Figure 12 is a flowchart showing the method for obtaining the feature quantities of jagged grain boundaries in Example 4. The method for obtaining the feature quantities of jagged grain boundaries in Example 4 will be explained with reference to Figure 12. The flowchart of Example 4 is performed to predict the correlation and coercivity distribution of the coercivity of sample 30.

[0077] The information processing unit 21 in Example 4 executes the processes S1201 to S1204 in the same manner as the processes S501 to S504 in Example 1.

[0078] Then, the information processing unit 21 calculates the frequency at which jagged grain boundaries 33 occur (jagged grain boundary occurrence frequency) relative to the number of particles contained within a predetermined area of ​​the cross-sectional image, similar to S508 in Example 1 (S1208).

[0079] Then, the information processing unit 21 in Example 4 executes the processes S1209 to S1212 in the same manner as the processes S509 to S512 in Example 1. In Example 4, the information processing unit 21 receives a magnetic properties DB in which the feature quantities of the jagged grain boundaries (frequency of jagged grain boundary occurrence) and magnetic properties are associated (S1213), and analyzes the feature quantities of the jagged grain boundaries using the magnetic properties DB (S1210).

[0080] By using the relationship between the frequency of jagged grain boundary occurrence and coercivity (see, for example, Figure 9), the coercivity of sample 30 can be predicted from the frequency of jagged grain boundary occurrence calculated in S1208.

[0081] (GUI (Graphical User Interface) 1300) Figure 13 shows the GUI 1300 of Example 4. The GUI 1300 of Example 4 will be described with reference to Figure 13. This GUI 1300 is displayed, for example, on the information display unit 23. Descriptions that overlap with the GUI 700 of Example 1 will be omitted as appropriate.

[0082] GUI1300 includes a sample input window 1301, a grain boundary image display window 1302 that displays a grain boundary image 1302a, and a jagged grain boundary image display window 1303 that displays a jagged grain boundary extraction image 1303a in which jagged grain boundaries 33 have been extracted. Furthermore, GUI1300 of Example 4 includes an analysis result display window 1304 that displays a graph 1304a showing the correlation between the occurrence frequency of jagged grain boundaries 33 and coercivity, and an analysis result display window 1304b showing the distribution of the occurrence frequency of jagged grain boundaries 33.

[0083] In Example 4, in order to calculate the frequency of occurrence of jagged grain boundaries 33 (S1208), the calculated frequency of occurrence of jagged grain boundaries is plotted on graph 1304a and displayed as marks (star marks in Figure 13) in the analysis results display window 1304.

[0084] Furthermore, in the distribution 1304b of Example 4, information corresponding to the magnitude of occurrence frequency is mapped to each region of the divided image, so that the regions in the sample 30 where the occurrence frequency of jagged grain boundaries 33 is high can be identified.

[0085] In addition, the analysis results display window 1304 of Example 4 also displays the frequency value of occurrence of jagged grain boundaries 33 within a predetermined region.

[0086] (Effects of Example 4) In Example 4, by calculating the frequency of occurrence of jagged grain boundaries 33, the characteristics (coercivity) of the sample 30 can be predicted using the correlation between the frequency of occurrence of jagged grain boundaries 33 and coercivity.

[0087] Furthermore, in Example 4, by mapping information corresponding to the magnitude of the occurrence frequency of jagged grain boundaries 33, it is possible to understand the variation in coercivity in the sample 30.

[0088] <Regarding variations of the present invention> The present invention is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations. [Explanation of Symbols]

[0089] 1: Jagged grain boundary evaluation system 10: Imaging device 11: Sample stage 12:Light source 13: Half-mirror 14: Detector 20: Information Processing Devices 21: Information Processing Section 22: Information storage section 23: Information display section 24: Program for acquiring features of jagged grain boundaries 30: Sample 31: Crystal Particles 32: Grain boundary 33: Jagged grain boundaries 34: Triple Point 35: Extreme Values 221: Processor 222: Memory 223: Auxiliary storage 224: Input / Output Interface

Claims

1. The processor is Extracting grain boundaries between multiple crystal particles from an image of a magnetic material containing multiple crystal particles, From the extracted grain boundaries, identify the jagged grain boundaries in which the irregularities are arranged alternately like the teeth of a saw, and The method involves obtaining a feature quantity of the jagged grain boundary by performing at least one of the following: (1) counting the number of extrema of the jagged grain boundary; (2) calculating the ratio of the length of the jagged grain boundary to the total length of multiple grain boundaries within a predetermined region of the image; (3) calculating the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundary; or (4) calculating the frequency at which the jagged grain boundary occurs relative to the number of particles contained within a predetermined region of the image. A method for obtaining characteristic features of jagged grain boundaries.

2. The aforementioned jagged grain boundary has three or more extrema, and the distance between the maximum and minimum values ​​is at least twice the width of the jagged grain boundary. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

3. Obtaining the feature quantities of the jagged grain boundary means that the processor The method includes obtaining the characteristic quantities of the jagged grain boundary by performing the following: (1) counting the number of extrema of the jagged grain boundary; (2) calculating the ratio of the length of the jagged grain boundary to the total length of multiple grain boundaries within a predetermined region of the image; (3) calculating the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundary; and (4) calculating the frequency at which the jagged grain boundary occurs relative to the number of particles contained within a predetermined region of the image. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

4. Obtaining the feature quantities of the jagged grain boundary means that the processor (3) The extreme value angle is calculated from the slope of the line segment connecting the extreme values ​​of the jagged grain boundary, and (1) counting the number of extreme values ​​of the jagged grain boundaries, (2) calculating the ratio of the length of the jagged grain boundaries to the total length of multiple grain boundaries within a predetermined region of the image, or (4) calculating the frequency at which the jagged grain boundaries occur relative to the number of particles contained within a predetermined region of the image, This includes obtaining the characteristic quantities of the jagged grain boundaries. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

5. Obtaining the feature quantities of the jagged grain boundary means that the processor The process includes at least (4) calculating the frequency at which the jagged grain boundaries occur relative to the number of particles contained within a predetermined region of the image, thereby obtaining the characteristic quantities of the jagged grain boundaries. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

6. The processor further comprises assigning a label to the identified jagged grain boundary to identify the jagged grain boundary. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

7. Obtaining the feature quantities of the jagged grain boundary includes the processor performing at least (1) counting the number of extrema of the jagged grain boundary, The processor further includes mapping information corresponding to the magnitude of the number of extreme values ​​counted for each divided region of the image and displaying it on the display unit. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

8. Obtaining the feature quantities of the jagged grain boundary includes the processor calculating at least (4) the frequency at which the jagged grain boundary occurs with respect to the number of particles contained within a predetermined region of the image, The processor further includes mapping information corresponding to the magnitude of the frequency of occurrence of the jagged grain boundaries calculated for each region of the divided image, and displaying it on the display unit. The method for obtaining feature quantities of jagged grain boundaries according to feature 1.

9. A storage means for storing an image of a magnetic material containing multiple crystal particles captured by an imaging device, The system comprises a processing means having a processor and memory for processing the image stored in the storage means, The processing means is From the aforementioned image, grain boundaries between crystal grains are extracted, From the extracted grain boundaries, we identify jagged grain boundaries in which the irregularities are arranged alternately like the teeth of a saw. The characteristic quantities of the jagged grain boundary are obtained by performing at least one of the following: (1) counting the number of extrema of the jagged grain boundary; (2) calculating the ratio of the length of the jagged grain boundary to the total length of multiple grain boundaries within a predetermined region of the image; (3) calculating the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundary; or (4) calculating the frequency at which the jagged grain boundary occurs relative to the number of particles contained within a predetermined region of the image. An information processing device characterized by the following:

10. The aforementioned jagged grain boundary has three or more extrema, and the distance between the maximum and minimum values ​​is at least twice the width of the jagged grain boundary. The information processing apparatus according to feature 9.

11. The processing means is The following steps are performed to obtain the characteristic quantities of the jagged grain boundary: (1) count the number of extrema of the jagged grain boundary; (2) calculate the ratio of the length of the jagged grain boundary to the total length of grain boundaries within a predetermined region of the image; (3) calculate the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundary; and (4) calculate the frequency of occurrence of the jagged grain boundary relative to the number of particles contained within a predetermined region of the image. The information processing apparatus according to feature 9.

12. The processing means is (3) The extremum angle is calculated from the slope of the line segment connecting the extremums of the jagged grain boundary, (1) counting the number of extreme values ​​of the jagged grain boundaries, (2) calculating the ratio of the length of the jagged grain boundaries to the total length of grain boundaries within a predetermined region of the image, or (4) calculating the frequency at which the jagged grain boundaries occur relative to the number of particles contained within a predetermined region of the image, Obtain the characteristic features of the aforementioned jagged grain boundary. The information processing apparatus according to feature 9.

13. The processing means is At least (4) calculate the frequency at which the jagged grain boundary occurs relative to the number of particles contained within a predetermined region of the image, thereby obtaining the feature quantity of the jagged grain boundary. The information processing apparatus according to feature 9.

14. A computer comprising: a storage means for storing an image of a magnetic material containing multiple crystal particles captured by an imaging device; and a processing means having a processor and memory for processing the image stored in the storage means, Extracting grain boundaries between crystal grains from the aforementioned image, From the extracted grain boundaries, identify the jagged grain boundaries in which the irregularities are arranged alternately like the teeth of a saw, and Perform at least one of the following to obtain the characteristic quantities of the jagged grain boundary: (1) count the number of extrema of the jagged grain boundary; (2) calculate the ratio of the length of the jagged grain boundary to the total length of multiple grain boundaries within a predetermined region of the image; (3) calculate the extremum angle from the slope of the line segment connecting the extrema of the jagged grain boundary; or (4) calculate the frequency at which the jagged grain boundary occurs relative to the number of particles contained within a predetermined region of the image. A program characterized by the following features.

15. The aforementioned jagged grain boundary has three or more extrema, and the distance between the maximum and minimum values ​​is at least twice the width of the jagged grain boundary. The program according to feature 14.

Images