KR20190086274A - Particle analysis method for composite, particle dispersion analysis method for composite and particle dispersion analysis system for composite - Google Patents

Abstract

The present invention relates to a composite particle analysis method, a composite particle dispersion analysis method, and a composite particle dispersion analysis system. According to an embodiment of the present invention, the composite particle analysis method comprises: a step of photographing a tissue image of a composite with sub-members including a particle and a defect, which are dispersed and placed; a step of extracting a gray scale information from the tissue image photographed by the step of photographing the tissue image of the composite, to automatically threshold based on the size of the gray scale value obtained from the gray scale information, and to distinguish the sub-member image from the tissue image; and a step of using the gray scale information of the boundary area and the central area of the sub-member distinguished from the tissue image to analyze the tissue image and to distinguish the particle placed in the composite. The present invention aims to provide the composite particle analysis method, the composite particle dispersion analysis method, and the composite particle dispersion analysis system, which are able to distinguish only particles from other defects, openings, and impurities in a composite.

Description

TECHNICAL FIELD The present invention relates to a composite particle analyzing method, a composite particle dispersion analyzing method, and a composite particle dispersion analyzing system,

TECHNICAL FIELD The present invention relates to a composite particle analysis method, a composite particle dispersion analysis method, and a composite particle dispersion analysis system. More particularly, the present invention relates to a composite particle dispersion analysis system, And analyzing the distribution of the same.

The composite material can be defined as a material that can artificially put a reinforcement material of the second phase on the base and obtain properties superior to the known constituent materials. Typical examples of the composite material include a metal composite material, a polymer composite material, and a ceramic composite material.

These composite materials have received technological and commercial interest beyond simple academic interest over the past centuries and have recognized that composite technology can be used in future military, , Leisure products, and other structural / mechanical and functional requirements.

Representative metal composite materials among composite materials can be designed to suit the characteristics of metal materials and reinforcements, and can be used for structural characteristics such as high strength, high toughness and hardness, and various characteristics such as oxidation resistance, high conductivity, low thermal expansion and neutron absorption It is possible to use it for the development of functional new materials. In addition, the metal composite material technology can be classified into a design technique for determining the composition type and distribution of the composite material, a process technology requiring dispersion, mixing, and interface control of the reinforcement material, and a property evaluation technique for evaluating the properties of the composite material It is important that the linkage between these element technologies is important in order to overcome the limitation of existing single materials. Especially, it is possible to improve the structural characteristics and to give new functions not possessed by base metal and reinforcement through interface control between base metal and reinforcement

Metal Matrix Composites (MMC) is a metal-based alloy and dispersed with various reinforcements. It has the advantages of excellent thermal conductivity, electrical conductivity and processability, excellent elastic modulus, strength and impact resistance, In order to maximize the advantages of the low coefficient ceramics, it is possible to freely control the properties by controlling the selection of the reinforcement material, the selection of the base metal and the volume ratio according to the required functions in order to realize the required characteristics. In recent years, attention has been focused on the reduction of manufacturing costs of metal composite materials and the development of environmentally friendly manufacturing processes, and development of materials capable of maximizing the properties of multiple materials is underway. As nanomaterials such as carbon nanotubes (CNTs) and graphenes have been developed, researchers of metal composite materials have also made efforts to develop more excellent materials by using them as reinforcements.

In order to evaluate the performance of such a composite material, particle analysis and particle dispersion analysis of the reinforcing material of the composite material are essential. For such analysis, particle analysis and dispersion analysis of the reinforcing material are performed after observing the cross section of the composite material, and it is difficult to accurately analyze it due to impurities and defects observed together with the reinforcement particles during analysis.

Korean Patent Registration No. 1656464 Japanese Patent Application Laid-Open No. 2005-169548

An object of the present invention is to provide a composite particle analysis method capable of distinguishing only particles from other defects, voids, and impurities in a composite material in which particles including particles and defects are dispersed and disposed.

The present invention also provides a composite particle dispersion analyzer capable of separating only particles from other defects, voids, and impurities in a composite material in which particles and defects are dispersed, and quantitatively analyzing the degree of dispersion thereof The present invention is directed to a method of manufacturing a semiconductor device.

The method for analyzing composite particles according to an embodiment of the present invention includes the steps of capturing a tissue image of a composite material in which particles and defects are dispersed and arranged; Extracting gray scale information from the tissue image captured at the step of photographing the tissue image of the composite material, automatically thresholding the gray scale information based on the size of the gray scale value obtained from the gray scale information, A step of dividing the image of the auxiliary member; And separating the particles disposed in the composite by analyzing the tissue image using grayscale information of a boundary region of the attachment member and brightness information of the center region separated in the tissue image.

In addition, the step of dividing the particles disposed in the composite material may include the step of dividing the particles disposed in the composite material into a plurality of particles, wherein the minimum gray scale peak value of the boundary area of the image of the attached member is within a predetermined first range, And may be classified into particles when the gray scale peak value is within a predetermined second range.

In addition, the predetermined first range and the predetermined second range are automatically determined through statistical analysis on the image of the subsidiary member.

In addition, the particles may be located in a first line passing through the particles, the first and second positions being spaced apart from each other and having a local minimum grayscale peak, and a second position located between the first position and the second position, And a third position having a maximum grayscale peak of < RTI ID = 0.0 >

Capturing a tissue image of a composite material in which composite particles dispersed in a particle and a defect including a defect are dispersed and arranged according to an embodiment of the present invention; Extracting gray scale information from the tissue image captured at the step of photographing a tissue image of the composite material, thresholding the gray scale information based on the gray scale value obtained from the gray scale information, (B) identifying the missing image; Dividing the particles disposed in the composite material by analyzing the tissue image using gray scale information of a boundary region of the additional member and brightness information of the center region separated in the tissue image; And measuring the degree of dispersion of the particles.

In addition, the step of dividing the particles disposed in the composite material may include the step of dividing the particles disposed in the composite material into a plurality of particles, wherein the minimum gray scale peak value of the boundary area of the image of the attached member is within a predetermined first range, And may be classified into particles when the gray scale peak value is within a predetermined second range.

In addition, the predetermined first range and the predetermined second range are automatically determined through statistical analysis on the image of the subsidiary member.

In addition, the particles may be located in a first line passing through the particles, the first and second positions being spaced apart from each other and having a local minimum grayscale peak, and a second position located between the first position and the second position, And a third position having a maximum grayscale peak of < RTI ID = 0.0 >

A composite particle dispersion analyzing system according to an embodiment of the present invention includes a collecting unit for acquiring a tissue image of a composite material in which particles and defects are dispersed and arranged; An analysis unit for analyzing the tissue image information of the composite material in which the particles including the particles and defects acquired in the collection unit are dispersed; And an extracting unit for extracting the tissue image information of the composite material analyzed by the analyzing unit, wherein the analyzing unit extracts the gray scale information from the tissue image of the composite material, and calculates the size of the gray scale value obtained from the gray scale information Wherein the method comprises the steps of automatically thresholding the base image to divide the image of the attached member from the image of the tissue and using the grayscale information of the boundary region and the center region of the divided member, By analyzing the tissue image, the particles disposed in the composite material can be identified, the degree of dispersion of the particles can be analyzed, and the extraction unit can extract the degree of dispersion of the particles.

The composite particle analysis method according to an embodiment of the present invention can automatically distinguish only particles from other defects, voids, and impurities in a composite material in which particles including particles and defects are dispersed and disposed, without user intervention.

Also, the composite particle dispersion analysis method according to the embodiment of the present invention automatically separates defects, voids, and impurities from only a particle in a composite material in which particles including particles and defects are dispersed, without user intervention And it is possible to quantitatively and automatically analyze the dispersion degree thereof, thereby improving the productivity of the particle dispersion analysis of the composite material.

1 shows a flowchart of a method for analyzing composite particles according to an embodiment of the present invention.
2a shows an optical image of a metal composite material.
FIG. 2B shows only particles extracted from the optical image of FIG. 2A.
FIG. 2C shows a bar distribution diagram of the closest distance between particles extracted from the optical image of FIG. 2B. FIG.
3a shows another optical image of a metal composite.
FIG. 3B shows only particles extracted from the optical image of FIG. 3A.
FIG. 3C shows a bar distribution diagram of the closest distance between particles extracted from the optical image of FIG. 3B. FIG.
FIG. 4A is an enlarged view of an area including only one particle in the optical image of FIG. 3A.
FIG. 4B shows the gray scale values along line AA 'of FIG. 4A.
FIG. 4C shows the gray scale values along line BB 'of FIG. 4A.
Figure 5a shows the grayscale values of the local minimum peaks of the boundary region of the Figure 4a particles in cumulative distribution.
Fig. 5b shows the cumulative distribution of the grayscale values of the central peak local maximum peaks of the Fig. 4a particles.
Fig. 6 shows one horizontal line of gray scale values passing horizontally in Fig. 4 according to the horizontal position.
Figure 7 shows the separation of defects except for particles and particles in the optical image.
FIG. 8A is an enlarged view of a region including a plurality of particles and defects in the optical image of FIG. 7; FIG.
FIG. 8B shows gray scale values along the line AA 'in FIG. 8A.
9 shows a flowchart of a method for analyzing a composite material dispersion according to an embodiment of the present invention.
10 is a graphical representation of a method for analyzing composite material dispersion according to an embodiment of the present invention.
Fig. 11 shows a comparison between the actual particle dispersion and the expected particle dispersion of the particles in the optical image.
12A is an optical image of a metal composite material of Example 1. Fig.
FIG. 12B shows particles extracted from the optical image of the metal composite of Example 1. FIG.
12C is an analysis of the particle dispersion degree in the optical image of the metal composite material of Example 1. Fig.
13A is an optical image of the metal composite of Example 2. Fig.
FIG. 13B shows particles extracted from the optical image of the metal composite of Example 2. FIG.
13C is an analysis of the particle dispersion degree in the optical image of the metal composite material of Example 2. Fig.
14A is an optical image of the metal composite of Example 3. Fig.
Fig. 14B shows particles extracted from the optical image of the metal composite of Example 3. Fig.
14C is an analysis of the particle dispersion degree in the optical image of the metal composite of Example 3. Fig.
15A is an optical image of the metal composite material of Example 4. Fig.
Fig. 15B shows particles extracted from the optical image of the metal composite of Example 4. Fig.
15C is an analysis of the particle dispersion degree in the optical image of the metal composite material of Example 4. Fig.
Figure 16 shows a block diagram of a variance analysis system according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. However, the embodiments of the present invention can be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. Further, the embodiments of the present invention are provided to more fully explain the present invention to those skilled in the art. Accordingly, the shapes and sizes of the elements in the drawings may be exaggerated for clarity of description, and the elements denoted by the same reference numerals in the drawings are the same elements. In the drawings, like reference numerals are used throughout the drawings. In addition, "including" an element throughout the specification does not exclude other elements unless specifically stated to the contrary.

Composite Particle Analysis Method

1 is a flow chart of a composite particle analysis method.

Referring to FIG. 1, a method for analyzing composite particles according to an embodiment of the present invention includes: capturing a tissue image of a composite material in which particles and defects are dispersed; Extracting gray scale information from the tissue image captured at the step of photographing a tissue image of the composite material, thresholding the gray scale information based on the gray scale value obtained from the gray scale information, (B) identifying the missing image; And separating the particles disposed in the composite by analyzing the tissue image using grayscale information of a boundary region of the attachment member and brightness information of the center region separated in the tissue image.

First, a step of photographing a tissue image of a composite material in which particles including defects and defects are dispersed is described.

The tissue image of the composite in which the particles and defects are dispersed and disposed is preferably a tissue image of a cross section of the composite. The attached member includes particles and bonds, which may include voids, defects and impurities of the metal matrix.

In addition, the composite material may be in the form of particles dispersed in a matrix and dispersed therein. The particle including the particle and the defect may be preferably composed of a material different from that of the base, and thus the color, brightness, saturation, and the like may be different from each other on the tissue image. The composite material may include at least one of a metal composite material, a polymer composite material, and a ceramic composite material.

The metal composite material is a metal-based alloy and dispersed with various reinforcement materials. Various physical properties such as strength, thermal conductivity, abrasion resistance and the like can be controlled by introducing various reinforcement materials such as metal oxide, ceramic material and carbon fiber.

Fiber-reinforced plastics (FRP) whose strength is improved by using carbon fiber or glass fiber reinforcement are widely known as the above-mentioned polymer composite material, which is a base material in which various reinforcing materials are dispersed based on a polymer resin. In addition, it is also possible to secure electromagnetic performance by injecting magnetic metal or metal oxide, or to secure conductivity by using carbon nanofibers / tubes (CNF / CNT).

The ceramic composite material is also the same as other composite materials in that a ceramic material is used as a base and various kinds of reinforcement materials are added. In the case of ceramic composite materials, application of ceramic fiber or carbon fiber reinforcement to improve the strength of ceramic base is the most common. In addition, various physical properties such as thermal shock resistance and fracture toughness can be improved.

The composite material may be prepared by preparing a composite material containing a matrix material and particles, and molding the composite material. The step of preparing the composite material comprising the base material and the particles may generally be prepared by evenly dispersing and mixing the matrix material and particles in powder form. The step of molding the composite material may be performed by molding a mixed powder of the matrix and the particles prepared in the preceding step and then sintering the mixture at a predetermined temperature.

Taking a Cross-sectional Image of the Composite Material [0054] In the above, a preparation step may be performed to cut the prepared composite material and then polish the cross-section of the composite material. Appropriate etching can also be performed for contrast between the matrix and the particles of the composite. The etching method may be a chemical etching method and may be performed by immersing the surface of the composite subjected to the polishing in a solution containing at least one of an acid, an alkali, a neutral solution and a molten salt in order to perform the chemical etching , The etching method is not necessarily limited to the chemical etching method.

The step of photographing a tissue image of the composite material in which the particles and the defects are dispersed may be performed by a photographing means for photographing the tissue image and the photographing means may be an optical microscope, A microscope (SEM), and a transmission electron microscope (TEM). Thus, the tissue image may be at least one of an optical image, a scanning electron microscopy image, or a transmission electron microscopy image, but is not limited thereto.

The imaging means may be connected to an electronic device such as a computer to store the tissue image taken by the imaging means in the form of a digital file and the tissue image stored in the form of a digital file is transmitted to a computer, The tissue image information can be analyzed.

Defects in the particles, including particles and defects, can include at least one of known voids and impurities. The defects may be defects inherent in the composite substrate, impurities introduced from the outside in the process of taking an image of the composite, or polishing or etching the composite or photographing the composite with an optical image Lt; / RTI >

2a shows an optical image of a metal composite material.

3a shows another optical image of a metal composite.

Referring to FIGS. 2A and 3A, it can be seen that in the optical image, a metal base and an additional member including particles and defects as compared with the metal base are observed. It is possible to discriminate the presence of particles through the optical image when the particle including the particles and defects exceeds a predetermined size, but it is difficult to clearly distinguish particles when the size is less than a predetermined size.

Next, grayscale information is extracted from the tissue image captured at the step of photographing the tissue image of the composite material, thresholding is performed based on the size of the gray scale value obtained from the gray scale information, The step of dividing the image of the attached member will be described.

The method of extracting the grayscale information from the captured tissue image in the step of photographing the tissue image of the composite material may be performed by extracting the grayscale information from the tissue image of the composite material obtained in the previous step using an image analysis program.

The gray scale indicates an image in which the value of a pixel is one sample, and can transmit only the information of luminous intensity. The gray scale value can be expressed by the white of the most luminous intensity, and the gray scale value can be expressed by the numerical value. The gray scale value may be represented by a value of 0 (or 0%) to 1 (or 100%), or may be represented by a gray scale value of 0 to 255 having 256 levels.

The tissue image of the composite can extract grayscale information of the matrix, the center and boundaries of the grains, impurities and defects. The matrix, gravity center of the composite, and grayscale information of the boundaries, impurities, and defects, respectively, may exhibit unique characteristics and thereby be distinguished from each other.

Referring to FIGS. 2A and 3A, the metal matrix of the optical image of the metal composite material may have a mirror surface after polishing due to the characteristics of the metal, and thus may have a high gray scale value. The particles, including the particles and defects, can have a low gray scale value distinct from the metal matrix.

Next, the step of distinguishing the particles disposed in the metal composite material by analyzing the tissue image using the gray-scale information of the boundary region and the center region of the attached member separated from the tissue image will be described.

FIG. 2B shows only particles extracted from the optical image of FIG. 2A.

FIG. 3B shows only particles extracted from the optical image of FIG. 3A.

Referring to FIGS. 2B and 3B, a method for analyzing a metal composite particle according to an embodiment of the present invention uses grayscale information of a boundary region of the divided member and brightness information of a center region, It can be seen that only the particles located in the metal composite can be distinguished through the step of dividing the particles disposed in the metal composite by analyzing the optical image.

FIG. 4A is an enlarged view of an area containing only one particle in the optical image of FIG. 2A or FIG. 3A.

FIG. 4B shows the gray scale values along line AA 'of FIG. 4A.

FIG. 4C shows the gray scale values along line BB 'of FIG. 4A.

Referring to FIGS. 4A and 4B, it can be seen that the gray scale value shown along the line AA 'has a high value close to 255 in the metal base as the background, and gradually the gray scale value Having a local minimum peak at a boundary between the particle and the metal matrix, having a local maximum peak at a central region of the particle after having a local minimum peak after being reduced, It is gradually shifted to the metal base side, and the value is close to 255. At this time, the local size range can be defined as a predetermined range that does not exceed the particle size.

As described above, a predetermined region having a local minimum peak at a boundary between a base and a particle can be defined as a boundary region.

As described above, a predetermined region having a local maximum peak at the boundary between the base and the particle can be defined as a central region.

Referring to FIGS. 4A and 4C, it can be seen that the gray scale value shown along the BB 'line shows a high value close to 255 in the metal base as the background, and the gradual gray scale value Having a local minimum peak at a boundary between the particle and the metal matrix, having a local maximum peak at a central region of the particle after having a local minimum peak after being reduced, It is gradually shifted to the metal base side, and the value is close to 255. At this time, the local size range can be defined as a predetermined range that does not exceed the particle size.

4B and 4C, when the grains arranged in the metal matrix are subjected to grayscale line scanning through the grains in the first direction or the second direction, Has two local minimum peaks, and has one local maximum peak located between the two local minimum peaks. This can be distinguished from impurities such as other voids, defects and the like due to the unique characteristics of the particles disposed in the metal matrix.

Figure 5a shows the grayscale values of the local minimum peaks of the boundary region of the Figure 4a particles in cumulative distribution.

Referring to FIG. 5A, the grayscale value of the borderline local minimum peak has a distribution of values of 65 to 95, and when it is approximated by a normal distribution, the 99% confidence interval is a value interval of 63 to 98. Therefore, when the value of the local minimum peak is included in the corresponding range (first range), it can be determined that the local minimum peak corresponds to the peak of the boundary region of the particle, that is, 99%.

Fig. 5b shows the cumulative distribution of the grayscale values of the central peak local maximum peaks of the Fig. 4a particles.

Referring to FIG. 5B, the grayscale value of the central region local maximum peak has a distribution of values of 80 to 130, and when it is approximated by a normal distribution, the 99% confidence interval is a value interval of 73 to 158. Therefore, when the value of the local maximum peak is included in the corresponding range (second range), it can be determined that the local maximum peak corresponds to the peak area of the central region of the particle, and that the reliability is 99%.

Wherein the step of dividing the particles disposed in the composite material comprises the step of dividing the grayscale value of the boundary region of the image of the attached member into a predetermined first range, Is included in the predetermined second range, it can be classified into particles.

The predetermined first range may vary depending on the material combination of the matrix and the particle and the measurement conditions, but may have a gray scale range of 63 to 98. [

The predetermined second range may vary depending on the material of the particles and the measurement conditions, but may have a grayscale range of 73 to 158. [

The first range and the predetermined second range may be automatically determined through statistical analysis on the image of the additional member.

Referring to Figures 4B and 4C, the particles are in a first and a second position with a local minimum grayscale peak located on a line passing through the particles, spaced from each other and falling within a first range, And a third position located between the first and second positions and having a local maximum gray scale peak included in the second range.

On the other hand, voids, defects, and impurities except for the particles are positioned on a straight line as voids, defects, and impurities as described above, 1 < / RTI > range having a local minimum grayscale peak, and a second location having a local maximum gray scale peak located between the first and second locations, Third position, which is a contrasting property of the particles with voids, defects and impurities.

Fig. 6 shows one horizontal line of gray scale values passing horizontally in Fig. 4 according to the horizontal position.

Referring to FIG. 6, there is shown a lithographic apparatus including a first position and a second position spaced from each other and having a local minimum gray scale peak included in a first range (red), and a second position located between the first position and the second position, And a third position having a local maximum gray scale peak included in the range (blue) is located in the vicinity of the horizontal position 320 and the horizontal position 630 as indicated by yellow, and the area is divided into the center area However, it is not limited to the range of the grayscale value of the secondary material including the particles and defects, and may vary depending on the measurement conditions and the type of the base material.

Figure 7 shows the separation of defects except for particles and particles in the optical image.

Referring to FIG. 7, as described above, the particles disposed in the matrix are arranged between the two local minimum peaks included in the first range and the local minimum peaks included in the first range when subjected to gray scale converted line scanning through the particles. The defect and pore may have a local maximum peak included within a second range located within a second range located within the first range, while the defect and pore may be localized within a second range located between the two local minimum peaks, It can not have the maximum peak. Thus, defects and voids that do not have the grayscale value characteristic of the particles disposed on the metal matrix through the conversion process can be excluded. Also, it can be seen that voids, defects, and impurities of the metal composite material can be easily removed even when particles and defects are overlapped and difficult to identify.

FIG. 8A is an enlarged view of a region including a plurality of particles and defects in the optical image of FIG. 7; FIG.

FIG. 8B shows gray scale values along the line AA 'in FIG. 8A.

Referring to Figures 8a and 8b, when performing grayscale line scanning through two particles disposed in the metal base, each of the particles has two local minima Peak and has one local maximum peak located between the two local minimum peaks. This can be distinguished from impurities such as other voids, defects and the like due to the unique characteristics of the particles disposed in the metal matrix. On the other hand, it can be seen that the defect located between the two particles has a gray scale value within a predetermined range without large fluctuation of the peak.

Analysis method of particle dispersion of metal composite

9 is a flowchart of a method of analyzing a dispersion of metal composite material according to an embodiment of the present invention.

10 is a graphical representation of a method for analyzing the dispersion of a metal composite material according to an embodiment of the present invention.

9 and 10, the steps of photographing an optical image of a metal composite material in which a particle including a particle and a defect according to an embodiment of the present invention are dispersed and disposed; Extracting gray scale information from the optical image photographed at the step of photographing the optical image of the metal composite material and automatically thresholding the gray scale information based on the size of the gray scale value obtained from the gray scale information, Dividing the image of the attached member; Separating the particles disposed in the metal composite by analyzing the optical image using gray scale information of a boundary region of the additional member and brightness information of the center region separated in the optical image; And measuring the degree of dispersion of the particles.

First, a step of photographing a tissue image of a composite material in which particles including defects and defects are dispersed is described.

The tissue image of the composite in which the particles and defects are dispersed and disposed is preferably a tissue image of a cross section of the composite. The attached member includes particles and bonds, which may include voids, defects and impurities of the metal matrix.

In addition, the composite material may be in the form of particles dispersed in a matrix and dispersed therein. The particle including the particle and the defect may be preferably composed of a material different from that of the base, and thus the color, brightness, saturation, and the like may be different from each other on the tissue image. The composite material may include at least one of a metal composite material, a polymer composite material, and a ceramic composite material, but is not limited thereto.

The metal composite material is a metal-based alloy and dispersed with various reinforcement materials. Various physical properties such as strength, thermal conductivity, abrasion resistance and the like can be controlled by introducing various reinforcement materials such as metal oxide, ceramic material and carbon fiber.

Fiber-reinforced plastics (FRP) whose strength is improved by using carbon fiber or glass fiber reinforcement are widely known as the above-mentioned polymer composite material, which is a base material in which various reinforcing materials are dispersed based on a polymer resin. In addition, it is also possible to secure electromagnetic performance by injecting magnetic metal or metal oxide, or to secure conductivity by using carbon nanofibers / tubes (CNF / CNT).

The ceramic composite material is also the same as other composite materials in that a ceramic material is used as a base and various kinds of reinforcement materials are added. In the case of ceramic composite materials, application of ceramic fiber or carbon fiber reinforcement to improve the strength of ceramic base is the most common. In addition, various physical properties such as thermal shock resistance and fracture toughness can be improved.

The composite material may be prepared by preparing a composite material containing a matrix material and particles, and molding the composite material. The step of preparing the composite material comprising the base material and the particles may generally be prepared by evenly dispersing and mixing the matrix material and particles in powder form. The step of molding the composite material may be performed by molding a mixed powder of the matrix and the particles prepared in the preceding step and then sintering the mixture at a predetermined temperature.

Taking a Cross-sectional Image of the Composite Material [0054] In the above, a preparation step may be performed to cut the prepared composite material and then polish the cross-section of the composite material. Appropriate etching can also be performed for contrast between the matrix and the particles of the composite. The etching method may be a chemical etching method and may be performed by immersing the surface of the composite subjected to the polishing in a solution containing at least one of an acid, an alkali, a neutral solution and a molten salt in order to perform the chemical etching , The etching method is not necessarily limited to the chemical etching method.

The step of photographing a tissue image of the composite material in which the particles and the defects are dispersed may be performed by a photographing means for photographing the tissue image and the photographing means may be an optical microscope, A microscope (SEM), and a transmission electron microscope (TEM). Thus, the tissue image may be at least one of an optical image, a scanning electron microscopy image, or a transmission electron microscopy image, but is not limited thereto.

The imaging means may be connected to an electronic device such as a computer to store the tissue image taken by the imaging means in the form of a digital file and the tissue image stored in the form of a digital file is transmitted to a computer, The tissue image information can be analyzed.

Defects in the particles, including particles and defects, can include at least one of known voids and impurities. The defects may be defects inherent in the composite substrate, impurities introduced from the outside in the process of taking an image of the composite, or polishing or etching the composite or photographing the composite with an optical image Lt; / RTI >

2a shows an optical image of a metal composite material.

3a shows another optical image of a metal composite.

Referring to FIGS. 2A and 3A, it can be seen that in the optical image, a metal base and an additional member including particles and defects as compared with the metal base are observed. It is possible to discriminate the presence of particles through the optical image when the particle including the particles and defects exceeds a predetermined size, but it is difficult to clearly distinguish particles when the size is less than a predetermined size.

Next, grayscale information is extracted from the tissue image captured at the step of photographing the tissue image of the composite material, thresholding is performed based on the size of the gray scale value obtained from the gray scale information, The step of dividing the image of the attached member will be described.

The method of extracting the grayscale information from the captured tissue image in the step of photographing the tissue image of the composite material may be performed by extracting the grayscale information from the tissue image of the composite material obtained in the previous step using an image analysis program.

The gray scale indicates an image in which the value of a pixel is one sample, and can transmit only the information of luminous intensity. The gray scale value can be expressed by the white of the most luminous intensity, and the gray scale value can be expressed by the numerical value. The gray scale value may be represented by a value of 0 (or 0%) to 1 (or 100%), or may be represented by a gray scale value of 0 to 255 having 256 levels.

The tissue image of the composite can extract grayscale information of the matrix, the center and boundaries of the grains, impurities and defects. The matrix, gravity center of the composite, and grayscale information of the boundaries, impurities, and defects, respectively, may exhibit unique characteristics and thereby be distinguished from each other.

Referring to FIGS. 2A and 3A, the metal matrix of the optical image of the metal composite material may have a mirror surface after polishing due to the characteristics of the metal, and thus may have a high gray scale value. The particles, including the particles and defects, can have a low gray scale value distinct from the metal matrix.

Next, the step of distinguishing the particles disposed in the metal composite material by analyzing the tissue image using the gray-scale information of the boundary region and the center region of the attached member separated from the tissue image will be described.

FIG. 2B shows only particles extracted from the optical image of FIG. 2A.

FIG. 3B shows only particles extracted from the optical image of FIG. 3A.

Referring to FIGS. 2B and 3B, the method for analyzing the dispersion of metal composite particles according to an embodiment of the present invention includes the step of dividing the gray-scale information of the boundary region of the divided member and the brightness information of the center region, It is possible to distinguish only the particles located in the metal composite material through the step of dividing the particles disposed in the metal composite material by analyzing the optical image using the optical image.

FIG. 4A is an enlarged view of an area containing only one particle in the optical image of FIG. 2A or FIG. 3A.

FIG. 4B shows the gray scale values along line AA 'of FIG. 4A.

FIG. 4C shows the gray scale values along line BB 'of FIG. 4A.

Referring to FIGS. 4A and 4B, it can be seen that the gray scale value shown along the line AA 'has a high value close to 255 in the metal base as the background, and gradually the gray scale value Having a local minimum peak at a boundary between the particle and the metal matrix, having a local maximum peak at a central region of the particle after having a local minimum peak after being reduced, It is gradually shifted to the metal base side, and the value is close to 255. At this time, the local size range can be defined as a predetermined range that does not exceed the particle size.

As described above, a predetermined region having a local minimum peak at a boundary between a base and a particle can be defined as a boundary region.

As described above, a predetermined region having a local maximum peak at the boundary between the base and the particle can be defined as a central region.

Referring to FIGS. 4A and 4C, it can be seen that the gray scale value shown along the BB 'line shows a high value close to 255 in the metal base as the background, and the gradual gray scale value Having a local minimum peak at a boundary between the particle and the metal matrix, having a local maximum peak at a central region of the particle after having a local minimum peak after being reduced, It is gradually shifted to the metal base side, and the value is close to 255. At this time, the local size range can be defined as a predetermined range that does not exceed the particle size.

4B and 4C, when the grains arranged in the metal matrix are subjected to grayscale line scanning through the grains in the first direction or the second direction, Has two local minimum peaks, and has one local maximum peak located between the two local minimum peaks. This can be distinguished from impurities such as other voids, defects and the like due to the unique characteristics of the particles disposed in the metal matrix.

Figure 5a shows the grayscale values of the local minimum peaks of the boundary region of the Figure 4a particles in cumulative distribution.

Referring to FIG. 5A, the grayscale value of the borderline local minimum peak has a distribution of values of 65 to 95, and when it is approximated by a normal distribution, the 99% confidence interval is a value interval of 63 to 98. Therefore, when the value of the local minimum peak is included in the corresponding range (first range), it can be determined that the local minimum peak corresponds to the peak of the boundary region of the particle, that is, 99%.

Fig. 5b shows the cumulative distribution of the grayscale values of the central peak local maximum peaks of the Fig. 4a particles.

Referring to FIG. 5B, the grayscale value of the central region local maximum peak has a distribution of values of 80 to 130, and when it is approximated by a normal distribution, the 99% confidence interval is a value interval of 73 to 158. Therefore, when the value of the local maximum peak is included in the corresponding range (second range), it can be determined that the local maximum peak corresponds to the peak area of the central region of the particle, and that the reliability is 99%.

Wherein the step of dividing the particles disposed in the composite material comprises the step of dividing the grayscale value of the boundary region of the image of the attached member into a predetermined first range, Is included in the predetermined second range, it can be classified into particles.

The predetermined first range may vary depending on the material combination of the matrix and the particle and the measurement conditions, but may have a gray scale range of 63 to 98. [

The predetermined second range may vary depending on the material of the particles and the measurement conditions, but may have a grayscale range of 73 to 158. [

The first range and the predetermined second range may be automatically determined through statistical analysis on the image of the additional member.

Referring to Figures 4B and 4C, the particles are in a first and a second position with a local minimum grayscale peak located on a line passing through the particles, spaced from each other and falling within a first range, And a third position located between the first and second positions and having a local maximum gray scale peak included in the second range.

On the other hand, voids, defects, and impurities except for the particles are positioned on a straight line as voids, defects, and impurities as described above, 1 < / RTI > range having a local minimum grayscale peak, and a second location having a local maximum gray scale peak located between the first and second locations, Third position, which is a contrasting property of the particles with voids, defects and impurities.

Fig. 6 shows one horizontal line of gray scale values passing horizontally in Fig. 4 according to the horizontal position.

Referring to FIG. 6, there is shown a lithographic apparatus including a first position and a second position spaced from each other and having a local minimum gray scale peak included in a first range (red), and a second position located between the first position and the second position, And a third position having a local maximum gray scale peak included in the range (blue) is located in the vicinity of the horizontal position 320 and the horizontal position 630 as indicated by yellow, and the area is divided into the center area However, it is not limited to the range of the grayscale value of the secondary material including the particles and defects, and may vary depending on the measurement conditions and the type of the base material.

Figure 7 shows the separation of defects except for particles and particles in the optical image.

Referring to FIG. 7, as described above, the particles disposed in the matrix are arranged between the two local minimum peaks included in the first range and the local minimum peaks included in the first range when subjected to gray scale converted line scanning through the particles. The defect and pore may have a local maximum peak included within a second range located within a second range located within the first range, while the defect and pore may be localized within a second range located between the two local minimum peaks, It can not have the maximum peak. Thus, defects and voids that do not have the grayscale value characteristic of the particles disposed on the metal matrix through the conversion process can be excluded. Also, it can be seen that voids, defects, and impurities of the metal composite material can be easily removed even when particles and defects are overlapped and difficult to identify.

FIG. 8A is an enlarged view of a region including a plurality of particles and defects in the optical image of FIG. 7; FIG.

FIG. 8B shows gray scale values along the line AA 'in FIG. 8A.

Referring to Figures 8a and 8b, when performing grayscale line scanning through two particles disposed in the metal base, each of the particles has two local minima Peak and has one local maximum peak located between the two local minimum peaks. This can be distinguished from impurities such as other voids, defects and the like due to the unique characteristics of the particles disposed in the metal matrix. On the other hand, it can be seen that the defect located between the two particles has a gray scale value within a predetermined range without large fluctuation of the peak.

Next, the step of measuring the degree of dispersion of the particles will be described.

The expected particle dispersion of the particles dispersed in the matrix generally follows the Weibull distribution of the following equation (1). The expected particle dispersion can be evaluated by comparing the Shape parameter and the Scale parameter or the mode, which determine the actual particle dispersion and the beam distribution, as well as the quantitative dispersion.

&Quot; (1) "

k: Shape parameter, λ: Scale parameter,

The expected particle dispersion can be calculated using the amount of particles injected into the composite and the known amount.

The expected particle dispersion of the particles can be calculated through the following procedure.

First, when there are N particles in the region A, N can be defined as shown in Equation (2).

&Quot; (2) "

N: number of particles, A: area of the region, n: per unit area, particle density, _v n: particle density per unit volume, F (y): The particle size distribution function, d _0: the average particle size

Next, when there are N particles in the area A, the probability that the distance from one particle to the closest particle is r can be calculated as shown in equation (3).

&Quot; (3) "

At this time, if the number of particles is sufficiently large (N → ∞), P (r) can be approximated as shown in Equation (4).

&Quot; (4) "

The method for analyzing composite particle dispersion according to an embodiment of the present invention includes analyzing the tissue image using grayscale information of a boundary region of the attached member and brightness information of the center region, In the step of dividing the particles placed in the composite material, the measurement of the actual particle dispersion can be performed by measuring the nearest distance between the separated particles in the composite material after the execution of the preceding step and analyzing the cumulative distribution thereof. Measurement of the closest neighbor distance of the particle can be performed by a tissue image analysis program.

The shape parameter and the scale parameter of the bebble distribution can be extracted by analyzing the actual particle dispersion degree extracted in the step of measuring the degree of dispersion of the particles.

2C shows a bar distribution diagram of the closest distance of particles extracted from the optical image of FIG. 2B.

FIG. 3C shows a bar distribution diagram of the closest distance between particles extracted from the optical image of FIG. 3B. FIG.

Referring to FIGS. 2C and 3C, a composite particle dispersion analysis method according to an embodiment of the present invention can quantitatively analyze the closest distance between particles, and can be shown as a stacked bar distribution diagram that can be easily confirmed .

Fig. 11 shows a comparison between the actual particle dispersion and the expected particle dispersion of the particles in the optical image.

Referring to FIG. 11, it can be seen that the nearest neighbor distance is smaller than the expected particle dispersion degree, and that the particles of the metal composite material are close to the expected particle dispersion degree.

Composite Particle Dispersion Analysis System

Figure 16 shows a block diagram of a variance analysis system according to an embodiment of the present invention.

16, a composite particle dispersion analyzing system 1000 according to an embodiment of the present invention includes a collecting unit 1100 that acquires a tissue image of a composite material in which particles and defects are dispersed and disposed, ); An analysis unit 1200 for analyzing the tissue image information of the composite material in which the particles and defects acquired from the collection unit are dispersed and arranged; And an extracting unit (1300) for extracting tissue image information of the composite material analyzed by the analyzing unit, wherein the analyzing unit extracts the gray scale information from the tissue image of the composite material, and converts the gray scale value obtained from the gray scale information Wherein the threshold value is determined by automatically thresholding based on the size of the boundary image and the size of the boundary image and the grayscale information of the boundary region and the center region of the divided member separated from the tissue image To analyze the tissue image, to classify the particles disposed in the metal composite, to analyze the degree of dispersion of the particles, and to extract the degree of dispersion of the particles.

The collecting unit 1100 can acquire a tissue image of a composite material in which particles including particles and defects are dispersed.

The collecting unit 1100 may include at least one of an optical microscope, a scanning electron microscope (SEM), and a transmission electron microscope (TEM), wherein the collecting unit 1100 includes photographing means capable of photographing a tissue image of the composite material. have. Thus, the tissue image may be at least one of an optical image, a scanning electron microscopy image, or a transmission electron microscopy image, but is not limited thereto.

The composite material may include at least one of a metal composite material, a polymer composite material, and a ceramic composite material.

The metal composite material is a metal-based alloy and dispersed with various reinforcement materials. Various physical properties such as strength, thermal conductivity, abrasion resistance and the like can be controlled by introducing various reinforcement materials such as metal oxide, ceramic material and carbon fiber.

Fiber-reinforced plastics (FRP) whose strength is improved by using carbon fiber or glass fiber reinforcement are widely known as the above-mentioned polymer composite material, which is a base material in which various reinforcing materials are dispersed based on a polymer resin. In addition, it is also possible to secure electromagnetic performance by injecting magnetic metal or metal oxide, or to secure conductivity by using carbon nanofibers / tubes (CNF / CNT).

The ceramic composite material is also the same as other composite materials in that a ceramic material is used as a base and various kinds of reinforcement materials are added. In the case of ceramic composite materials, application of ceramic fiber or carbon fiber reinforcement to improve the strength of ceramic base is the most common. In addition, various physical properties such as thermal shock resistance and fracture toughness can be improved.

The analyzer 1200 analyzes the tissue image information of the composite material in which the particles including the particles and defects acquired in the collecting unit are dispersed and arranged, extracts the gray scale information from the tissue image of the composite material , Automatically thresholding based on the size of the gray scale value obtained from the gray scale information to distinguish the image of the subsidiary member from the image of the tissue, The grayscale information of the boundary region and the center region of the metal composite material can be used to analyze the tissue image to separate particles disposed in the metal composite material and to analyze the degree of dispersion of the particles.

The extraction unit 1300 may extract the tissue image information of the composite material analyzed by the analysis unit and extract the degree of dispersion of the particles. Also, the extracting unit can extract the average particle size of the particles, the area or the volume fraction of the particles to the composite material, and the dispersity of the particles through the tissue image information of the composite material analyzed by the analysis unit.

Example

Example 1

The composites were prepared by the following process.

In the case of the molten metal stirring process, the aluminum (Al) alloy was dissolved at 750 ° C. through induction heating, and the melting temperature was confirmed through a thermocouple. After dissolving the aluminum (Al) alloy, 5 to 15 vol% of B ₄ C particles having an average particle size of about 40 μm were charged at an agitation speed of 800 rpm using an impeller, and stirred for 10 minutes.

Next, the mixed molten metal was charged into a square mold mold having a width and a thickness of 10 mm and a length of 20 mm and cooled to prepare a metal composite material.

Next, the prepared metal composite material was cut into sections using a cutting device, the specimen was inserted into a polymer resin and cured to prepare a polishing sample. The polishing sample was polished using a polishing apparatus to obtain an optical image Specimens for observation were prepared.

Next, the polished specimen was photographed at an x50 magnification using an optical microscope and transferred to an optical image analysis program connected to the optical microscope. The optical image was analyzed by analyzing the metal composite particle analysis method and the metal composite particle dispersion analysis method according to the embodiment of the present invention, and the degree of dispersion was analyzed.

Example 2

The specimen prepared in Example 1 was cut using a cutting apparatus to prepare a rectangular specimen having a thickness of 15 mm and subjected to hot rolling. The cut specimens were preheated at 600 ° C. for 30 minutes and then rolled at a rolling speed of 10 MPM (meter per min.) And a reduction rate of about 10%. This was followed by a metal composite particle analysis method according to the embodiment of the present invention, The particles were analyzed and the degree of dispersion was analyzed by the dispersion method.

Example 3

A metal composite specimen was prepared in the same manner as in Example 1, except that the specimen was photographed at an x100 magnification using an optical microscope in Example 1. This was compared with the method of analyzing a metal composite particle according to the embodiment of the present invention, The particles were analyzed and the degree of dispersion was analyzed by the dispersion method.

Example 4

A metal composite specimen was prepared in the same manner as in Example 3 except that the specimen was photographed at an x100 magnification using an optical microscope in Example 3. This was compared with the method of analyzing a metal composite particle according to the embodiment of the present invention, The particles were analyzed and the degree of dispersion was analyzed by the dispersion method.

12A is a metallic composite optical image prepared by Example 1. Fig.

Referring to FIG. 12A, it can be seen that particles of a predetermined size or larger can be distinguished from particles through an optical image picked up by the metal composite optical image prepared by Example 1, It can be seen that it is difficult to distinguish between particles and defects and voids when a large number of particles are aggregated.

FIG. 12B shows particles extracted from the metal composite optical image prepared by Example 1. FIG.

Referring to FIG. 12B, the method for analyzing metal composite particles according to the embodiment of the present invention includes extracting particles only from fine particles or voids, which are difficult to identify in FIG. 12A, or impurities or voids, can do.

12C is an analysis of the particle dispersion degree in the optical image of the metal composite material of Example 1. Fig.

Referring to FIG. 12C, the shape parameter of the expected dispersion degree of the metal composite material of Example 1 estimated by the bebble distribution is 2 and the scale parameter is 32.4416, and the dispersion of the metal composite particles according to the embodiment of the present invention The actual particle dispersity of the metal composite specimen of Example 1 measured by the analytical method was 1.5512 and the scale parameter was 28.3257. The actual particle dispersion was 77.6% and the actual particle dispersion was 87.3% compared to the expected particle dispersion degree. Therefore, it can be seen that the actual particles are closer to each other than the expected particle dispersion.

13A is a metal composite optical image prepared by Example 2. Fig.

Referring to FIG. 13A, it can be seen that particles of a predetermined size or larger can be distinguished from particles through an optical image picked up by the metal composite optical image prepared by Example 2, It can be seen that it is difficult to distinguish between particles and defects and voids when a large number of particles are aggregated.

Fig. 13B shows particles extracted from the optical image of the metal composite prepared in Example 2. Fig.

Referring to FIG. 13B, the method of analyzing metal composite particles according to the embodiment of the present invention is a method of analyzing a metal composite particle according to an embodiment of the present invention, in which fine impurities or voids, or impurities or voids, can do.

13C is an analysis of the particle dispersion degree in the optical image of the metal composite prepared in Example 2. Fig.

13C, the shape parameter of the expected dispersion degree of the metal composite material of Example 2 estimated through the beige distribution is 2 and the scale parameter is 38.9490, and the dispersion of the metal composite particles according to the embodiment of the present invention The actual particle dispersity of the metal composite specimen of Example 2 measured by the analytical method had a shape parameter of 1.4246 and a scale parameter of 24.6050. The actual particle dispersion was 71.2% and the actual particle dispersion degree was 63.2%. Therefore, it can be understood that the actual particles are dispersed more densely in comparison with the expected particle dispersion, and it can be confirmed that the density of particles is increased according to the rolling process as compared with the result of Example 1.

14A is a metallic composite optical image prepared by Example 3. Fig.

Referring to FIG. 14A, it can be seen that particles of a predetermined size or larger can be distinguished from particles through an optical image picked up by the metal composite optical image prepared by Example 3, It can be seen that it is difficult to distinguish between particles and defects and voids when a large number of particles are aggregated.

Fig. 14B shows particles extracted from the metal composite optical image prepared according to Example 3. Fig.

Referring to FIG. 14B, the method for analyzing metal composite particles according to an embodiment of the present invention includes separating particles or fine particles, which are difficult to distinguish from fine particles or voids or particles, can do.

Fig. 14C is an analysis of the particle dispersion degree in the optical image of the metal composite prepared in Example 3. Fig.

14C, the shape parameter of the expected dispersion degree of the metal composite material of Example 3 estimated through the beige distribution is 2 and the scale parameter is 17.8483, and the dispersion of the metal composite particles according to the embodiment of the present invention The shape parameter of the actual particle dispersity of the metal composite specimen of Example 3 measured by the analytical method was 1.4555 and the parameter of the scale was 14.1027. The actual particle dispersion was 72.3% and the actual particle dispersion was 79.0% as compared with the expected particle dispersion degree. Therefore, it can be seen that the actual particles are closer to each other than the expected particle dispersion.

15A is a metallic composite optical image prepared by Example 4. Fig.

Referring to FIG. 15A, it can be seen that particles of a predetermined size or larger can be distinguished from particles through an optical image picked up by the metal composite optical image prepared by Example 4, It can be seen that it is difficult to distinguish between particles and defects and voids when a large number of particles are aggregated.

15B shows particles extracted from the metal composite optical image prepared by Example 4. Fig.

Referring to FIG. 15B, the method for analyzing metal composite particles according to the embodiment of the present invention includes extracting particles only from fine particles or voids, which are difficult to identify in FIG. 15A, or impurities or voids, can do.

15C is an analysis of the particle dispersion degree in the optical image of the metal composite prepared by Example 4. Fig.

15C, the shape parameter of the expected dispersion degree of the metal composite material of Example 3 estimated through the beige distribution is 2 and the scale parameter is 26.4231, and the dispersion of the metal composite particles according to the embodiment of the present invention The actual particle dispersity of the metal composite specimen of Example 3 measured by the analytical method was 1.1971 and the scale parameter was 16.4397. The actual particle dispersion was 59.9% and the actual particle dispersion was 62.2% compared to the expected particle dispersion degree. Therefore, it can be seen that the actual particles are dispersed more densely in comparison with the expected particle dispersion, and it can be confirmed that the density of particles is increased according to the rolling process as compared with the result of Example 3.

The present invention is not limited to the above-described embodiment and the accompanying drawings, but is intended to be limited by the appended claims. It will be apparent to those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. something to do.

1100: Composite Particle Dispersion Analysis System
1100: Collecting unit
1200: Analytical Department
1300:

Claims (10)

Taking a tissue image of a composite material in which particles including particles and defects are dispersed;
Extracting gray scale information from the tissue image captured at the step of photographing the tissue image of the composite material, automatically thresholding the gray scale information based on the size of the gray scale value obtained from the gray scale information, A step of dividing the image of the auxiliary member; And
And separating the particles disposed in the composite by analyzing the tissue image using grayscale information of the boundary region and the center region of the attached member separated from the tissue image.
The method according to claim 1,
Wherein the step of dividing the particles disposed in the composite comprises:
When the minimum gray scale peak value of the boundary area of the image of the attached member is within a predetermined first range and the maximum gray scale peak value of the center area of the attached member image is within a predetermined second range A method for analyzing composite particles classified as particles.
3. The method of claim 2,
Wherein the predetermined first range and the predetermined second range are automatically determined through statistical analysis on a tissue image including a known component including a particle and a defect and a defect.
The method according to claim 1,
Wherein the composite material comprises at least one of a metal composite material, a polymer composite material, and a ceramic composite material.
The method according to claim 1,
Said particles being located on a straight line passing through the particles, said first and second positions being spaced apart from each other and having a local minimum gray scale peak, and a second peak position located between said first and second positions, And a third position having a grayscale peak of the composite particle.
Taking a tissue image of a composite material in which particles including particles and defects are dispersed;
Extracting gray scale information from the tissue image captured at the step of photographing the tissue image of the composite material, automatically thresholding the gray scale information based on the size of the gray scale value obtained from the gray scale information, A step of dividing the image of the auxiliary member;
Dividing the particles disposed in the composite by analyzing the tissue image using grayscale information of a boundary region and a center region of the attached member separated from the tissue image; And
And measuring the degree of dispersion of the particles.
6. The method of claim 5,
Wherein the step of dividing the particles disposed in the composite comprises:
When the minimum gray scale peak value of the boundary area of the image of the attached member is within a predetermined first range and the maximum gray scale peak value of the center area of the attached member image is within a predetermined second range A method for analyzing the dispersion of composite particles classified into particles.
The method according to claim 6,
Wherein the predetermined first range and the predetermined second range are automatically determined through statistical analysis on a tissue image including a base, particles, and defects.
6. The method of claim 5,
Said particles being located on a straight line passing through the particles, said first and second positions being spaced apart from each other and having a local minimum gray scale peak, and a second peak position located between said first and second positions, And a third position having a grayscale peak of the composite particle.
A collector for acquiring a tissue image of a composite material in which particles including particles and defects are dispersed and arranged;
An analysis unit for analyzing the tissue image information of the composite material in which the particles including the particles and defects acquired in the collection unit are dispersed; And
And an extracting unit for extracting tissue image information of the composite material analyzed by the analyzing unit,
Wherein the analyzing unit extracts gray scale information from the tissue image of the composite material and automatically thresholds the gray scale information based on the gray scale value obtained from the gray scale information, And separating the particles disposed in the composite material by analyzing the tissue image using grayscale information of a boundary region and a center region of the auxiliary member divided in the tissue image, However,
Wherein the extraction unit is capable of extracting the degree of dispersion of the particles.

Images