Method and apparatus for analyzing the structure of composite materials

Abstract

To provide an appropriate texture feature analysis method and an analyzer for composite material texture.SOLUTION: A composite material texture analysis method using an information processing device includes: an image input step (S1) of inputting an observation image of a composite material; a binary image creation step (S2) of dividing the observation image into regions and creating a binary image; a texture feature analysis step (S3) of analyzing a texture feature from the binary image; and a texture feature output step (S4) of outputting the analyzed texture feature. The texture feature analysis step (S3) further includes: a boundary image creation step (S31) of creating a boundary image in which boundaries in the binary image are defined as positive and others are defined as negative; and a normalized boundary length measurement step (S32) of measuring the total number of pixels in boundaries of the boundary image, dividing by a pixel size to convert into a boundary length, and measuring a normalized boundary length derived by normalizing the boundary length by a ratio of an imaged area to a designated area. The composite material contains Gd2O2S.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method and an analysis device for analyzing the tissue characteristics of a composite material.

Background Art

[0002] The performance of a light-emitting device composed of a light-emitting material and a non-light-emitting material depends not only on the characteristics of each material but also on the structure of the light-emitting device. Taking a resin scintillator as an example, the performance of a resin scintillator formed by pulverizing a ceramic scintillator, mixing it with a resin, and curing the resin depends not only on the material characteristics of the powder and the resin but also on the structure of the resin scintillator. Technologies for improving performance by controlling the structure of resin scintillators were investigated. In addition, technologies for improving performance by structure control were also investigated for light-emitting devices other than resin scintillators.

[0003] Patent Document 1 describes "a bulk light-converting ceramic composite composed of a solidified body having a structure in which at least two or more oxide phases are continuously and three-dimensionally intertwined with each other, and at least one of the oxide phases is a crystalline phase that emits fluorescence, and the interface length between the oxide phases per 1 mm on a plane in the light-converting ceramic composite is 150 mm or more and 1500 mm or less." (See the claims). There is described "a light-converting ceramic composite characterized in that the interface length between the oxide phases per 1 mm on a plane in the light-converting ceramic composite is 150 mm or more and 1500 mm or less." (See the claims).

[0004] Patent Document 2 describes "an X-ray detector obtained by kneading and curing a scintillator powder and a translucent resin, wherein the filling degree of the scintillator powder changes almost continuously in the thickness direction of the X-ray detector, the filling degree A on the side of the photodetector that converts light into electricity is greater than the filling degree B on the opposite side, and the ratio A / B of the filling degree A to the filling degree B is 1.1 or more and 5.0 or less." (See the claims).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] We are conducting research and development to improve device performance by analyzing the relationship between process, microstructure characteristics, and device performance, and by controlling the microstructure.

[0007] Patent Document 1 states that in order to obtain stronger fluorescence at the same thickness in a ceramic composite for photoconversion, it is important to control the interface length. The interface length is defined as the length of the interface observed as a boundary line when the structure of the ceramic composite for photoconversion is observed in a plane. However, the method for measuring the interface length is not described.

[0008] Patent Document 2 states that in order to reduce the amount of scintillator powder containing heavy metals used without significantly reducing the luminescence intensity of the detector, it is important to control the packing density of the scintillator powder. The packing density of the scintillator powder is expressed as a percentage of the observed area by observing the detector structure with a microscope. It is stated that the area percentage of scintillator powder relative to the observed area is calculated by drawing vertical and horizontal grid lines on a micrograph of the detector, counting the grids containing scintillator powder, and calculating the ratio. However, boundary length is not taken into consideration as a structural characteristic.

[0009] Tissue characteristics depend not only on the definition of tissue characteristics but also on the measurement method and the field of view size of the observed image. Without specifying the measurement method and field of view size, tissue characteristics cannot be treated as macroscopic average information and cannot be used for analyzing the relationship between process, tissue characteristics, and element performance.

[0010] The present invention aims to provide a method for analyzing the appropriate microstructure characteristics of composite material structures. [Means for solving the problem]

[0011] To give an example of the "method for analyzing the structure of composite materials" of the present invention for solving the above problems, the method for analyzing the structure of composite materials using an information processing device is as follows: The image input process involves inputting observation images of the composite material, A binary image creation step is performed by dividing the aforementioned observation image into regions to create a binary image, A tissue feature analysis step for analyzing tissue features from the aforementioned binary image, The process includes a tissue feature output step that outputs the analyzed tissue features, The aforementioned tissue characteristic analysis step is: A boundary image creation step, which creates a boundary image in which the boundary in the aforementioned binary image is positive and the rest is negative, A normalized boundary length measurement step involves measuring the total number of pixels at the boundary in the boundary image, dividing it by the pixel size to convert it to the boundary length, and measuring the normalized boundary length obtained by normalizing the boundary length by the ratio of the captured area to the specified area. It is characterized by containing the following.

[0012] Furthermore, an example of the "analysis device for composite material structure" of the present invention is an analysis device for composite material structure, An image input unit for inputting observation images of composite materials, A binary image creation unit that divides the aforementioned observation image into regions to create a binary image, A tissue feature analysis unit analyzes tissue features from the aforementioned binary image, It includes a tissue feature output unit that outputs the analyzed tissue features, The aforementioned tissue feature analysis unit, A boundary image creation unit that creates a boundary image in which the boundary in the aforementioned binary image is positive and everything else is negative, A normalized boundary length measuring unit measures the total number of pixels at the boundary in the boundary image, divides it by the pixel size to convert it into a boundary length, and measures a normalized boundary length obtained by normalizing the boundary length by the ratio of the captured area to the specified area. It is characterized by containing the following. [Effects of the Invention]

[0013] According to the present invention, by measuring the normalized boundary length using a boundary image created from a binary image, a method for analyzing appropriate tissue characteristics of a composite material structure can be provided. According to the present invention, the accuracy and efficiency of tissue characteristic analysis can be improved, and research and development for improving device characteristics by tissue control can be accelerated.

[0014] Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of Drawings

[0015] [Figure 1] It is a flowchart for measuring the normalized boundary length as a tissue characteristic of Example 1. [Figure 2A] It shows a SEM image explaining the criteria for correcting a binary image. [Figure 2B] It shows a binary image before correction explaining the criteria for correcting a binary image. [Figure 2C] It shows a binary image after correction explaining the criteria for correcting a binary image. [Figure 3A] It is an explanatory diagram showing the relationship between a binary image and a boundary image, and shows the binary image. [Figure 3B] It is an explanatory diagram showing the relationship between a binary image and a boundary image, and shows the boundary image. [Figure 4] It shows a display screen of a SEM image, a binary image, and a boundary image used for tissue characteristic analysis. [Figure 5] It shows an output example of the analysis result of tissue characteristics. [Figure 6] It is a flowchart showing the field size analysis of Example 2. [Figure 7A] It is an explanatory diagram of a t-test, showing the change in the t-distribution when the number of observations is changed. [Figure 7B] It is an explanatory diagram of a t-test, showing the regions on both sides of P(T<=t) for the difference T in the average values. [Figure 8] It is a diagram showing a calculation example of the relationship between the field size and the regions on both sides of P(T<=t). [Figure 9] It is a diagram showing an analysis device for a composite material structure of Example 3. [Figure 10] This is a functional block diagram of the composite material structure analysis device for Example 3. [Modes for carrying out the invention]

[0016] Embodiments of the present invention will be described in detail below with reference to the drawings. However, the present invention is not to be construed as being limited to the embodiments described below. It will be easily understood by those skilled in the art that the specific configuration can be modified without departing from the idea or spirit of the present invention. Furthermore, in the configuration of the invention described below, the same reference numerals may be used in common across different drawings for identical parts or parts having similar functions, and redundant explanations may be omitted. The designations such as "Part 1," "Part 2," and "Part 3" used in this specification are for the purpose of identifying constituent elements and do not necessarily limit their number, order, or content. Furthermore, the numbers used to identify components are used on a context-by-context basis, and a number used in one context does not necessarily indicate the same configuration in another context. Also, this does not prevent a component identified by one number from performing the function of another component identified by a different number. The positions, sizes, shapes, and ranges of each component shown in drawings, etc., may not represent their actual positions, sizes, shapes, and ranges in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, and ranges disclosed in drawings, etc. [Examples]

[0017] In Example 1, a resin scintillator is taken as the analysis target, and a method for analyzing the composite material tissue characteristics is described. When radiation such as X-rays or γ-rays hits a scintillator, it absorbs the energy and emits visible light. A resin scintillator is a composite material composed of scintillator powder and an epoxy resin. The scintillator powder is produced by pulverizing a ceramic scintillator. Examples of the ceramic scintillator material include Gd2O2S and CdWO4. A paste-like mixture composed of the scintillator powder and the epoxy resin is poured into a mold and the resin is cured to produce it. When cured, a difference occurs in the tissue characteristics in the direction of sedimentation due to the self-weight of the scintillator powder.

[0018] To analyze the morphological characteristics of the tissue, it is necessary to binarize the observation image. The tissue characteristics are analyzed in the binary image, and the analyzed tissue characteristics are output. Fig. 1 shows a flowchart for analyzing the tissue characteristics of the observation image. It includes an image input step (S1) for inputting an observation image of a composite material composed of a luminescent material and a non-luminescent material, a binary image creation step (S2) for dividing the observation image into regions to create a binary image, a tissue characteristic analysis step (S3) for analyzing the tissue characteristics in the binary image, and a tissue characteristic output step (S4) for outputting the analyzed tissue characteristics. The tissue characteristic analysis step (S3) includes a boundary image creation step (S31) for creating a boundary image and a normalized boundary length measurement step (S32) for measuring the normalized boundary length as a tissue characteristic. Hereinafter, the details of each step will be described.

[0019] <S1. Image input step> The observation image of the resin scintillator is obtained by a scanning electron microscope ( S canning E lectron MIt is photographed with a (microscope). To take a SEM image, the sample cross-section is exposed. If there are irregularities on the cross-section to be photographed, image contrast depending on the irregularities will be observed in the SEM image, so the surface to be photographed is processed to be flat by polishing. A focused ion beam processing apparatus or a cross-section polishing processing apparatus may be used for the flat processing. Since the scintillator and the epoxy resin are insulators, after polishing, Pt-Pd is coated with a thickness of several nm by an ion sputtering apparatus for antistatic purposes, and then an observation image is taken. The taken image is input into a composite material structure analysis system. Note that the input image is not limited to the SEM image. Images taken with other imaging devices such as an optical microscope or a probe microscope may also be used.

[0020] <S2. Binary Image Creation Step> The input observation image is regionally divided into a scintillator powder and a resin. After normalizing the intensity of the SEM image, it is binarized by a threshold method for regional division. To normalize the intensity of the SEM image, an intensity histogram of the SEM image is created, and the most frequent value of the image intensity of the scintillator powder and the most frequent value of the image intensity of the resin are specified. The image intensity is converted so that each most frequent value becomes the specified value. For example, after normalizing so that the most frequent value of the image intensity of the scintillator powder is 200 and the most frequent value of the image intensity of the resin is 60, it is binarized with a threshold of 130. An example of a SEM image of a resin scintillator is shown in Fig. 2A, and an image binarized by the threshold method is shown in Fig. 2B. In the binary image, the intensity of the pixels of the scintillator powder 1 is set to 1 (white), and the intensity of the pixels of the resin 2 is set to 0 (black). When the observation surface is produced by mirror polishing, a region 31 where particles are removed and a region 32 with polishing scratches are observed. When this SEM image is binarized by the threshold method, information different from the structure of the resin scintillator powder is mixed into the binary image. The information on particle removal and polishing scratches was excluded from the binary image by the following procedure. In the SEM image, when there are irregularities on the sample surface, secondary electrons increase from the side walls. It is judged that there are surface irregularities in a region with a high SEM image intensity, and it is corrected with a paint function while referring to the SEM image. An image with particle removal and polishing scratches corrected is shown in Fig. 2C.

[0021] <S3. Tissue Feature Analysis Step> As an organizational feature analyzed using a binary image, there is a filling rate. The number of pixels of the scintillator powder included in the binary image is counted and normalized by the total number of pixels to measure the filling rate. On the other hand, the normalized boundary length is an organizational feature that cannot be measured from the number of pixels of the binary image. Therefore, a method for measuring the normalized boundary length as an organizational feature was devised.

[0022] The process of measuring the normalized boundary length includes a boundary image creation step (S31) of creating a boundary image in which the boundaries of each region in the binary image are positive and the rest are negative, and a normalized boundary length measurement step (S32) of measuring the total number of pixels of the boundaries in the boundary image, converting it into a boundary length by dividing by the pixel size, and normalizing the boundary length by the ratio of the imaging area to the specified area to measure the normalized boundary length.

[0023] <S31. Boundary Image Creation Step> A boundary image is created by extracting the pixels of the outermost periphery of the scintillator powder in the binary image. An example of the binary image is shown in FIG. 3A, and an example of the boundary image created from the binary image is shown in FIG. 3B. The boundary image is an image in which the pixels of boundary 4 are positive and the other pixels are negative. Note that the same processing is executed if an image in which the pixels of boundary 4 are negative and the other pixels are positive is used and the pixels with a negative label are treated as the boundary.

[0024] <S32. Normalized Boundary Length Measurement Step> First, the number of pixels of the boundaries included in the boundary image is counted. The boundary length is measured by the number of boundary pixels / pixel size. The boundary length is normalized by the specified normalized area. 100μm 2 When normalizing with, multiply the boundary length by 100μm 2 / field of view area for normalization. The normalized boundary length is an organizational feature quantity indicating the density of the boundary.

[0025] <Display Screens of S2 and S3> The display screen used for measuring the filling rate and the normalized boundary length is shown in FIG. 4. First, the SEM image is displayed, and the parameters necessary for binarization are set. The parameters necessary for binarization are the parameters for intensity normalization and the threshold for binarization. When parameters are set and "Execute" is clicked, a binary image is created. The binary image is displayed, and threshing and polishing scratches are corrected (S2 completed). After correction, parameters required for tissue feature analysis are set. The parameters that need to be set are the pixel size and the normalized area. In the example of the figure, the pixel size is 0.44 μm and the normalized area is 100 μm 2 is. When "Execute" is clicked, tissue analysis is performed, and the boundary image, and the measurement results of the filling rate and the normalized boundary length are displayed (S3 completed). In the example of the figure, the filling rate is 0.55 and the normalized boundary length is 6.21.

[0026] <S4. Tissue Feature Output Step> Fig. 5 shows an output example of tissue feature analysis. The analysis results of the tissue features in the input binary image are summarized and output in a single line. It is in a format that allows a binary image set to be input, the tissue features to be analyzed by batch processing, and output as a tissue feature set. The numerical values are the same as those of the boundary image in Fig. 4.

[0027] According to this embodiment, a method for measuring the normalized boundary length, which is an appropriate tissue feature of the composite material tissue, can be provided, the accuracy and efficiency of tissue feature analysis can be improved, and the research and development of improving element characteristics by tissue control can be accelerated.

Example

[0028] Example 2 is an example in which, in addition to the tissue feature analysis of Example 1, the field of view size corresponding to the purpose of tissue feature analysis is analyzed. When the analyzed tissue features are used for the analysis of the relationship between process - tissue feature - element performance, the tissue features need to be macroscopic average information. The field of view size required to obtain macroscopic average information varies depending on the analysis accuracy required for the tissue features and the non - uniformity of the material tissue.

[0029] Fig. 6 shows a flowchart of the field of view size analysis step for analyzing the field of view size corresponding to the purpose of tissue feature analysis. First, two sample surfaces are prepared that differ in the macroscopic average information of their tissue characteristics. The difference in tissue characteristics between the first and second sample surfaces is defined as the required analytical accuracy for the tissue characteristics. For example, the top surface of the sample perpendicular to the sedimentation direction is set as the first sample surface, and the bottom surface of the sample perpendicular to the sedimentation direction is set as the second sample surface. Multiple images are taken of the first sample surface, and the first image set, obtained by binarizing the observed images, and multiple images are taken of the second sample surface, obtained by binarizing the observed images, are input (S51). The field of view size of the image set used for field of view size analysis should be set to a wide range that is feasible for experimentation. Input the field of view size to be extracted from the image set (S52). A third image set is created by cutting out the first image set, and a fourth image set is created by cutting out the second image set (S53). A first set of microstructure features analyzed using the third set of images and a second set of microstructure features analyzed using the fourth set of images are created (S54). Microstructure features may include not only normalized boundary length and packing density, but also average particle size and mode particle size. Next, we calculate the probability that a difference occurs between the mean values ​​of the first set of organizational features and the mean values ​​of the second set of organizational features (S55).

[0030] Here, we will explain in detail how to calculate the probability of a difference in means occurring. The t-test is a method for determining whether the difference in means between two groups falls within the range of random error. The t-test is used when the sample size is small. Figure 7A shows t-distributions p(t) with different numbers of observations. While the distribution is similar in shape to the standard normal distribution, the distribution broadens as the number of observations decreases, and the width of the 95% confidence interval increases. That is, even if the difference in means is large, it is judged to be within the range of random error. The difference in means T is calculated using the following formula.

[0031]

number

[0032] This formula is used when the data are not correlated and the variances of the two groups are different. Using T and p(t), we calculate P(T<=t) (Figure 7B). P(T<=t) corresponds to the probability of the mean difference T occurring. If P(T<=t) is less than or equal to the significance level of 5%, the difference in means is considered statistically significant and not due to random error.

[0033] Next, we will explain the process (S56) for estimating the field of view size at which the probability of a difference in the mean occurs is 5%. The field of view size and the P(T<=t) ratio calculated in S55 are saved and plotted on the graph shown in Figure 8. If the P(T<=t) ratio is greater than 5%, the field of view size is enlarged; if it is smaller, the field of view size is reduced and the P(T<=t) ratio is calculated (S57, S53, S54, S55). By repeating the above process, a graph (Figure 8) showing the relationship between the field of view size and the P(T<=t) ratio is created. From the graph, we estimate the field of view size at which the P(T<=t) ratio is 5%.

[0034] After estimation, the relationship between the field of view size and P(T<=t) on both sides is output (S58).

[0035] In this study, we estimated the field of view size at a significance level of 5%. However, if the difference in tissue characteristics between the first and second sample surfaces used in the field of view size analysis process is large, the probability may be set to less than 5%, and if the difference is small, the probability may be set to greater than 5%. Furthermore, if the sample size is large, a z-test may be used. We will use a method that can test the significance of the difference in the mean values ​​of tissue characteristics between the first and second sample surfaces to estimate the required field of view size.

[0036] In this embodiment, the accuracy of tissue feature analysis is specified by the difference in tissue features between the first and second sample surfaces, and the field of view size required to analyze the difference in tissue features at a significance level is estimated. By analyzing with the required field of view size, the analyzed tissue features can be treated as macroscopic average information. Furthermore, if the field of view size is too large, the turn-to-attach (TAT) of the analysis decreases. By analyzing tissue features with the required field of view size, the efficiency of tissue feature analysis can be improved. [Examples]

[0037] Example 3 is an example of a composite material structure analysis device. Figure 9 shows the system configuration of the composite material structure analysis device of Example 3. The analysis system consists of one or more user terminals 602 and a composite material structure analysis device 600.

[0038] The composite material structure analysis device 600 consists of an information processing device, such as a personal computer or general-purpose computer. The analysis device 600 includes a processor 604 such as a CPU, a memory unit 606, a user interface 608 (User I / F in the diagram), a network interface 610 (Network I / F in the diagram), and an internal network connecting these components.

[0039] The processor 604 can execute programs stored in the memory unit 606. Examples of processors include CPUs and GPUs, but other semiconductor devices may also be used as long as they are the main entities that perform predetermined processing. The memory unit 606 stores the programs to be executed by the processor 604 and various information used by these programs. In this embodiment, the memory unit 606 stores the composite material structure analysis program 612 and also stores various information in the database 618. The memory unit 606 may be, for example, a semiconductor memory, flash memory, HDD (Hard Disk Drive), SSD (Solid State Drive), etc., and may be either a volatile or non-volatile type of memory.

[0040] The analysis program 612 is a program that performs analysis of the composite material structure and consists of a structure feature analysis program 614 and a field of view size analysis program 616. The structure feature analysis program 614 is a program that analyzes structure features such as normalized boundary length using the method of Example 1. The field of view size analysis program 616 is a program that analyzes the optimal field of view size using the method of Example 2.

[0041] The user interface 608 may be, for example, a touch panel, display, keyboard, or mouse, but it may also be any other device that can accept input from the operator (user) and display information. The user interface 608 may be composed of multiple such devices.

[0042] The network interface 610 is an interface for communicating with external devices (e.g., user terminal 602, etc.) via a network.

[0043] The user terminal 602 is, for example, a computer used by a user of the analysis device 600. The user terminal 602 has a processor, memory, and an input / output interface (IF) for the user. The user terminal 602 accesses the analysis device 600, for example, and transmits input data via a menu screen. The conditions entered by the user are stored in the storage unit 606, and based on the stored data, the analysis program 612 calculates structural characteristics such as the packing density and normalized boundary length, and transmits the results to the user terminal 602. This allows the user to view the calculated results of structural characteristics such as the normalized boundary length.

[0044] Figure 10 shows a functional block diagram of the processing performed by the analysis device in Figure 9. The functional block diagram in Figure 10 is divided into a tissue feature analysis program 614 and a field of view size analysis program 616. The tissue feature analysis program 614 corresponds to the tissue feature analysis flow in Figure 1, and the field of view size analysis program 616 corresponds to the field of view size analysis flow in Figure 6 of Example 2.

[0045] As shown in Figure 10, the tissue feature analysis program 614 includes an image input unit 702, a binary image creation unit 704, a boundary image creation unit 706, a normalized boundary length measurement unit 708, and a tissue feature output unit 710. The image input unit 702 receives an observation image of a composite material consisting of a light-emitting material and a non-light-emitting material. The binary image creation unit 704 creates a binary image by segmenting the observed image into regions. The boundary image creation unit 706 creates a boundary image in which the boundary in the binary image is positive and everything else is negative. The standardized boundary length measuring unit 708 measures the total number of pixels at the boundary in the boundary image, divides it by the pixel size to convert it into the boundary length, and measures the standardized boundary length by normalizing the boundary length using the ratio of the captured area to the specified area. The tissue feature output unit 710 outputs the normalized boundary length, which is an analyzed tissue feature.

[0046] The field of view size analysis program 616 includes a field of view size analysis unit 720. The field of view size analysis unit 720 estimates the field of view size necessary to obtain macroscopic average information.

[0047] A composite material structure analysis device can be implemented in a computer by having a processor, such as a CPU, load a predetermined program into memory, and then having the processor execute the predetermined program loaded into memory. This predetermined program can be loaded into memory from a storage unit. For example, it can be loaded directly into memory from a storage medium where the program is stored via a user interface, or from a network via a network interface, or it can be stored in an external storage device first and then loaded into memory. The processor and storage unit may be configured on the cloud.

[0048] The program invention in this invention is a program that is incorporated into a computer and operates the computer as an analysis device for composite material structures. By incorporating the program of this invention into a computer, the analysis device for composite material structures shown in the block diagrams of Figures 9 and 10 is constructed.

[0049] According to this embodiment, by measuring the normalized boundary length using boundary images created from binary images, an appropriate microstructure analysis device for composite materials can be provided. Furthermore, an analysis device for the field of view size necessary to obtain macroscopic average information can be provided. This improves the accuracy and efficiency of microstructure feature analysis and accelerates research and development on improving element characteristics through microstructure control.

[0050] In the embodiments described above, a resin scintillator was used as the subject of analysis, but the present invention can also be used with other composite materials.

[0051] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. 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]

[0052] 1...Scintillator powder 2…Epoxy resin 31… Area where grains have been threshed 32… Area with polishing marks 4…Boundary between scintillator powder and resin 600…Analysis equipment 602...User terminal 604… Processor 606...Storage section 608...User Interface 610…Network Interface 612...Analysis Program 614... Organizational characteristic analysis program 616... Field of View Size Analysis Program 618…Database 702...Image input section 704...Binary Image Creation Unit 706...Boundary Image Creation Unit 708…Normalized boundary length measurement section 710...Organizational characteristics output unit 720 Field of View Size Analysis Unit

Claims

1. A method for analyzing the structure of composite materials using an information processing device, The image input process involves inputting observation images of the composite material, A binary image creation step is performed by dividing the aforementioned observation image into regions to create a binary image, A tissue feature analysis step for analyzing tissue features from the aforementioned binary image, The process includes a tissue feature output step that outputs the analyzed tissue features, The aforementioned tissue characteristic analysis step is: A boundary image creation step, which creates a boundary image in which the boundary in the aforementioned binary image is positive and the rest is negative, A normalized boundary length measurement step involves measuring the total number of pixels at the boundary in the boundary image, dividing it by the pixel size to convert it to the boundary length, and measuring the normalized boundary length obtained by normalizing the boundary length by the ratio of the captured area to the specified area. Includes, The aforementioned composite material is Gd ２ O ２ A method for analyzing the structure of a composite material, characterized by containing S.

2. In the method for analyzing the structure of a composite material according to claim 1, A method for analyzing the structure of a composite material, characterized in that the composite material absorbs radiation energy and emits light.

3. In the method for analyzing the structure of a composite material according to claim 1, further, A method for analyzing the structure of a composite material, characterized by comprising a field-of-view size analysis step for estimating the field-of-view size of the observation image necessary to analyze the difference in tissue characteristics between a first sample surface and a second sample surface at a significant level.

4. In the method for analyzing the structure of a composite material according to claim 3, The aforementioned field of view size analysis step is: The process involves inputting a first image set obtained by taking multiple images of the first sample surface and binarizing the observed images, and a second image set obtained by taking multiple images of the second sample surface and binarizing the observed images. The process involves inputting the field of view size to be extracted from the image set, The process of creating a third image set extracted from the first image set and a fourth image set extracted from the second image set, The process involves creating a first set of tissue features analyzed from a third set of images and a second set of tissue features analyzed from a fourth set of images. A process of calculating the probability that a difference occurs between the mean value of the first set of organizational features and the mean value of the second set of organizational features, The process involves estimating the field of view size required for the probability of a difference to reach a statistically significant level, A process to output the relationship between the probability of a difference occurring and the field of view size, A method for analyzing the structure of composite materials, characterized by including the following.

5. In the method for analyzing the structure of a composite material according to claim 4, A method for analyzing the structure of a composite material, characterized by using a t-test or a z-test in the step of estimating the field of view size required for the probability of the aforementioned difference occurring to reach a significant level.

6. A composite material structure analysis device, An image input unit for inputting observation images of composite materials, A binary image creation unit that divides the aforementioned observation image into regions to create a binary image, A tissue feature analysis unit analyzes tissue features from the aforementioned binary image, It includes a tissue feature output unit that outputs the analyzed tissue features, The aforementioned tissue feature analysis unit, A boundary image creation unit that creates a boundary image in which the boundary in the aforementioned binary image is positive and everything else is negative, A normalized boundary length measuring unit measures the total number of pixels at the boundary in the boundary image, divides it by the pixel size to convert it into a boundary length, and measures a normalized boundary length obtained by normalizing the boundary length by the ratio of the captured area to the specified area. Includes, The aforementioned composite material is Gd ２ O ２ An analytical device for the structure of composite materials, characterized by containing S.

7. In the composite material structure analysis apparatus according to claim 6, The composite material is characterized by absorbing radiation energy and emitting light, and is used as an analytical device for the structure of a composite material.

8. In the composite material structure analysis apparatus according to claim 6, further, A composite material structure analysis device characterized by comprising a field of view size analysis unit that estimates the field of view size of the observation image necessary to analyze the difference in tissue characteristics between a first sample surface and a second sample surface at a significant level.

9. In the composite material structure analysis apparatus according to claim 8, The aforementioned field of view size analysis unit, The system inputs a first image set obtained by binarizing observation images taken from multiple images of the first sample surface, and a second image set obtained by binarizing observation images taken from multiple images of the second sample surface. Enter the field of view size to be extracted from the image set. Create a third image set extracted from the first image set, and a fourth image set extracted from the second image set. A first set of tissue features analyzed from the third set of images and a second set of tissue features analyzed from the fourth set of images are created. We calculate the probability that a difference occurs between the mean value of the first set of organizational features and the mean value of the second set of organizational features. We estimate the field of view size required for the probability of a difference to reach a statistically significant level. Output the relationship between the probability of a difference occurring and the field of view size. A composite material structure analyzer characterized by the following features.

10. In the composite material structure analysis apparatus according to claim 9, An analytical apparatus for the structure of composite materials, characterized by using a t-test or a z-test when estimating the field of view size required for the probability of the aforementioned difference occurring to reach a significant level.

Images