WO2018159689A1

WO2018159689A1 - Method for observing inner structure of ceramic, method for producing ceramic, analysis system, and system for producing ceramic

Info

Publication number: WO2018159689A1
Application number: PCT/JP2018/007541
Authority: WO
Inventors: 拓実 ▲高▼橋; 多々見　純一
Original assignee: 地方独立行政法人神奈川県立産業技術総合研究所; 国立大学法人横浜国立大学
Priority date: 2017-02-28
Filing date: 2018-02-28
Publication date: 2018-09-07
Also published as: JPWO2018159689A1; JP7153275B2

Abstract

This method for observing the inner structure of ceramic using optical coherence tomography includes: a step for dividing light in the infrared region into a reference light and an irradiation light; a step for irradiating the ceramic with the irradiation light; and a step for observing the inner structure of the ceramic by observing the coherency between the reflected reference light and returning light obtained by irradiating the ceramic with the irradiation light.

Description

Ceramic internal structure observation method, ceramic manufacturing method, analysis system, and ceramic manufacturing system

The present invention relates to a ceramic internal structure observation method, a ceramic manufacturing method, an analysis system, and a ceramic manufacturing system.
This application claims priority based on Japanese Patent Application No. 2017-037446 filed in Japan on February 28, 2017, the contents of which are incorporated herein by reference.

It is known that the internal structure of ceramics changes greatly during the process from raw material powder to slurry, compact, and sintered body. Conventionally, a method using an optical microscope (for example, see Non-Patent Document 1), a method using an X-ray CT (for example, see Non-Patent Document 2), and the like have been used for observing the internal structure of ceramics.

It is known that the characteristics of ceramics are governed by the ceramic structure. Therefore, if the formation process of the structure in the ceramics process chain from the raw material to the final product ceramics can be accurately grasped and the structure can be controlled, ceramics with excellent functions and high reliability can be manufactured. . In addition, relying on visual observation or craftsmanship if the structure of slurry, molded body, sintered body, etc. that change every moment during the manufacturing process can be evaluated in real time by utilizing such observation method of structure formation process. However, it is possible to detect a factor that forms a non-uniform structure in a relatively preceding process and remove it.

Furthermore, if 100% inspection of the internal structure of the final product can be performed at high speed, high resolution, and a wide range at a low cost, the reliability of the product can be improved and the cost required for the inspection can be reduced.
As described above, it is extremely important to dynamically and three-dimensionally observe and scientifically understand the structure formation process in the ceramic manufacturing process in order to improve the yield and reliability of ceramics.

On the other hand, each unit operation in the ceramic manufacturing process has many control factors. For example, in the dispersion of ceramic fine particles, the type and amount of the dispersing agent correspond to the control factor. Dispersion of ceramic fine particles in a dispersion medium is a complicated phenomenon involving adsorption of the dispersant to the ceramic fine particles, wettability of the dispersion medium with respect to the ceramic fine particles, and the like. Therefore, apparent optimization based on intuition and experience has been performed on the control factors for preparing the slurry.

In addition, phenomena related to the interface between the particles and the liquid phase, such as the affinity with the solvent and the adsorption behavior of the dispersant, should be different for each ceramic fine particle. Therefore, in consideration of the subsequent molding process, it is necessary to optimize the control factors in consideration of the viscosity of the slurry, the solid content in the slurry, and the organic substances (binder, plasticizer, lubricant, etc.) contained in the slurry. .

Further, when the sheet-like molded body is dried, the structure should be changed dynamically from a structure in which ceramic fine particles are dispersed in a liquid to a structure in which solids are in contact with each other. This change is similar to aggregation. If the molded body is dried and the changes in the internal structure of the molded body are accurately grasped and the control factors for the formation of the structure are scientifically elucidated, the drying temperature for obtaining a homogeneous molded body free of cracks and deformation, It is believed that time and atmosphere can be determined more analytically.
Further, even in the sintering process of the compact that consumes a lot of energy, the temperature rise profile is largely due to craftsmanship settings. If the control factors in the sintering process can be scientifically elucidated and optimized appropriately, energy consumption can be reduced, and thus cost can be reduced.

Thus, if the understanding of the structure formation process in the ceramic manufacturing process and the chemical elucidation of its control factors can be realized, it will be possible to systematize the ceramic manufacturing process technology. Through the optimization of the entire ceramic process chain, it is possible to solve various technical problems that are an obstacle to the spread of ceramics. As a result, it is possible to improve yield, reduce costs, increase reliability, and the like in the production of ceramics.

The present invention provides a ceramic internal structure observation method, a ceramic production method, an analysis system, and a ceramic production system, which can observe a structure formation process in a ceramic production process three-dimensionally in real time.

According to the first aspect of the present invention, the method for observing the internal structure of the ceramic reflects the step of dividing the light in the infrared region into reference light and irradiation light, the step of irradiating the ceramic with the irradiation light, and the reflection. Observing the internal structure of the ceramic using optical coherence tomography by observing interference between the reference light and return light obtained by irradiating the ceramic with the irradiation light.

The light in the infrared region may be light having a center wavelength in a range from 700 nanometers to 2000 nanometers and reflected by the ceramics.

The method for observing the internal structure of the ceramic includes a step of generating a tomographic image of each physical property state in the ceramic manufacturing process by the optical coherence tomography, and a tomographic image in each physical property state, and optical in any physical property state And a step of performing an analysis process for determining whether or not the state is uneven.

The physical properties include a slurry state containing the ceramic raw material in the manufacturing process, a dry state in which the material in the slurry state is dried, a molded state in which the material in the slurry state is molded after drying, and a material in the molded state At least any two of the sintered states obtained by sintering may be used.

The analysis processing includes a step of removing speckle noise caused by fine particles constituting the physical property state in the tomographic image, and a shape of an area having a luminance different from that in the tomographic image after the speckle noise removal processing. And a step of determining in which physical property state the optical non-uniform state is generated based on the size.

The ceramic internal structure observation method may further include a step of determining a processing method in the speckle noise removal processing based on machine learning for each physical property state.

The method for observing the internal structure of the ceramic may further include a step of determining a processing method in the processing for removing the speckle noise based on machine learning for each type of the optical non-uniform state.

The method of observing the internal structure of the ceramic based on learning data obtained by specifying the type of the optical non-uniform state by a method other than the method based on the optical coherence tomography. It may be included.

According to a second aspect of the present invention, a method for producing a ceramic is a method for producing a ceramic using optical coherence tomography, wherein the slurry contains an inorganic compound that is a raw material of the ceramic, or a granule of the inorganic compound. A preparation step for preparing the slurry, a molding step for molding the slurry containing the inorganic compound or the granule to form a compact, a sintering step for sintering the compact, and light in the infrared region for reference light and irradiation light The reference light that is irradiated with and reflected by any one of the slurry or the granules in the preparation step, the molded body in the molding step, or the sintered body in the sintering step. By observing interference with the return light obtained by irradiating the irradiation light to the slurry, the granule, the molded body or the sintered body, the slurry, the granule Including, an observation step of observing the internal structure of the molded body or the sintered body.

The observation step may include controlling the molding conditions in the molding step or the sintering conditions in the sintering step according to the observation result of the slurry or the granule or the internal structure of the molded body.

According to the third aspect of the present invention, the analysis system uses a tomographic image generation unit that generates a tomographic image of each physical property state in the ceramic manufacturing process by optical coherence tomography, and a tomographic image in each of the physical property states. And an analysis processing unit that performs an analysis process to determine in which physical property state an optical non-uniform state has occurred.

According to a fourth aspect of the present invention, a ceramic manufacturing system includes the above-described analysis system and at least one of a preparation device, a molding device, and a sintering device, and the preparation device, the molding device, and the sintering device. At least one of the apparatuses changes at least one of the conditions of ceramic preparation, molding, and sintering based on the analysis result of the analysis system.

According to the ceramic internal structure observation method, ceramic manufacturing method, analysis system, and ceramic manufacturing system described above, the structure formation process in the ceramic manufacturing process can be observed in three dimensions in real time.

It is a schematic diagram which shows an optical coherence tomography apparatus. 6 is an optical coherence tomography image in Experimental Example 1. 6 is an optical coherence tomography image in Experimental Example 1. It is an optical coherence tomography image in Experimental example 2. It is an optical coherence tomography image in Experimental example 2. It is an optical coherence tomography image in Experimental example 2. 10 is an optical coherence tomographic image of a molded body in Experimental Example 3. It is an optical coherence tomography image in Experimental example 4. 10 is an optical coherence tomography image of a thin film in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. 10 is an optical coherence tomography image of a sintered body in Experimental Example 5. It is a schematic block diagram which shows the example of a function structure of the analysis system which concerns on embodiment. It is a figure which shows the example of classification | category of the physical-property state of the sample which is the ceramics which concern on embodiment. It is a figure which shows the 1st example of the data for learning which concerns on embodiment. It is a figure which shows the 2nd example of the data for learning which concerns on embodiment. It is a figure which shows the example of the inconvenience which applies the removal processing method of the same speckle noise with respect to all the physical-property states of ceramics, and all the optical nonuniformity states. 5 is a flowchart illustrating an example of a procedure of processing performed by the analysis apparatus when analyzing a sample in the embodiment. 6 is a flowchart illustrating an example of a procedure of processing performed by the analysis processing unit in the analysis processing in the embodiment. 5 is a flowchart illustrating an example of a procedure of processing performed by a machine learning unit of a learning device when machine learning a speckle noise removal method in the embodiment. In an embodiment, it is a figure showing the 1st example of the procedure of the processing in which a speckle noise removal processing part performs a speckle noise removal processing. It is a figure which shows the 2nd example of the procedure of the process which a speckle noise removal process part performs a speckle noise removal process in embodiment. It is a figure which shows the 3rd example of the procedure of the process which the speckle noise removal process part 293 performs a speckle noise removal process. It is a schematic block diagram which shows the example of a function structure of the manufacturing system of the ceramics which concern on embodiment. It is a schematic block diagram which shows the structure of the computer which concerns on embodiment.

Embodiments of the present invention will be described below, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention.

[Method for observing the internal structure of ceramics]
The ceramic internal structure observation method according to the present embodiment is a ceramic internal structure observation method using optical coherence tomography, in which light in the infrared region is divided into reference light and irradiation light, and the ceramic is irradiated with irradiation light. The internal structure of the ceramic is observed by observing the interference between the reflected reference light and the return light obtained by irradiating the ceramic with the irradiation light.

The ceramics to be observed by the method for observing the internal structure of the ceramic of this embodiment are: an inorganic compound slurry, an inorganic compound granule, an inorganic compound dried body, an inorganic compound molded body, an inorganic compound sintered body, and an inorganic compound sintered body. Any one or more types of ligations.
The inorganic compound slurry corresponds to an example of a ceramic slurry. The inorganic compound granules correspond to examples of ceramic raw materials. An inorganic compound dry body corresponds to an example of a ceramic dry body, and an inorganic compound formed body corresponds to an example of a ceramic formed body. The sintered body of an inorganic compound corresponds to an example of a sintered body of ceramic.

The inorganic compound which is a raw material material for ceramics is not limited to a specific material as long as it is a material that transmits light in the infrared region. Examples of such inorganic compounds include silicon oxide (SiO ₂ ), silicon nitride (Si ₃ N ₄ ), hydroxide apatite (Ca ₁₀ (PO ₄ ) ₆ (OH) ₂ ), and aluminum oxide (Al ₂ O ₃ ). Boron nitride (BN), yttrium oxide (Y ₂ O ₃ ), zinc oxide (ZnO), titanium oxide (TiO ₂ ), calcium carbonate (CaCO ₃ ), and barium titanate (BaTiO ₃ ).

The slurry containing an inorganic compound contains the above inorganic compound and a solvent (dispersion medium).
The solvent is not particularly limited as long as it can disperse the above-described inorganic compound and transmits at least part of the light in the infrared region used in the present embodiment. Examples of the solvent include water, xylene, toluene, and ethanol.
Moreover, the slurry may contain a dispersing agent, a plasticizer, etc. in the range which does not inhibit the characteristic of an inorganic compound. Examples of the dispersant include polycarboxylic acid, polyacrylic acid, polyethyleneimine, and higher fatty acid ester.

As the granules of the inorganic compound, for example, those produced by a spray drying method using the above-described inorganic compound can be used.
Examples of the molded body of the inorganic compound include those in which the above slurry is put into a molding die and molded into a predetermined shape, and those in which inorganic compound granules are filled in a mold and compression molded. The molded body may contain a solvent.
Examples of the sintered body of the inorganic compound include a completely sintered body and a partially sintered body.

Here, optical coherence tomography will be described. Optical coherence tomography (OCT) is a technique for imaging the internal structure of a sample at high resolution and high speed using the coherence of light.
In the present embodiment, for example, an optical coherence tomography (OCT) apparatus as shown in FIG. 1 is used.
An optical coherence tomography apparatus 10 shown in FIG. 1 includes a light source 11, a half mirror 12, a reference mirror 13, and a detector 14.

The light source 11 is for irradiating the sample 100 with light in the infrared region. In the present embodiment, the sample 100 is ceramic.
The light source 11 emits light having a center wavelength of 700 nm (nanometers) to 2000 nm and reflected by the ceramic in the present embodiment. The light reflected by the ceramic is, for example, light that is not absorbed by the ceramic.

The half mirror 12 is provided on the optical path of light emitted from the light source 11. The half mirror 12 is arranged such that the surface 12a on the light source 11 side is inclined at an angle of 45 ° toward the light source 11 with respect to the optical path.
The half mirror 12 divides the light emitted from the light source 11 into irradiation light that irradiates the sample 100 and reference light that enters the reference mirror 13. Then, the half mirror 12 reflects the divided irradiation light and makes it enter the sample 100. Further, the half mirror 12 transmits the divided reference light and makes it incident on the reference mirror 13.

The reference mirror 13 is provided on the optical path of the light emitted from the light source 11.
The reference mirror 13 reflects the reference light transmitted through the half mirror 12 and returns the reflected light to the half mirror 12. Therefore, the reference mirror 13 is provided so as to face the half mirror 12.
Further, the reference mirror 13 is movable along the optical path direction of the light emitted from the light source 11. That is, the reference mirror 13 can adjust the distance from the half mirror 12. Instead of making the reference mirror 13 movable, a wavelength variable light source may be used to perform the same function.

The detector 14 is provided on the optical path of the return light obtained by irradiating the sample 100 with the irradiation light and on the optical path of the reference light. The reference light is reflected by the reference mirror 13, returns to the half mirror 12, and further reflected by the half mirror 12.
The detector 14 is for observing the return light and the reference light.

Observation or imaging of the internal structure of the sample 100 using the optical coherence tomography apparatus 10 is performed as follows.
The light source 11 emits light in the infrared region. Here, the light in the infrared region is light having a central wavelength from 700 nm to 2000 nm and reflected by ceramics.
The half mirror 12 divides the light emitted from the light source 11 into irradiation light that irradiates the sample 100 and reference light that enters the reference mirror 13. The half mirror 12 reflects the divided irradiation light and makes it incident on the sample 100. Further, the half mirror 12 transmits the divided reference light and makes it incident on the reference mirror 13.

Irradiation light incident on the sample 100 is reflected at an interface having a difference in refractive index, such as the surface or internal structure of the sample 100, and is emitted from the surface of the sample 100 as return light.
The return light obtained by irradiating the sample 100 with the irradiation light and the reference light reflected and returned by the reference mirror 13 are superimposed again on the half mirror 12. At this time, if the distance traveled by the return light from the sample 100 and the reference light from the reference mirror 13 is equal, the two lights strengthen each other. On the other hand, when the return light from the sample 100 and the reference light from the reference mirror 13 are shifted in distance and the phases of the light are reversed, the two lights cancel each other.

Here, the reference mirror 13 is moved to adjust the distance between the reference mirror 13 and the half mirror 12, and the position where the two lights interfere and strengthen on the detector 14 is observed. By this observation, it is possible to know at which depth in the sample 100 the reflecting surface is present. Thereby, the internal structure of the sample 100 can be observed. Moreover, the internal structure of the sample 100 can be photographed by imaging the inspection result.
According to the optical coherence tomography apparatus 10, the internal structure of the sample 100 can be observed or photographed in real time. Furthermore, according to the optical coherence tomography apparatus 10, observation of the internal structure of the sample 100 can be recorded as a moving image.

In the ceramic internal structure observation method of the present embodiment, the light source 11 irradiates the sample 100 with light having a central wavelength of 700 nm to 2000 nm and reflected by the sample 100 as light in the infrared region. By moving the reference mirror 13, the sample 100 made of ceramics is observed. Thereby, the internal structure of the ceramics which could not be observed conventionally can be observed three-dimensionally. If a wavelength tunable light source is used instead of making the reference mirror 13 movable, the sample 100 may be observed by adjusting the wavelength and intensity of light emitted from the wavelength tunable light source.

Here, the example which can apply the internal structure observation method of the ceramics of this embodiment is shown.
[Case 1]
The use of composite particles composed of a plurality of types of particles is one of the methods for controlling the microstructure of ceramics and making ceramics highly functional and multifunctional. Examples of using composite particles composed of a plurality of types of particles include adjustment of nanocomposite particles by mechanical treatment and control of the microstructure of ceramics using this.

For example, attention is paid to a post reaction sintering method which is a manufacturing process capable of manufacturing silicon nitride (Si ₃ N ₄ ) ceramics at low cost. Then, it is considered that the structure of the molded body is controlled using nanocomposite particles composed of silicon (Si) and sintering aids yttrium oxide (Y ₂ O ₃ ) and aluminum oxide (Al ₂ O ₃ ). . By this control, a homogeneous nitride and a dense sintered body are generated.
By using nanocomposite particles, it became clear that the sintering aid suppresses the contact between the silicon particles, and the silicon particles can be uniformly nitrided without melting. In addition, it was revealed that silicon nitride (Si ₃ N ₄ ) ceramics that are dense and have no coarse pores can be produced by using nanocomposite particles and densifying them at high temperatures.

Thus, the use of nanocomposite particles is effective for controlling the microstructure of ceramics and improving the manufacturing process of ceramics. However, for example, the structure of a molded body formed using nanocomposite particles should be different from that formed by a mixing process using general uncomposited fine particles. Nevertheless, the correlation between the structure of a molded body formed using nanocomposite particles and the sintering behavior of the molded body has not been clarified.

According to the method for observing the internal structure of a ceramic according to the present embodiment, the structure and forming process of a compact formed using nanocomposite particles, the drying process of slurry, and the sintering behavior of the compact are observed in three dimensions in real time. can do. Therefore, according to the ceramic internal structure observation method of the present embodiment, it is possible to clarify the correlation between the structure of a molded body formed using nanocomposite particles and the sintering behavior of the molded body.

[Case 2]
A method has been proposed in which the surface design and dispersion process of fine particles, which have been relied on intuition and experience, have been elucidated from a scientific viewpoint based on the evaluation of the flow characteristics of the slurry and the interaction between the particles.
According to this method, the fine particles can be dispersed in the dispersion medium by individual surface design according to the material of the particles and the type of the dispersion medium. For example, the fine particle surface modification by multi-step chemical reaction, the development of new surface modifiers and the development of appropriate surface modification operation methods have achieved a high degree of dispersion of fine particles of various materials in solvents and fingers. . In addition, by developing aggregates of cationic polymers and fatty acids, we have developed a simple and designed modification process for polymer dispersants. Design of dispersants that can be used for various particle materials and high-concentration multi-component slurry Successful decentralization.

However, in the above-described technology, the wettability between the fine particles, the aggregates and the solvent, and the interface structure in the presence of other organic substances such as a binder and a lubricant are not understood and optimized. In addition, the above-described technology is currently specialized in fine particle dispersion, and the particle interface is not designed in consideration of the fine particle drying process and the fine particle forming process in the production of ceramics.

According to the ceramic internal structure observation method of the present embodiment, the drying process of fine particles and the forming process of fine particles can be observed in three dimensions in real time. Therefore, according to the ceramic internal structure observation method of the present embodiment, the particle interface can be designed in consideration of the drying process of the fine particles and the forming process of the fine particles.

[Case 3]
By adding an appropriate amount of a dispersant to the ceramic powder, a highly concentrated and highly dispersible slurry can be prepared. In addition, the viscosity of the slurry changes greatly with increasing temperature. By utilizing such a phenomenon, a high-density and homogeneous molded body can be generated.
Utilizing this phenomenon, a silicon oxide (SiO ₂ ) glass molded body is produced, and the glass molded body is fired at 1400 ° C. in the air. Thereby, a transparent silicon oxide (SiO ₂ ) sintered body having a complicated shape can be generated without melting at a high temperature. Furthermore, by using a three-dimensional printer to produce a structure with resin, using this structure as a mold, casting a highly concentrated and highly dispersible slurry to form a molded body, and firing the molded body A transparent microstructure can be generated.

In order to further develop a method for manufacturing a ceramic microstructure using such a resin mold, it is necessary to make it possible to prepare a slurry having a high concentration and high dispersibility regardless of the properties of the material. In addition, it is necessary to elucidate the structure formation process in the manufacturing process of homogeneous molded bodies and sintered bodies, and to design the manufacturing process based on the results.
According to the ceramic internal structure observation method of the present embodiment, the structure formation process in the manufacturing process of the molded body and the sintered body can be observed three-dimensionally in real time. Therefore, according to the ceramic internal structure observation method of the present embodiment, the structure formation process in the manufacturing process of the molded body and the sintered body can be clarified, and the manufacturing process can be designed based on the result.

[Case 4]
For the purpose of reducing the manufacturing cost of ceramics, it is conceivable to produce high-strength silicon nitride (Si ₃ N ₄ ) ceramics using low-cost powder synthesized by direct nitriding as a raw material.
However, the strength of silicon nitride (Si ₃ N ₄ ) ceramics produced by conventional methods is low.

It has been clarified that this is caused by the fact that coarse crack-like voids are the source of destruction. As a result of observing the internal structure of the low-cost powder compact with an infrared microscope, it was confirmed that a large defect was present at the particle interface when coarse particles were used. Since these defects remain as coarse defects in the sintered body obtained by sintering the molded body, it is estimated that the strength of the silicon nitride (Si ₃ N ₄ ) ceramics was reduced.

On the other hand, when fine particles are used as a raw material, the bending strength of the obtained silicon nitride (Si ₃ N ₄ ) ceramics is much higher than when coarse particles are used. This bending strength was almost the same as the bending strength of silicon nitride (Si ₃ N ₄ ) ceramics synthesized by the imide decomposition method. Silicon nitride (Si ₃ N ₄ ) ceramics using fine particles as a raw material was an unsintered region having a fracture source of 20 μm (micrometers) or less. When this silicon nitride (Si ₃ N ₄ ) ceramics is observed with an infrared microscope, defects of about 10 μm to 20 μm are observed in its internal structure. However, this silicon nitride (Si ₃ N ₄ ) ceramics has been found to be more homogeneous than silicon nitride (Si ₃ N ₄ ) ceramics produced by conventional methods.

Furthermore, the density of the molded body that has been subjected to cold isostatic pressing (CIP) molding 10 times using a fine particle is higher than the density of the molded body that has been subjected to CIP molding 1 time. It turns out that it improves. The sintered body of the molded body subjected to the CIP molding 10 times repeatedly showed a higher bending strength than the bending strength of silicon nitride (Si ₃ N ₄ ) ceramics synthesized by the imide decomposition method.
That is, the observation of the molded body and the sintered body using light in the near-infrared region has proved effective for elucidating these internal structures. Also, observation of compacts and sintered compacts using light in the near infrared region shows that the use of fine particles and repeated CIP molding are effective in increasing the strength of silicon nitride (Si ₃ N ₄ ) ceramics. It became clear that there was.

In order to extend this concept to other materials and improve the reliability of ceramics, elucidate the structure formation process from fine particles to compacts, elucidate the structure formation process from compacts to sintered bodies, and It is necessary to elucidate regulatory factors such as additives.
According to the ceramic internal structure observation method of the present embodiment, the structure formation process from the fine particles to the molded body and the structure formation process from the molded body to the sintered body can be observed three-dimensionally in real time. Therefore, according to the method for observing the internal structure of the ceramic according to the present embodiment, it is possible to elucidate the structure forming process from the fine particles to the molded body and the structure forming process from the molded body to the sintered body.

[Case 5]
If the pores, which are light scattering sources, can be completely removed from the molded body by homogenizing the molded body and controlling the behavior of fine particles when sintering the molded body, make the ceramic transparent. Is possible. Transparent fluorescent sialon ceramics could be developed by densifying sialon ceramics at low temperature based on the above-mentioned repeated CIP molding method and elucidation of the behavior of fine particles during sintering. That is, it was found that a new function of transparency can be imparted to ceramics by efficiently removing pores in the molded body by controlling the internal structure of the molded body.

In order to realize such transparent ceramics, it is necessary to design an integrated manufacturing process from raw material powder to slurry preparation, molding of a molded body, and sintering of the molded body.
However, in the past research and development, the relationship between each operation has not been sufficiently studied, and the entire ceramics process chain has not been optimized.

According to the ceramic internal structure observation method of the present embodiment, the structure formation process from the raw material powder to the slurry, the structure formation process from the raw material powder to the compact, and the structure formation process from the compact to the sintered body Can be observed in three dimensions in real time. Therefore, according to the method for observing the internal structure of the ceramic of the present embodiment, it is possible to sufficiently examine the relation of each operation, and the entire ceramic process chain can be optimized.

[Case 6]
One technique for improving the characteristics of crystalline ceramics and developing new functions is to use anisotropy by orienting crystals. Reported methods for generating crystallographic orientation materials include mechanical methods such as sheet molding that uses particle geometry, and methods for magnetically orienting particles with a superconducting magnet using the magnetic anisotropy of particles. Has been. The mechanical method has a problem in that the direction in which orientation can be performed with respect to the outer shape of the ceramic is limited. The method of orienting particles in a magnetic field can control the direction in which the particles are orientated by a magnetic field. However, this method requires a superconducting magnet in order to orient a ceramic powder having a small absolute value of a general diamagnetic susceptibility. In addition, this method has a problem that it may be necessary to apply the magnetic field to the fine particles while rotating the magnetic field. On the other hand, it is conceivable that graphene having anisotropic giant diamagnetism as particles bearing magnetic torque is combined with host fine particles to impart magnetic anisotropy to the powder. Thus, oriented ceramics are produced by effectively applying magnetic torque to fine particles in a low magnetic field and a static magnetic field.

Graphene-coated silicon nitride (Si ₃ N ₄ ) particles prepared by a mechanical method can be oriented in a low magnetic field as high as a neodymium magnet and in a static magnetic field. Hereinafter, the silicon nitride (Si ₃ N ₄ ) particles coated with graphene are also referred to as graphene-coated particles. By using the graphene-coated particles, C-axis-oriented silicon nitride (Si ₃ N ₄ ) ceramics can be generated without using a superconducting magnet that has been essential in the past.
A method for producing silicon nitride (Si ₃ N ₄ ) ceramics without using a superconducting magnet will be described. First, a graphene-coated particle is oriented in a magnetic field to form a compact. Next, the graphene is oxidized to remove the graphene from the molded body. Next, the compact from which graphene has been removed is fired at a high temperature to obtain C-axis oriented silicon nitride (Si ₃ N ₄ ) ceramics.

This manufacturing process is applicable not only to silicon nitride (Si ₃ N ₄ ) ceramics but also to many ceramics, and can be applied to mass production. In addition, such a ceramic manufacturing technique is an important microstructure control method that enables the essential characteristics of ceramics to be exhibited. In order to expand this ceramic manufacturing technology to other material systems, appropriate dispersion of graphene coated particles, low viscosity of slurry, and elucidation and control of anisotropy of sintering shrinkage, etc. It is necessary to improve the operation. In addition, the entire ceramic process chain needs to be optimized. However, at present, it is difficult to say that these findings are sufficiently obtained, and further study is necessary.

According to the ceramic internal structure observation method of the present embodiment, the dispersion state of the graphene-coated particles in the slurry and the shrinkage anisotropy when the molded body is sintered can be observed in three dimensions in real time. . Therefore, according to the method for observing the internal structure of the ceramic according to the present embodiment, it is possible to appropriately disperse the graphene-coated particles, lower the viscosity of the slurry, and elucidate and control the anisotropy of the sintering shrinkage. The operation can be advanced and the entire ceramic process chain can be optimized.

[Case 7]
The shrinkage accompanying the sintering of ceramics may cause cracking or deformation of the ceramics in some cases. Therefore, in order to produce a homogeneous ceramic, it is necessary to evaluate the shrinkage behavior associated with the sintering of the ceramic and to control the shrinkage behavior. So far, master sintering curve (MSC) theory, which is an extension of classical sintering theory, has been proposed. Here, it is conceivable to perform in-situ measurement of the sintering shrinkage curves of various ceramics, apply the MSC theory to the analysis, and control the sintering shrinkage of the ceramics using the obtained MSC. Furthermore, the present inventors can newly propose a master sintering curve theory relating to liquid phase sintering and apply it to the analysis of the sintering shrinkage behavior of silicon nitride (Si ₃ N ₄ ) ceramics.

However, the relationship between the MSC and the structure of the molded body is not clear, and the control of the sintering shrinkage behavior cannot be fed back to the formation of the structure of the molded body. In addition, since the conventional MSC theory cannot be applied in principle when external stress is applied, it cannot be introduced into finite element analysis to simulate the sintering shrinkage behavior of complex shapes, and further development is necessary. It was.

According to the method for observing the internal structure of the ceramic according to the present embodiment, the structural change of the formed body during sintering can be observed in three dimensions in real time. Therefore, according to the method for observing the internal structure of the ceramic of this embodiment, the relationship between the MSC and the structural change of the compact during sintering is clarified, and the control of the sintering shrinkage behavior of the ceramic is fed back to the structure formation of the compact. can do.

[Ceramics manufacturing method]
The ceramic manufacturing method of this embodiment is a ceramic manufacturing method using optical coherence tomography, and includes a preparation step of preparing a slurry containing an inorganic compound that is a ceramic raw material, or a granule of an inorganic compound, and an inorganic compound. A molding step that forms a slurry or granule containing a compound into a molded body, a sintering step that sinters the molded body, and the light in the infrared region is divided into reference light and irradiation light, and the slurry or granule in the preparation step, It is obtained by irradiating irradiated light to either the molded body in the molding process or the sintered body in the sintering process, and reflecting the reflected reference light and the slurry, granules, molded body or sintered body. An observation step of observing the internal structure of the slurry, granule, molded body or sintered body by observing the interference with the return light.

In the preparation step, a slurry containing the inorganic compound is prepared by dispersing the inorganic compound in the solvent. In the preparation step, granules of the inorganic compound are prepared by spray drying or the like using the inorganic compound.
In the preparation step, a dispersant, a plasticizer, or the like may be used as necessary.

In the molding step, the slurry prepared in the preparation step is put into a molding die having a predetermined shape and molded into a predetermined shape. In the molding step, the granules prepared in the preparation step are filled into a mold, compression-molded, and molded into a predetermined shape.
In the sintering process, the molded body molded in the molding process is sintered at a predetermined temperature to obtain a sintered body of an inorganic compound.

In the observation process, the light in the infrared region is divided into reference light and irradiation light, and either the slurry or granule in the preparation process, the molded body in the molding process or the sintered body in the sintering process is irradiated with the irradiation light and reflected. The interference between the reflected reference light and the return light obtained by irradiating the slurry, granules, molded body or sintered body with irradiation light is observed. Thereby, the internal structure of a slurry, a granule, a molded object, or a sintered compact is observed.
Observation of the internal structure in the observation step is performed in the same manner as the above-described ceramic internal structure observation method.

According to the ceramic manufacturing method of the present embodiment, by having an observation step, the observation result can be fed back to the molding conditions in the molding step and the sintering conditions in the sintering step. Thereby, dense and homogeneous ceramics can be produced efficiently. As a result, in the production of ceramics, it is possible to realize improvement in yield, cost reduction, high reliability, and the like.

In the ceramic manufacturing method of the present embodiment, the molding conditions in the molding process or the sintering conditions in the sintering process may be controlled according to the observation result of the slurry or granules in the observation process or the internal structure of the molded body. Good. That is, the molding conditions in the molding process are controlled according to the observation results of the slurry or granules in the observation process, and the sintering conditions in the sintering process are controlled according to the observation results of the internal structure of the molded body in the observation process. You may make it do.
Thereby, in the manufacturing method of the ceramics of this embodiment, the molding conditions in the molding process and the sintering conditions in the sintering process can be optimized.

[Ceramics manufacturing system]
The ceramic manufacturing system of this embodiment includes at least one selected from the group consisting of a preparation device, a molding device, and a sintering device, and an optical coherence tomography apparatus. The optical coherence tomography apparatus applies light in the infrared region to either a slurry containing an inorganic compound that is a ceramic raw material or a granule of an inorganic compound, a molded body of a slurry or a granule, or a sintered body obtained by sintering a molded body. A light source for irradiation, and a detector for observing interference between the return light of the light irradiated on the slurry, granule, molded body or sintered body and the reference light. The molding apparatus has a control unit that controls the molding conditions of the slurry or granules according to the result observed by the detector. The sintering apparatus has a control unit that controls the sintering conditions of the compact according to the result observed by the detector.

Examples of the optical coherence tomography apparatus include those similar to those used in the above-described ceramic internal structure observation method.
The preparation device prepares a slurry containing the inorganic compound by dispersing the inorganic compound in the solvent. As the preparation apparatus, for example, an apparatus generally used for preparing a slurry can be used. Moreover, the preparation device makes the inorganic compound into granules. As the preparation device, for example, a device capable of granulating the inorganic compound by a spray drying method is used.

The molding apparatus is not limited to a specific one as long as it has a molding die capable of charging slurry or granules into the mold and molding the slurry into a predetermined shape. As the molding apparatus, for example, an apparatus generally used for wet molding using a slurry or dry molding using granules can be used.
The sintering apparatus is not limited to a specific one as long as it includes a sintering furnace for sintering the molded body. As a sintering apparatus, an apparatus generally used for sintering a molded body made of slurry or granules can be used.

According to the ceramic manufacturing system of the present embodiment, the molding apparatus has a control unit that controls the molding conditions of the slurry or granule according to the result observed by the detector of the optical coherence tomography apparatus, so that it is more precise. A homogeneous molded body can be molded. In addition, according to the ceramic manufacturing system of the present embodiment, the sintering apparatus has a control unit that controls the sintering conditions of the compact according to the result observed by the detector of the optical coherence tomography apparatus. Thus, it is possible to obtain a sintered body in which warpage and cracking are further reduced. That is, according to the ceramic manufacturing system of the present embodiment, it is possible to achieve an improvement in yield, cost reduction, high reliability, and the like in ceramic manufacturing.

Hereinafter, the present invention will be described more specifically with experimental examples, but the present invention is not limited to the following experimental examples.
[Experimental Example 1]
An alumina slurry in which aluminum oxide (Al ₂ O ₃ ) was dispersed in water was prepared. In addition, as an alumina slurry, what added the ammonium polycarboxylate of a dispersing agent, and the thing which did not add a dispersing agent were prepared.
The alumina slurry was irradiated with light having a central wavelength of 930 nm using an optical coherence tomography (OCT) apparatus similar to that shown in FIG. 1, and the internal structure of the alumina slurry was observed. A product name: GAN930V2-BU, manufactured by Sorabo Japan Co., Ltd. was used as the optical coherence tomography apparatus.

The results are shown in FIG. 2 and FIG. FIG. 2 is an optical coherence tomography image of an alumina slurry with no dispersant added. FIG. 3 is an optical coherence tomography image of an alumina slurry to which a dispersant is added.
In the optical coherence tomography apparatus, since an image is obtained by utilizing light interference, the contrast increases in a region where light is more strongly scattered.
From the result of FIG. 2, it was confirmed that a structure of about several tens of μm appeared in the alumina slurry without addition of a dispersant. On the other hand, from the results of FIG. 3, it was confirmed that the alumina slurry to which the dispersant was added did not show a structure like the alumina slurry without the dispersant.

Moreover, although it is not known by conventional static structure observation, the following has been clarified by dynamic structure observation by an optical coherence tomography apparatus.
The internal structure of the alumina slurry to which no dispersant was added changed relatively slowly on a scale of about several hundred μm over time.
On the other hand, as for the internal structure of the alumina slurry to which the dispersant was added, fast structure fluctuations that were attributed to the Brownian motion of the aluminum oxide fine particles were observed. In addition, the alumina slurry to which the dispersant was added was also confirmed to have a structural change that seems to accompany drying from the surface.

[Experiment 2]
A silicon nitride slurry in which silicon nitride (Si ₃ N ₄ ) was dispersed in toluene was prepared. The silicon nitride slurry was added with an aggregate of polyethyleneimine and oleic acid as a dispersant.
The silicon nitride slurry was irradiated with light having a center wavelength of 1310 nm using an optical coherence tomography (OCT) apparatus similar to that shown in FIG. 1, and the internal structure of the silicon nitride slurry was observed. As an optical coherence tomography apparatus, trade name: IVS-2000, manufactured by santec was used.

The results are shown in FIGS. FIG. 4 is an optical coherence tomography image of a silicon nitride slurry, showing a silicon nitride slurry (fluidized bed). FIG. 5 is an optical coherence tomography image of a silicon nitride slurry, showing a slide glass onto which silicon nitride slurry has been dropped, and a silicon nitride slurry (fluidized bed), and showing the silicon nitride slurry being dried. FIG. 6 is an optical coherence tomographic image of a silicon nitride slurry, showing a slide glass onto which silicon nitride slurry has been dropped and a silicon nitride slurry (fluidized bed), and showing the silicon nitride slurry after drying.

From the result of FIG. 5, since light is scattered in the silicon nitride slurry in the middle of drying, the interface between the slide glass and the silicon nitride slurry was clearly confirmed.
As for the internal structure of the silicon nitride slurry to which the dispersing agent was added, a rapid fluctuation of the structure, which was probably caused by the Brownian motion of the silicon nitride fine particles, was observed. In addition, it was confirmed that the silicon nitride slurry to which the dispersant was added had a structural change that seems to be caused by drying from the surface.

[Experiment 3]
A molded product was obtained by dry molding using commercially available aluminum oxide granules (trade name: AKS-20, manufactured by Sumitomo Chemical Co., Ltd.).
The obtained molded body was irradiated with light having a central wavelength of 1310 nm using an optical coherence tomography (OCT) apparatus (trade name: IVS-2000, manufactured by Santec) similar to that shown in FIG. The internal structure of the body was observed.
The results are shown in FIG. FIG. 7 is an optical coherence tomography image of the molded body. In FIG. 7, a structure with dimensions corresponding to aluminum oxide granules was observed.

[Experimental Example 4]
The granules in Experimental Example 3 were put into a transparent mold and the internal structure was observed while applying pressure.
Using an optical coherence tomography (OCT) apparatus similar to that shown in FIG. 1, light having a wavelength of 1310 nm was irradiated to observe the internal structure. As an optical coherence tomography apparatus, trade name: IVS-2000, manufactured by santec was used.
The results are shown in FIG. FIG. 8 is an optical coherence tomography image of aluminum oxide granules.
In Experimental Example 4, it was observed in real time that the granule was deformed and the gap between the granules decreased and was formed.

[Experimental Example 5]
The aluminum oxide molded body produced in Experimental Example 3 was fired at 1400 ° C. for 2 hours to produce a sintered body.
The obtained sintered body was irradiated with light having a central wavelength of 1310 nm using an optical coherence tomography (OCT) apparatus similar to that shown in FIG. 1, and the internal structure of the sintered body was observed. As an optical coherence tomography apparatus, trade name: IVS-2000, manufactured by santec was used. In Experimental Example 5, the internal structure of the sintered body was observed in order along the thickness direction using a wavelength variable light source.

The results are shown in FIGS. 9 to 23 are optical coherence tomography images of the sintered bodies, respectively. 9 to 23 show results of observing the internal structure of the sintered body along the thickness direction in order from the side closer to the light source.
From the results of FIGS. 9 to 23, the sintered body was almost homogeneous. Further, in FIGS. 9 to 23, several regions having a large contrast were observed. This region seems to correspond to a region that is not sufficiently densified in view of the properties of the image obtained by optical coherence tomography.

[Analysis system and analysis method]
Next, an analysis system and an analysis method according to this embodiment will be described.
FIG. 24 is a schematic block diagram illustrating an example of a functional configuration of the analysis system according to the present embodiment. With the configuration shown in FIG. 24, the analysis system 1 includes an optical coherence tomography apparatus 10, an analysis apparatus 20, and a learning apparatus 30. The analysis device 20 includes a first communication unit 210, a first storage unit 280, and a first control unit 290. The first control unit 290 includes a tomographic image generation unit 291, an analysis processing unit 292, a speckle noise removal processing unit 293, and a non-uniform state detection unit 294. The learning device 30 includes a second communication unit 310, a second storage unit 380, and a second control unit 390. The second storage unit 380 includes a learning data storage unit 381. The second control unit 390 includes a learning data acquisition unit 391 and a machine learning unit 392.
The optical coherence tomography apparatus 10 in FIG. 24 is the same as the optical coherence tomography apparatus 10 in FIG. 1, and is denoted by the same reference numeral (10) and description thereof is omitted.

The analysis system 1 analyzes a sample 100 that is ceramic. In particular, the analysis system 1 acquires a tomographic image of the sample 100 and analyzes the state of the sample 100 based on the luminance in the tomographic image.
The analysis device 20 generates a tomographic image of the sample 100 based on the measurement result of the sample 100 by the optical coherence tomography apparatus 10, and analyzes the state of the sample 100 using the obtained tomographic image.
The analysis device 20 is configured using a computer such as a personal computer (PC) or a workstation.

The first communication unit 210 communicates with other devices. In particular, the first communication unit 210 communicates with the optical coherence tomography apparatus 10 and receives the measurement result of the sample 100 by the optical coherence tomography apparatus 10. In addition, the first communication unit 210 communicates with the second communication unit 310 of the learning device 30 and receives a learning result of speckle noise (Speckle Noise) removal processing by the learning device 30 from the learning device 30. Further, the first communication unit 210 communicates with the second communication unit 310 of the learning device 30 to transmit a tomographic image of the sample 100 to the learning device 30.

The first storage unit 280 stores various data. The first storage unit 280 is configured using a storage device provided in the analysis apparatus 20.
The first control unit 290 controls each unit of the analysis device 20 and performs various processes. The first control unit 290 is configured by a CPU (Central Processing Unit) included in the analysis apparatus 20 reading out and executing a program from the first storage unit 280.

The tomographic image generation unit 291 generates a tomographic image of each physical property state in the ceramic manufacturing process by optical coherence tomography by the optical coherence tomography apparatus 10. Specifically, the tomographic image generation unit 291 generates a tomographic image of the sample 100 based on the measurement result of the sample 100 by the optical coherence tomography apparatus 10. As a method for generating a tomographic image by the tomographic image generating unit 291, a known tomographic image generating method in optical coherence tomography can be used.

The direction of the tomographic image generated by the tomographic image generation unit 291 is not limited to a specific direction. For example, the optical coherence tomography apparatus 10 may scan the sample 100 three-dimensionally, and the tomographic image generation unit 291 may generate a three-dimensional image of the sample 100. Thereby, the tomographic image generation unit 291 can generate a tomographic image at an arbitrary position and an arbitrary direction within the scan range of the sample 100.

FIG. 25 is a diagram illustrating a classification example of the physical property state of the sample 100 which is ceramic. In the example of FIG. 25, the physical properties of ceramics are classified into a raw material state, a slurry state, a dry state, a molded state, and a sintered state.
The raw material state is a state before the ceramic raw material powder and the solvent are mixed.
A slurry can be obtained by mixing a ceramic raw material powder and a solvent. The slurry state is a state in which ceramics are in a slurry state.

A dried product can be obtained by drying the slurry. The dry state is a state in which the ceramic is a dry body.
A molded body can be obtained by molding the dried body. The molded state is a state in which the ceramic is a molded body.
A sintered body can be obtained by sintering the molded body. The sintered state is a state in which the ceramic is a sintered body.

The tomographic image generation unit 291 generates a tomographic image of the sample 100 for any one or more of the physical properties of the sample 100 that are ceramics. In particular, the tomographic image generation unit 291 may generate a tomographic image of the sample 100 for each of a plurality of physical properties of the sample 100, such as generating a tomographic image of the sample 100 for all of the above physical properties. Good. By analyzing the tomographic image of the sample 100 for each of a plurality of physical property states of the sample 100, the analysis apparatus 20 can not only determine the presence or absence of an optical non-uniform state in the sample 100, but also in which physical property state the optical non-uniform state You can get information about what happened.

The analysis processing unit 292 detects an optical non-uniform state in the sample 100 using the tomographic image of the sample 100 generated by the tomographic image generation unit 291. The optical non-uniform state in the sample 100 here is a state in which the state of light reflection is different from the tendency in the entire sample 100. In the tomographic image of the sample 100, the optical non-uniform state is shown as a difference in luminance. Hereinafter, a portion of the tomographic image whose luminance is different from the tendency in the entire tomographic image is referred to as an optically non-uniform portion in the tomographic image.
As described above, the tomographic image generation unit 291 generates a tomographic image of the sample 100 in each of the plurality of physical property states of the sample 100, and the analysis processing unit 292 uses these tomographic images to perform optical in any physical property state. You may make it perform the analysis process about whether the target nonuniform state has arisen.

The speckle noise removal processing unit 293 removes noise in the tomographic image of the sample 100. In particular, the speckle noise removal processing unit 293 removes speckle noise in the tomographic image of the sample 100. In the case of a tomographic image of ceramics, speckle noise is caused by fine particles constituting the physical state of ceramics.
The speckle noise removal processing unit 293 determines a speckle noise removal method to be applied to the tomographic image according to the speckle noise removal method acquired by the learning device 30 through machine learning, and executes the determined method.

The non-uniform state detection unit 294 detects an optical non-uniform state in the sample 100 using the tomographic image of the sample 100 after the speckle noise removal process. The non-uniform state detection unit 294 determines the type of the optical non-uniform state in addition to detecting the area where the optical non-uniform state occurs in the tomographic image. Specifically, it is determined whether the optical nonuniformity is a pore or a crack based on the shape and size of an area having a luminance different from that in the tomographic image. However, the optical non-uniform state detected by the non-uniform state detection unit 294 is not limited to pores or cracks.
The non-uniform state detection unit 294 uses the tomographic image of the sample 100 after the speckle noise removal processing in each of the plurality of physical state of the sample 100 to determine in which physical state the optical non-uniform state has occurred. Analysis processing may be performed.

The learning device 30 performs machine learning on a method for removing speckle noise from a tomographic image. The learning device 30 performs machine learning on a method of removing speckle noise from a tomographic image for each physical property state of ceramics and for each type of optical non-uniform state. Alternatively, as will be described later, the learning device 30 is a machine that removes speckle noise from a tomographic image for each physical property state of ceramics, for each type of optical non-uniform state, and for each type of substance constituting the ceramic. You may make it learn.
The learning device 30 is configured using a computer such as a personal computer (PC) or a workstation.

The second communication unit 310 communicates with other devices. In particular, the second communication unit 310 communicates with the first communication unit 210 of the analysis device 20 and transmits the learning result of the speckle noise removal processing by the learning device 30 to the analysis device 20. In addition, the second communication unit 310 communicates with the first communication unit 210 of the analysis apparatus 20 and receives a tomographic image of the sample 100 from the analysis apparatus 20.
The second storage unit 380 stores various data. The second storage unit 380 is configured using a storage device provided in the learning device 30.

The learning data storage unit 381 stores learning data. The learning data here is data for the learning device 30 to machine-learn the speckle noise removal method.
The learning data storage unit 381 stores learning data for each physical property state of ceramics and for each type of optical non-uniform state. Alternatively, the learning data storage unit 381 may store learning data for each physical property state of ceramics, for each type of optical non-uniform state, and for each type of substance constituting the ceramic.

FIG. 26 is a diagram showing a first example of learning data. FIG. 26 shows an example of learning data for learning a speckle noise removal method applied to a tomographic image when pores in a ceramic sintered body are detected. In FIG. 26, the learning data is shown in a table format, and one row corresponds to one learning data. Each of the learning data is configured by combining an identification number, an original image, and a target image.

The identification number is a number for identifying learning data.
As the original image, a tomographic image before the removal of speckle noise in which the user knows the physical property state and the optical non-uniform state type of the ceramic is used. FIG. 26 shows an example of data for performing machine learning in the case of detecting pores in a ceramic sintered body, and thus a tomographic image showing pores is used as an original image.
The background portion of the original image includes speckle noise. In the case of the learning data with the identification number A1, the region A111 corresponds to the background portion, the region A112 corresponds to the region of the pore portion image, and the region A113 is the image region of the boundary portion between the pore and the portion other than the pores. It corresponds to.

A region A112 that is an image region of the pore portion is a relatively dark region. A region A113, which is an image region at the boundary portion of the pores, is a relatively bright region.
The background image area A111 is a relatively dark area, but is brighter than the area A112 because it includes speckle noise. Since the area A111 is slightly bright, the area A111 and the area A112 are relatively difficult to distinguish. In this regard, it is difficult to detect the pore region in the image before speckle noise removal.

In the case of the learning data with the identification number A2, the region A121 corresponds to the background portion, the region A122 corresponds to the image region of the pore portion, and the region A123 is the image region of the boundary portion between the pore and the portion other than the pore. It corresponds to. In the case of the learning data with the identification number A3, the region A131 corresponds to the background portion, the region A132 corresponds to the image region of the pore portion, and the region A133 is the image region of the boundary portion between the pore and the portion other than the pore. It corresponds to.

As the target image, an image obtained by removing speckle noise from the original image is used. An image obtained by actually performing speckle noise removal processing on the original image may be used as the target image.
Alternatively, an image generated by the user based on the original image may be used as the target image. For example, the user may process the original image to generate the target image. Alternatively, the user may draw the target image with reference to the original image.
Alternatively, a tomographic image captured by a method other than optical coherence tomography, such as an image captured by an infrared camera installed so as to have the same angle of view as the original image, may be used as the target image.

In the case of the learning data with the identification number A1, the area A211 corresponds to the areas A111 and A112 of the original image. By removing speckle noise from the background image area, the background image area and the functional image area have the same brightness. A region A212 corresponds to the region A113 of the original image. The area A212 is a relatively bright area like the area A113.
Since the speckle noise is removed from the background image area to make it darker, it is easier to detect the relatively bright area A212. In this regard, it is easy to detect the pore region in the image after speckle noise removal.

In the case of the learning data with the identification number A2, the area A221 corresponds to the areas A121 and A122 of the original image. An area A222 corresponds to the area A123 of the original image. In the case of the learning data with the identification number A3, the area A231 corresponds to the areas A131 and A132 of the original image. A region A232 corresponds to the region A133 of the original image.

FIG. 27 is a diagram showing a second example of learning data. FIG. 27 shows an example of learning data for learning a speckle noise removal method applied to a tomographic image when a crack in a ceramic sintered body is detected. In FIG. 27, the learning data is shown in a table format, and one row corresponds to one learning data. As described with reference to FIG. 26, each of the learning data is configured by combining an identification number, an original image, and a target image.

As described with reference to FIG. 26, the identification number is a number for identifying learning data.
As described with reference to FIG. 26, as the original image, a tomographic image before removal of speckle noise, in which the types of physical properties and optical nonuniformity of ceramics are known to the user, is used.

FIG. 27 shows an example of data for performing machine learning in the case of detecting a crack in a ceramic sintered body, and thus a tomographic image showing a crack is used as an original image.
As described with reference to FIG. 26, the background portion of the original image includes speckle noise.
In the case of the learning data with the identification number B1, the region A311 corresponds to the background portion, and the region A312 corresponds to the image region of the crack portion.

A region A312 which is an image region of the crack portion is a relatively bright region. The background image area A311 is a relatively dark area, but is slightly brighter because it includes speckle noise. Since the area A311 is slightly bright, the area A311 and the area A312 are relatively difficult to distinguish. In this respect, it is difficult to detect a crack region in the image before speckle noise removal.

In the case of the learning data with the identification number B2, the region A321 corresponds to the background portion, and the region A322 corresponds to the image region of the crack portion. In the case of the learning data with the identification number B3, the region A331 corresponds to the background portion, and the region A332 corresponds to the image region of the crack portion.

As described with reference to FIG. 26, an image obtained by removing speckle noise from the original image is used as the target image.
In the case of the learning data with the identification number B1, the area A411 corresponds to the area A311 of the original image. Since the speckle noise is removed from the background image area, the background image area is darker than before the speckle noise removal. Area A412 corresponds to area A312 of the original image. The area A412 is a relatively bright area like the area A312.
Since the speckle noise is removed from the background image area and it becomes dark, it is easy to detect the relatively bright area A412. In this regard, it is easy to detect a crack region in the image after speckle noise removal.

In the case of the learning data with the identification number B2, the area A421 corresponds to the area A321 of the original image. A region A422 corresponds to the region A322 of the original image. In the case of the learning data with the identification number B3, the area A431 corresponds to the area A331 of the original image. A region A432 corresponds to the region A332 of the original image.

The second control unit 390 performs various processes by controlling each unit of the learning device 30. The second control unit 390 is configured by a CPU (Central Processing Unit) provided in the learning device 30 reads out and executes a program from the second storage unit 380.
The learning data acquisition unit 391 acquires learning data. For example, the learning data may be received by communicating with another device storing learning data such as a user's personal computer via the second communication unit 310. Alternatively, the learning data acquisition unit 391 displays the original image on the drawing tool, and the user processes the original image into the target image so that the learning data acquisition unit 391 acquires a set of the original image and the target image. It may be. Then, the learning data acquisition unit 391 may acquire the learning data by attaching an identification number to each obtained group.

The learning data acquisition unit 391 stores the learning data in the learning data storage unit 381 for each physical property state of the ceramics and for each type of optical non-uniform state. Therefore, the learning data acquisition unit 391 may acquire the learning data classified for each physical property state of ceramics and for each type of optical non-uniform state. Alternatively, the user may specify the physical property state and the optical non-uniform state type of the ceramic for each learning data, and the learning data acquisition unit 391 may classify the learning data according to the user specification.
Alternatively, the learning data acquisition unit 391 may acquire the learning data classified for each physical property state of the ceramic, for each type of optical non-uniformity, and for each type of substance constituting the ceramic. . Alternatively, the learning data acquisition unit 391 may classify the learning data for each physical property state of the ceramic, for each type of optically non-uniform state, and for each type of substance constituting the ceramic.

The learning data acquisition unit 391 may acquire learning data obtained by specifying the type of optical nonuniformity by a method other than the method based on optical coherence tomography. For example, the user may specify the optical non-uniformity state by referring to an image photographed by a method other than a method based on optical coherence tomography, such as photographing the sample 100 using an infrared camera. Alternatively, the optical non-uniform state may be specified by the user cutting the sample 100 and visually confirming the cross section.

The machine learning unit 392 performs machine learning on the processing method in the speckle noise removal processing for each physical property state of ceramics and for each type of optical non-uniform state. The machine learning unit 392 acquires learning data from the learning data storage unit 381 for each physical property state of ceramics and for each type of optical non-uniform state, so that each physical property state and optical non-uniform state Machine learning is performed for each type. Alternatively, the machine learning unit 392 may acquire learning data for each physical property state of the ceramic, for each type of the optical non-uniform state, and for each type of substance constituting the ceramic. The machine learning unit 392 may use this learning data to perform machine learning for each physical property state, for each type of optical non-uniform state, and for each type of substance constituting the ceramic.
The machine learning algorithm used by the machine learning unit 392 is not limited to a specific one. As the machine learning algorithm used by the machine learning unit 392, various known algorithms to which learning data including an original image and a target image can be applied can be used.

FIG. 28 is a diagram illustrating an example of inconvenience of applying the same speckle noise removal processing method to all physical properties of ceramics and all optical non-uniformity.
FIG. 28 shows images of “before processing”, “after processing (preferred)”, and “after processing (unsuitable)” for each combination of the physical property state and the optical non-uniform state. The combination of the physical property state and the optical non-uniform state shown in FIG. 28 is as follows: (1) when detecting an agglomerated structure in the slurry, (2) when detecting granule traces in the molded body, (3) in the sintered body When detecting spherical defects (pores), (4) when detecting planar defects (cracks) in the sintered body.

The “before processing” image is an image before the speckle noise removal processing.
The “post-processing (preferred)” image is an image obtained by performing processing by selecting a suitable speckle noise removal processing algorithm for each physical property state and each type of optical non-uniform state.
(1) When detecting an agglomerated structure in the slurry, as processing after “processing (preferred)”, pixel values are converted to 8 bits, background luminance is averaged by background processing, and brightness and contrast are adjusted. Processing is performed in the order of.

(2) In the case of detecting granule traces in a molded product, as processing in “after processing (preferred)”, pixel values are converted into 8 bits, background luminance is averaged by background processing, and pixels are averaged by filtering. Processing is performed in the order of adjustment of brightness and contrast. In this case, the setting value for averaging the background luminance by the background processing and the setting value for adjusting the brightness and contrast are different from those in the case of detecting the aggregated structure in the slurry (1).

(3) When detecting a spherical defect in the sintered body, as processing after “processing (preferred)”, pixel values are converted to 8 bits, background luminance is averaged by background processing, and brightness and contrast are adjusted. Processing is performed in order. The processing procedure itself is the same as (1) the case where the aggregated structure in the slurry is detected, but the setting values for adjusting the brightness and contrast are different. (3) In the image after “processing (preferred)” when detecting spherical defects in the sintered body, an image of spherical defects can be extracted in a portion surrounded by a broken line.

(4) When detecting a planar defect in a sintered body, as processing after “processing (preferred)”, pixel values are converted to 8 bits, background luminance is averaged by background processing, and brightness and contrast are adjusted. Processing is performed in the order of. The processing procedure itself is the same as (1) the case where the aggregated structure in the slurry is detected, but the setting values for averaging the background luminance by the background processing and the setting values for adjusting the brightness and contrast are different.

The “after-processing (unsuitable)” image shows an image when processing is performed using a speckle noise removal processing algorithm different from the “after-processing (preferred)” case. Specifically, (3) the processing in “after processing (preferred)” when detecting spherical defects in the sintered body, (1) when detecting the aggregated structure in the slurry, (2) granule traces in the molded body When (4) is detected, (4) it applies to each of the cases where the planar defect in a sintered compact is detected. Since this process is suitable for (3) detecting spherical defects in the sintered body, the image of “after processing (unsuitable)” when detecting (3) spherical defects in the sintered body is Not shown.

(1) When detecting an agglomeration structure in the slurry, only a part of points with high luminance are extracted in “after treatment (unsuitable)”. This image is an image in which the aggregation structure is not sufficiently shown in that a point with relatively low luminance is not extracted.
(2) When detecting granule traces in a molded product, speckle noise cannot be sufficiently removed after “after treatment (unsuitable)”. This image is unclear because the speckle noise cannot be sufficiently removed, and it is difficult to determine the optical non-uniform state.

(4) When detecting a planar defect in a sintered body, extraction of a region with low luminance is insufficient after “processing (unsuitable)”. In this case, comparing the image after “Processing (preferred)” with the image after “Processing (unsuitable)”, “Image after processing (preferred)” can extract the image of the planar defect in the part surrounded by the broken line. ing. On the other hand, in “after processing (unsuitable)”, an image of a planar defect cannot be extracted in a portion surrounded by a broken line.

Thus, in order to detect the optical non-uniform state, it is necessary to select a processing method for removing speckle noise according to the physical property state of the ceramic and according to the optical non-uniform state. Therefore, the machine learning unit 392 performs machine learning for each physical property state of the ceramic and for each type of optical non-uniform state, and a processing method for each physical property state of the ceramic and for each type of optical non-uniform state. To decide.

It is also conceivable that the preferred treatment method varies depending on the ceramic raw material. On the other hand, even if it is a different raw material, if the optical characteristics such as the refractive index and the light absorption rate are the same, it is considered that it is not necessary to change the processing method for each raw material. Therefore, the machine learning unit 392 performs a machine operation for each physical property state of the ceramic, for each type of optical non-uniformity, and for each type of substance constituting the ceramic (particularly, for each type in which the substance is classified by optical characteristics). Learning may be performed to determine the processing method for each physical property state of the ceramic, for each type of optically inhomogeneous state, and for each type of substance constituting the ceramic.

The optical coherence tomography apparatus 10 may include a tomographic image generation unit 291 instead of the analysis apparatus 20. Alternatively, the tomographic image generation unit 291 may be configured as a separate device from either the optical coherence tomography apparatus 10 or the analysis apparatus 20.
The analysis device 20 and the learning device 30 may be configured as one device, such as configured using the same computer.

Next, the operation of the analysis system 1 will be described with reference to FIGS.
FIG. 29 is a flowchart illustrating an example of a procedure of processing performed by the analysis apparatus 20 when the sample 100 is analyzed.
In the process of FIG. 29, the tomographic image generation unit 291 acquires the measurement result of the sample 100 received by the first communication unit 210 from the optical coherence tomography apparatus 10, and the tomographic image of the sample 100 based on the obtained measurement result. Is generated (step S11).
Then, the analysis processing unit 292 analyzes the tomographic image obtained in step S11 (step S12).
After step S12, the analysis apparatus 20 ends the process of FIG.

FIG. 30 is a flowchart illustrating an example of a procedure of processing performed by the analysis processing unit 292 in step S12 (analysis processing) in FIG.
In the processing of FIG. 30, the analysis processing unit 292 starts a loop L1 that performs processing for each type of optical nonuniformity (step S21). Examples of types of optical non-uniformity include, but are not limited to, pores and cracks.

Next, the speckle noise removal processing unit 293 of the analysis processing unit 292 performs speckle noise removal processing on the tomographic image generated by the tomographic image generation unit 291 in step S11 of FIG. 29 (step S22). As described above, the learning device 30 determines the speckle noise removal method by performing machine learning for each physical property state of the ceramic and for each type of the optically non-uniform state. The speckle noise removal processing unit 293 includes, in the speckle noise removal method determined by the learning device 30, a physical property state of ceramics in the tomographic image to be analyzed, and an optical non-uniform state that is a treatment target in the loop L1. The speckle noise removal method corresponding to the type of the is used.
Or you may make it the learning apparatus 30 select the speckle noise removal method according to the kind of substance which comprises ceramics for every physical property state, every kind of optical nonuniform state, and ceramics. For example, the user may input the type of material constituting the ceramics to the learning device 30 and the learning device 30 may select a speckle noise removal method according to the user input.

Next, the non-uniform state detection unit 294 of the analysis processing unit 292 detects the optical non-uniform state in the sample 100 using the tomographic image after noise removal obtained in step S22 (step S23). Specifically, the non-uniform state detection unit 294 detects an optical non-uniform portion in the tomographic image after noise removal. When the optical non-uniform portion is detected, the non-uniform state detecting unit 294 determines the type of the optical non-uniform state based on the size and shape of the optical non-uniform portion.

And the analysis process part 292 performs the termination process of the loop L1 (step S24). Specifically, the analysis processing unit 292 determines whether or not the processing of the loop L1 has been performed for all types of optical nonuniformity. If it is determined that there is a type of unprocessed optical non-uniform state, the process returns to step S21, and the process of loop L1 is continued for the type of unprocessed optical non-uniform state. On the other hand, when it is determined that the process of the loop L1 has been performed for all types of optical nonuniformity, the analysis processing unit 292 ends the loop L1.
When the loop L1 is ended in step S24, the analysis processing unit 292 ends the process of FIG.

FIG. 31 is a flowchart illustrating an example of a processing procedure performed by the machine learning unit 392 of the learning device 30 when machine learning is performed on the speckle noise removal method. As described above, the learning data storage unit 381 stores learning data for each physical property state of ceramics and for each type of optical non-uniform state. The machine learning unit 392 performs the processing of FIG. 31 for each physical property state of the ceramic and for each type of optical non-uniform state, and speckles for each physical property state of the ceramic and for each type of optical non-uniform state. Determine the noise removal method.

In the process of FIG. 31, the machine learning unit 392 starts a loop L2 that performs the process for each physical property state of the ceramic (step S31).
Further, the machine learning unit 392 starts a loop L3 that performs processing for each type of optical nonuniformity (step S32).
Next, the machine learning unit 392 acquires learning data (step S33). Specifically, the machine learning unit 392 uses the learning data of the types of the physical property state of the ceramics to be processed in the loop L2 and the optical non-uniform state types to be processed in the loop L3 for learning. Read from the data storage unit 381.

Next, the machine learning unit 392 performs machine learning on the speckle noise removal method using the learning data obtained in step S33 (step S34). By this machine learning, the machine learning unit 392 removes the peckle noise in the case of the physical property state of the ceramics to be processed in the loop L2 and the optical non-uniform state type to be processed in the loop L3. To decide.

Then, the machine learning unit 392 performs a termination process of the loop L3 (step S35). Specifically, the machine learning unit 392 determines whether or not the process of the loop L3 has been performed for all types of optical nonuniformity states. If it is determined that there is a type of unprocessed optical non-uniform state, the process returns to step S32, and the processing of loop L3 is continued for the type of unprocessed optical non-uniform state. On the other hand, when it is determined that the process of the loop L3 has been performed for all types of optical nonuniformity, the machine learning unit 392 ends the loop L3.

When the loop L3 is terminated in step S35, the machine learning unit 392 performs a termination process for the loop L2 (step S36). The machine learning unit 392 determines whether or not the process of the loop L2 has been performed for all the physical properties of the ceramic. If it is determined that there is an unprocessed physical property state, the process returns to step S31, and the processing of the loop L2 is continued for the unprocessed physical property state. On the other hand, when it is determined that the process of the loop L2 has been performed for all the physical property states of the ceramics, the machine learning unit 392 ends the loop L2.
When the loop L2 is ended in step S36, the machine learning unit 392 ends the process of FIG.

Alternatively, the machine learning unit 392 may perform machine learning for each physical property state of ceramics, for each type of optical non-uniform state, and for each type of substance constituting the ceramic. Therefore, in the process of FIG. 31, the hazard learning unit 392 performs a loop for each type of substance constituting the ceramic in addition to a loop for each physical property state of the ceramic and a loop for each type of optical non-uniform state. A triple loop process may be performed.

FIG. 32 is a diagram illustrating a first example of a processing procedure in which the speckle noise removal processing unit 293 performs the speckle noise removal processing. FIG. 32 shows an example of a procedure of processing performed by the speckle noise removal processing unit 293 when detecting pores in the sintered body, for example. The speckle noise removal processing unit 293 performs the process of FIG. 32 as one of the speckle noise removal processes performed for each physical property state of the ceramic and for each type of optical non-uniform state in step S22 of FIG. .

In FIG. 32, the original image is an image before speckle noise removal. The target image is an image after speckle noise is removed.
In the process of FIG. 32, the speckle noise removal processing unit 293 performs the process A using the original image (step S41). Next, the speckle noise removal processing unit 293 performs process B using the image obtained in process A and the original image (step S42). Further, the speckle noise removal processing unit 293 performs the process C using the image obtained in the process B (step S43). Further, the speckle noise removal processing unit 293 performs the process D using the original image (step S44). And the speckle noise removal process part 293 performs the process E using the image obtained by the process C, and the image obtained by the process D (step S45). A target image is obtained by processing E.
When the machine learning unit 392 detects pores in the sintered body, the machine learning unit 392 determines the processing procedure of FIG. 32 by machine learning. The speckle noise removal processing unit 293 performs the processing of FIG. 32 according to the processing procedure determined by the machine learning unit 392.

FIG. 33 is a diagram illustrating a second example of a processing procedure in which the speckle noise removal processing unit 293 performs the speckle noise removal processing. FIG. 33 shows an example of a processing procedure in the case of a physical property state and an optical non-uniform state different from the case of FIG. 32, for example, when detecting a crack in a sintered body. The speckle noise removal processing unit 293 performs the process of FIG. 33 as one of the speckle noise removal processes performed for each physical property state of the ceramic and for each type of optical non-uniform state in step S22 of FIG. .

In FIG. 33, the original image is an image before speckle noise removal. The target image is an image after speckle noise is removed.
In the example shown in FIG. 33, the speckle noise removal processing unit 293 performs the process B using the original image (step S51), and performs the process F using the image obtained in the process B (step S52). Further, the speckle noise removal processing unit 293 performs the process C using the image obtained in the process F (step S53), and performs the process B using the image obtained in the process C (step S54). Further, the speckle noise removal processing unit 293 performs a process G using the image obtained in the process B of step S54 to obtain a target image.

FIG. 32 shows an example in which the original image is used a plurality of times, whereas FIG. 33 shows an example in which the original image is used once.
When the machine learning unit 392 detects a crack in the sintered body, the machine learning unit 392 determines the processing procedure of FIG. 33 by machine learning. The speckle noise removal processing unit 293 performs the process of FIG. 33 according to the processing procedure determined by the machine learning unit 392.

FIG. 34 is a diagram illustrating a third example of a processing procedure in which the speckle noise removal processing unit 293 performs the speckle noise removal processing. FIG. 34 shows an example of a processing procedure in the case of detecting spherical defects (pores) in the sintered body, for example, in more detail than in the case of FIG. 32 and 33 show examples of patterns assumed as processing performed by the speckle noise removal processing unit 293, whereas FIG. 34 shows more specific processing examples. The speckle noise removal processing unit 293 performs the process of FIG. 34 as one of the speckle noise removal processes performed for each physical property state of the ceramic and for each type of optical non-uniform state in step S22 of FIG. .

In FIG. 34, the original image is an image before speckle noise removal. The target image is an image after speckle noise is removed. Each square represents processing for an image.
For example, Green, Blue, and Red indicate processes for reading a green pixel value, a blue pixel value, and a red pixel value of an image, respectively. Clo (Closing) indicates a process of performing expansion by the maximum value filter and performing contraction by the minimum value filter as many times as expansion. BDA indicates binarization by a threshold value calculated by a discriminant analysis method. Ran (Range) indicates a process of outputting the maximum value-minimum value of the pixels in the 3 × 3 window centered on the target pixel for each pixel. LBW indicates processing for leaving a low filling rate (for example, less than 0.9) for the circumscribed rectangle. Ave (Average) indicates an averaging process ((f1 + f2) / 2).

The machine learning unit 392 uses a genetic algorithm (GA) and genetic programming (GP) in combination using learning data including a weight image in addition to an original image and a target image, for example. Machine learning based on the evolutionary calculation is performed, and the processing procedure of FIG. In genetic programming, the same processing as in the case of a genetic algorithm is performed on a tree in which operations are expressed in a tree structure.
The speckle noise removal processing unit 293 performs the process of FIG. 34 according to the processing procedure determined by the machine learning unit 392.
However, as described above, the machine learning algorithm used by the machine learning unit 392 is not limited to a specific one.

[Configuration of ceramic manufacturing system]
A ceramic manufacturing system may be configured using the analysis system 1.
FIG. 35 is a schematic block diagram illustrating an example of a functional configuration of the ceramic manufacturing system according to the present embodiment. In the configuration shown in FIG. 35, the ceramic manufacturing system 2 includes an analysis system 1, a preparation device 40, a molding device 50, and a sintering device 60. The preparation device 40 includes a preparation control unit 41. The molding apparatus 50 includes a molding control unit 51. The sintering apparatus 60 includes a sintering control unit 61.
The analysis system 1 shown in FIG. 35 is the same as the analysis system 1 shown in FIG.

The ceramic manufacturing system 2 manufactures ceramics.
The preparation device 40 prepares a ceramic raw material and a solvent. The preparation here is to mix the ceramic raw material and the solvent in a predetermined amount. A slurry is obtained by preparation.
The preparation control unit 41 controls the preparation by the preparation device 40. For example, the preparation control unit 41 controls the amount of raw material and solvent, the strength of mixing, and the mixing time. When it is determined in the analysis of the analysis system 1 that an optical non-uniform state has occurred during the preparation, the preparation control unit 41 controls the preparation according to the analysis result.

The molding apparatus 50 performs ceramic molding. Specifically, the molding device 50 performs molding on a dried body obtained by drying the slurry generated by the preparation device 40.
The molding control unit 51 controls molding by the molding apparatus 50. For example, when the molding apparatus 50 performs pressing on the dry body, the molding control unit 51 controls the strength and time of the press. When it is determined in the analysis of the analysis system 1 that an optical non-uniform state has occurred during molding, the preparation control unit 41 controls molding according to the analysis result.

The sintering device 60 sinters ceramics. Specifically, the sintering device 60 performs sintering on the molded body generated by the molding device 50.
The sintering control unit 61 controls sintering by the sintering apparatus 60. For example, the sintering control unit 61 controls the sintering temperature and time. When it is determined by the analysis of the analysis system 1 that an optical non-uniform state has occurred during sintering, the preparation control unit 41 controls the sintering according to the analysis result.

As described above, the ceramic production system 2 controls the production of ceramics based on the analysis result of the analysis system 1, so that it is expected that the ceramic production accuracy is improved, for example, the frequency of occurrence of pores and cracks is reduced. .
Any one or more of the preparation control unit 41, the molding control unit 51, and the sintering control unit 61 may be configured as a part of the analysis system 1. Alternatively, any one or more of the preparation control unit 41, the molding control unit 51, and the sintering control unit 61 is configured as a separate device from any of the analysis system 1, the preparation device 40, the molding device 50, and the sintering device 60. May be.

As described above, the half mirror 12 divides the light in the infrared region into the reference light and the irradiation light, and irradiates the sample 100 made of ceramics with the irradiation light. The reference mirror 13 reflects the reference light. The detector 14 detects the internal structure of the sample 100 using optical coherence tomography by detecting interference between the reference light reflected by the reference mirror 13 and the return light obtained by irradiating the ceramic with the irradiation light. To detect.
According to the optical coherence tomography apparatus 10, the structure formation process in the ceramic manufacturing process can be observed three-dimensionally in real time. Specifically, according to the optical coherence tomography apparatus 10, tomographic images of ceramics can be obtained at various depths at various steps in the ceramic manufacturing process.

The light in the infrared region may be light having a center wavelength in a range from 700 nanometers to 2000 nanometers and reflected by ceramics.
As a result, it is expected that the ceramics can be measured with high accuracy by optical coherence tomography without the light being absorbed by the ceramics.

The tomographic image generation unit 291 generates a tomographic image of each physical property state in the ceramic manufacturing process by optical coherence tomography. The analysis processing unit 292 uses the tomographic image in each physical property state to perform analysis processing to determine in which physical property state the optical non-uniform state has occurred.
According to the analysis system 1, it is possible to grasp the state of occurrence of optical nonuniformity such as pores or cracks in the ceramic manufacturing process, which can be reflected in the review of conditions in the ceramic manufacturing process.

The physical property state includes a slurry state containing the ceramic raw material in the manufacturing process, a dried state in which the material in the slurry state is dried, a molded state in which the material in the slurry state is molded after drying, and the molded state Any of the sintered states obtained by sintering the material may be used.
As a result, the analysis system 1 can not only detect the presence / absence of an optical non-uniform state in the ceramics but also obtain information on which physical property state caused the optical non-uniform state.

In addition, the speckle noise removal processing unit 293 performs a process for removing speckle noise caused by the fine particles constituting the physical property state in the tomographic image. The non-uniform state detection unit 294 determines in which physical property state an optical non-uniform state has occurred based on the shape and size of an area where the brightness is different from the other in the tomographic image after the speckle noise removal processing. To do.
The non-uniform state detection unit 294 can detect the optical non-uniform state with high accuracy in that the optical non-uniform state is detected using the tomographic image after the speckle noise removal processing.

Further, the machine learning unit 392 determines a processing method in the speckle noise removal processing based on machine learning for each physical property state.
The machine learning unit 392 determines the processing method in the speckle noise removal processing for each physical property state, so that the speckle noise removal processing unit 293 selects the speckle noise removal processing method according to the physical property state. be able to. In this respect, the speckle noise removal processing unit 293 can perform the speckle noise removal processing with high accuracy.

Further, the machine learning unit 392 determines a processing method in the speckle noise removal processing based on machine learning for each type of optical non-uniform state.
The machine learning unit 392 determines the processing method in the speckle noise removal processing for each type of optical non-uniform state, so that the speckle noise removal processing unit 293 performs speckle for each type of optical non-uniform state. Noise removal processing can be performed. Thereby, improvement of the detection accuracy of the non-uniform state by the non-uniform state detection unit 294 is expected.

In addition, the machine learning unit 392 performs machine learning based on learning data obtained by specifying the type of optical nonuniformity by a method other than the method based on optical coherence tomography.
Thereby, it is expected that the learning data can be classified with high accuracy for each type of optical non-uniformity. Since the learning data can be classified with high accuracy for each type of optical non-uniform state, it is expected that the learning accuracy of the method of removing speckle noise by the machine learning unit 392 is improved.

Further, at least one of the preparation device 40, the molding device 50, and the sintering device 60 satisfies at least one of the conditions of ceramic preparation, molding, and sintering based on the analysis result by the analysis system 1. Change.
This is expected to improve the accuracy of ceramic production, for example, by reducing the frequency of occurrence of pores and cracks.

As described above, both the analysis device 20 and the learning device 30 may be configured using a computer.
FIG. 36 is a schematic block diagram illustrating a configuration of a computer according to the embodiment.
A computer 70 shown in FIG. 36 includes a CPU 71, a main storage device 72, an auxiliary storage device 73, and an interface 74.

When the analysis device 20 is configured using the computer 70, the operation of each unit of the first control unit 290 is stored in the auxiliary storage device 73 in the form of a program. The CPU 71 reads out the program from the auxiliary storage device 73, expands it in the main storage device 72, and executes the above processing according to the program. Further, the CPU 71 secures a storage area corresponding to the first storage unit 280 in the main storage device 72 according to the program.

When the learning device 30 is configured using the computer 70, the operation of each unit of the second control unit 390 is stored in the auxiliary storage device 73 in the form of a program. The CPU 71 reads out the program from the auxiliary storage device 73, expands it in the main storage device 72, and executes the above processing according to the program. Further, the CPU 71 secures a storage area corresponding to the second storage unit 380 in the main storage device 72 according to the program.

An embodiment of the present invention is a method for observing an internal structure of a ceramic using optical coherence tomography, a step of dividing light in an infrared region into reference light and irradiation light, and a step of irradiating the ceramic with the irradiation light And observing the internal structure of the ceramic by observing interference between the reflected reference light and the return light obtained by irradiating the ceramic with the irradiation light. The present invention relates to a structure observation method.
According to this embodiment, the structure formation process in the ceramic manufacturing process can be observed three-dimensionally in real time.

DESCRIPTION OF SYMBOLS 1 Analysis system 2 Ceramic production system 10 Optical coherence tomography apparatus 11 Light source 12 Half mirror 13 Reference mirror 14 Detector 20 Analysis apparatus 30 Learning apparatus 40 Preparation apparatus 41 Preparation control part 50 Molding apparatus 51 Molding control part 60 Sintering apparatus 61 Sintering control unit 100 Sample 210 First communication unit 280 First storage unit 290 First control unit 291 Tomographic image generation unit 292 Analysis processing unit 293 Speckle noise removal processing unit 294 Non-uniform state detection unit 310 Second communication unit 380 First 2 storage unit 381 learning data storage unit 390 second control unit 391 learning data acquisition unit 392 machine learning unit

Claims

Dividing the light in the infrared region into reference light and irradiation light;
Irradiating ceramics with the irradiation light;
Observing the internal structure of the ceramic using optical coherence tomography by observing the interference between the reflected reference light and the return light obtained by irradiating the ceramic with the irradiation light. A method for observing the internal structure of ceramics.
The method for observing an internal structure of ceramics according to claim 1, wherein the light in the infrared region is light having a central wavelength in a range from 700 nanometers to 2000 nanometers and reflected by the ceramics.
Generating a tomographic image of each physical property state in the ceramic manufacturing process by the optical coherence tomography;
A step of performing analysis processing on which physical property state an optical non-uniform state is generated using tomographic images in each of the physical property states,
The method for observing the internal structure of ceramics according to claim 1 or 2.
The physical properties include a slurry state containing the ceramic raw material in the manufacturing process, a dry state in which the material in the slurry state is dried, a molded state in which the material in the slurry state is molded after drying, and a material in the molded state Among the sintered state obtained by sintering, at least any two or more,
The ceramic internal structure observation method according to claim 3.
The analysis process includes a process of removing speckle noise caused by fine particles constituting the physical property state in the tomographic image;
A step of determining in which physical property state the optically non-uniform state occurs based on the shape and size of an area having a brightness different from that in the tomographic image after the speckle noise removal processing. Including,
The method for observing the internal structure of the ceramic according to claim 3 or 4.
Further including a step of determining a processing method in the speckle noise removal processing based on machine learning for each physical property state,
The method for observing the internal structure of the ceramic according to claim 5.
Further including a step of determining a processing method in the speckle noise removal processing based on machine learning for each type of the optical non-uniformity state.
The ceramic internal structure observation method according to claim 5 or 6.
Further comprising the step of performing the machine learning based on learning data obtained by specifying the type of the optical non-uniform state by a method other than the method based on the optical coherence tomography.
The ceramic internal structure observation method according to claim 7.
A ceramic manufacturing method using optical coherence tomography,
A preparation step for preparing a slurry containing an inorganic compound as a raw material of ceramics, or granules of the inorganic compound;
A molding step of molding the slurry containing the inorganic compound or the granule into a molded body; and
A sintering step of sintering the molded body;
The light in the infrared region is divided into reference light and irradiation light, and the irradiation light is irradiated to either the slurry or the granule in the preparation step, the molded body in the molding step, or the sintered body in the sintering step. And by observing interference between the reflected reference light and the return light obtained by irradiating the slurry, the granule, the compact or the sintered body with the irradiation light, the slurry, An observation step of observing the internal structure of the granule, the molded body or the sintered body;
A method for producing ceramics comprising:
The observation step includes controlling a molding condition in the molding step or a sintering condition in the sintering step according to an observation result of an internal structure of the slurry or the granule or the molded body. The manufacturing method of ceramics as described in 2.
A tomographic image generator for generating a tomographic image of each physical property state in the ceramic manufacturing process by optical coherence tomography;
Using the tomographic image in each of the physical property states, an analysis processing unit that performs analysis processing on which physical property state causes an optical non-uniform state; and
An analysis system comprising:
An analysis system according to claim 11;
And at least one of a preparation device, a molding device, and a sintering device,
At least any one of the preparation device, the molding device, and the sintering device changes at least one of the conditions of ceramic preparation, molding, and sintering based on the analysis result by the analysis system.
Ceramic production system.