Particle analysis method, granulated powder production method, and sintered body production method

Abstract

Provided is a particle analysis method that can accurately determine a particle characteristic. This particle analysis method comprises: a step for imaging a plurality of particles to obtain a first image; a step for obtaining, on the basis of the first image, a second image in which a region that is a deformation of a particle image main section and a region other than said region have been identified at least using a prescribed threshold value; a step for obtaining, on the basis of the second image, a third image in which a region that is a deformation of a particle image has been identified; a step for obtaining, on the basis of the third image, a fourth image in which separation processing has been used to separate the region that is a deformation of the particle image; a step for obtaining, on the basis of the fourth image and the second image, a fifth image in which mask processing has been used to separate the region that is a deformation of the particle image main region; and a step for analyzing a particle characteristic on the basis of the fifth image.

Description

Method for analyzing particles, method for manufacturing granulated powder, and method for manufacturing sintered body

[0001] The embodiments described below generally relate to a method for analyzing particles, a method for manufacturing granulated powder, and a method for manufacturing a sintered body.

[0002] Analysis of particle characteristics based on an image by means such as image analysis is required in various fields such as materials and medicine. In such analysis, a method of creating an image suitable for analysis by performing image processing on a captured image of particles is known. Examples of the above method include a method of creating an image suitable for analysis by performing image processing on a TEM (transmission electron microscope) image of hollow particles as an analysis target, and evaluating various characteristics such as the porosity inside the shell.

[0003] For example, in the manufacture of granulated powder, evaluation and classification of granulated powder may be performed based on an image. In the analysis, a method using processing such as separation of particle images and noise removal for the captured image is known. Examples of the above method include a method of creating an image in which individual particle images are separated by applying image processing such as shrinkage and circular separation to an image obtained by photographing granulated powder, and accurately evaluating various characteristics such as particle size distribution and individual particle diameters.

[0004] In the analysis of particle characteristics based on an image, when using known image processing such as binarization and morphological operations, it may not be possible to accurately extract the particle characteristics of individual particles during analysis, and desirable analysis may not be possible. In particular, when using a method of analyzing based on contour information of various regions in an image, which is a general method of image analysis, an appropriate contour may be lost due to image processing, and the analysis may become greatly inaccurate. For example, when analyzing the shape of granulated powder, defects such as recessed shapes provided in the granulated powder can be found as an inner circle surrounded by the region showing the particle image after binarization of the captured image (see FIG. 1). However, when processing such as shrinkage is performed on this image to separate the region showing the particle image, the region showing the particle image is interrupted and the inner circle disappears (see FIG. 2), causing problems in determining the shape and the like.

[0005] Japanese Patent Application Laid-Open No. 2022-124469, Patent No. 3525355

[0006] The problem that this invention aims to solve is to provide a particle analysis method that can accurately determine the characteristics of particles.

[0007] The particle analysis method according to the embodiment comprises the steps of: taking images of a plurality of particles to obtain a first image; obtaining a second image based on the first image in which regions that are deformations of the main part of the particle image and other regions are identified by at least a predetermined threshold; obtaining a third image based on the second image in which regions that are deformations of the particle image are identified; obtaining a fourth image based on the third image in which regions that are deformations of the particle image are separated using separation processing; obtaining a fifth image based on the fourth image and the second image in which regions that are deformations of the main part of the particle image are separated using mask processing; and performing particle characteristics analysis based on the fifth image.

[0008] This is a schematic diagram showing an example of conventional image processing for particle images. This is a schematic diagram showing an example of conventional image processing for particle images. This is a flowchart showing an example of the procedure for the analysis method according to the first embodiment. This is a schematic diagram showing an example of image processing for particle images in the analysis method according to the first embodiment. This is a schematic diagram showing an example of image processing for particle images in the analysis method according to the first embodiment. This is a schematic diagram showing an example of image processing for particle images in the analysis method according to the first embodiment. This is a flowchart showing an example of the procedure for the analysis method according to the second embodiment. This is a flowchart showing an example of the procedure for the analysis method according to the second embodiment. This is a flowchart showing an example of the procedure for the analysis method according to the second embodiment. This is a flowchart showing an example of the procedure for the analysis method according to the third embodiment.

[0009] In this specification, image data may sometimes be simply referred to as "images."

[0010] The embodiments described below present an example of identifying regions within an image based on binarization processing. However, the inspection method according to the embodiment may identify regions within an image based on a process that converts pixel values ​​to three or more levels (multilevel conversion processing) in some or all of its steps, or it may identify regions within an image without a process that converts pixel values. In that case, regions within an image may be identified based on means such as labeling or means such as classifying pixel values.

[0011] (First Embodiment) An analysis method according to an embodiment will be described with reference to Figures 3, 4, 5, 6, and 7. The analysis method according to the embodiment can be performed using hardware such as an analytical device or a personal computer.

[0012] (S11) First, multiple particles are photographed using an image acquisition device to obtain an image (hereinafter also referred to as the first image). The image acquisition device may be selected according to the purpose, and for example, a microscope or an SEM (scanning electron microscope) can be used. The magnification of the image is arbitrary, but 30x or higher is preferred. If it is lower than 30x, it may be difficult to distinguish the sub-parts described later. There is no particular upper limit to the magnification, but 200x or lower is preferred. Measurement is possible even at high magnification, but the number of particles that can be captured in one field of view decreases, which may reduce the analytical efficiency. Next, tape (for example, double-sided tape) is attached to the petri dish. The tape should be attached with the adhesive side facing outwards. The measurement sample, which is a particle, is sprinkled on the adhesive side of the tape. Remove the measurement sample that did not stick to the adhesive side of the tape. For example, the measurement sample that did not stick to the adhesive side can be removed by turning the petri dish upside down. Measure the measurement sample that has adhered to the adhesive tape.

[0013] The first image consists of a particle image and a margin. Here, "particle image" refers to the area in the first image that represents a particle. It is not necessary to consider all areas that represent a particle as the "particle image"; for example, only the area that represents any particle selected as the object of analysis may be considered the "particle image". Here, "margin" refers to the area in the first image that represents the outside of the particle, such as the outside of the particle, the gaps between particles, or the boundaries between particles. In other words, the "margin" represents the area in the first image that does not contain particles. The particle image also consists of a main part and a secondary part. Here, "main part" refers to the area in the first image that represents the main part of the particle, and "secondary part" refers to the area in the first image that represents the secondary part of the particle. The main and secondary parts of a particle may be determined according to the purpose; for example, pores, concave shapes, defects, etc., that the particle has may be considered secondary parts, and the parts that do not fall under these categories may be considered main parts. Defects refer to cracks in particles (including granulated powder). Furthermore, particles (including granulated powder) that have pores, concave shapes, defects, etc., are sometimes called depressed spheres. Also, granulated powder that has a flattened shape may be used as the detection target. Here, "location" refers to the pixels or groups of pixels that make up the image. The first image represents the particle image and margins, as well as the main part and sub-part of the particle image, based on the differences in each location (for example, differences in brightness, other color information, height information, etc.).

[0014] Furthermore, an example of a method for determining whether a measured particle (including granulated powder) is a sunken sphere is to program it so that it is detected as a sunken sphere if the area (number of pixels) of the defective part corresponding to the inner circle in the image is greater than or equal to a certain value (arbitrary threshold). This is because, when viewed in an image, the defective part appears to be the inner part of the particle (including granulated powder). Note that if the defective part appears to be the edge of the particle (including granulated powder) when viewed in an image, it may be excluded from the measurement target as necessary.

[0015] Furthermore, the following is an example of a method for determining whether a measured particle (including granulated powder) is a flattened sphere. For example, the contour of each particle (including granulated powder) is obtained from a binarized image. Next, the smallest rectangle circumscribing the contour is detected. The width and height of the detected rectangle are compared, and if the ratio is above a certain level (for example, 1.5 or more), it can be determined to be a flattened sphere.

[0016] Detecting a defect area (number of pixels) that is a certain value (an arbitrary threshold) or the smallest rectangle can be done more efficiently by pre-setting this in the image analysis software.

[0017] The above classification of areas in the first image is used for explanatory purposes only. The classification of areas in the first image does not need to be determined solely from the first image. Images other than the first image may be used to define the "main part of the particle image," the "sub-part of the particle image," and the "marginal area." Furthermore, the classification of areas in the first image may be determined according to the purpose of the analysis. For example, an area corresponding to a particle pore may be considered the "sub-part of the particle image" or the "marginal area." Also, the particle image and marginal area, as well as the main part and sub-part of the particle image, do not necessarily need to perfectly represent the actual state and may contain noise or errors. In addition, one area may belong to multiple different types in the classification. It is sufficient to unify the judgment criteria within the first image.

[0018] The number of particles in the first image is not particularly limited, but may be, for example, 10 or more, 100 or more, or 400 or more. There is no particular upper limit to the number of particles, but 1500 or less is preferred. A large number of particles may increase the analysis time. Also, when the particle size is 200 μm or less, a method of measuring by dividing the measurement area to be captured at once into 500 μm x 500 μm sections is also possible. In this way, the measurement area can be changed according to the particle size and the number of particles.

[0019] Images obtained based on the first image, whose represented object and its appearance are the same as those of the first image, will hereafter be referred to as processed images. For example, the second, third, fourth, and fifth images described later are processed images. Processed images do not necessarily have to perfectly represent the actual appearance and may contain noise, errors, etc.

[0020] (S21) Next, based on the first image, a processed image (hereinafter also referred to as the second image) is created in which the region that is a deformation of the main part of the particle image and the other regions are identified. Here, "the region that is a deformation of the main part of the particle image" refers to the area in the processed image that is considered to correspond to the main part of the particle image in the first image.

[0021] For example, a second image can be obtained by processing a first captured image at a predetermined threshold. Processing at a predetermined threshold can include binarization. When binarization is used to create the second image, for example, processing using a predetermined threshold can be used. Alternatively, preprocessing such as grayscale conversion, luminance conversion, or noise reduction may be performed prior to binarization. Luminance conversion refers to processes such as shrinking and dilating of grayscale images, and multi-level conversion.

[0022] The second image is a binary image in which, for example, the region that is a deformation of the main part of the particle image is represented by pixels or groups thereof with a "white" value (hereinafter also called the "foreground"), and the other regions, i.e., the parts that are considered to correspond to the secondary part of the particle image or the margins in the first image, are represented by pixels or groups thereof with a "black" value (hereinafter also called the "background"). An example of the second image is shown in Figure 4.

[0023] (S22) Next, based on the second image, a processed image (hereinafter also referred to as the third image) is created in which regions that are deformations of the particle image are identified. Here, "regions that are deformations of the particle image" refers to the parts of the processed image that are considered to correspond to the particle image in the first image.

[0024] For example, for the second image, contour extraction is performed on the outermost contour surrounding the region that is a deformation of the main part of the particle image. Then, a new processed image is created in which the inside of the extracted contour is filled with a single value. In this way, the third image can be created. For example, when using an image that has been trinarized or higher, the image should be filled with a value of one or more. For example, in the case of 256 levels of brightness, in addition to brightness values ​​of 0 and 255, one method of filling the image with a value between 1 and 254 can be used.

[0025] The third image is a binary image in which, for example, a region representing a deformation of the particle image is the foreground, and the rest of the image is the background. An example of the third image is shown in Figure 5.

[0026] (S23) Next, based on the third image, a processed image (hereinafter also called the fourth image) is created in which regions that are deformations of the particle image are separated using separation processing. Here, region separation refers to the operation of separating connected parts of regions using pixel value conversion, labeling, etc. Separation processing is used, for example, to determine the boundaries between multiple objects whose images overlap and to identify them as deformations of the images of multiple objects.

[0027] For example, a fourth image can be obtained by applying an erosion process to a region of the third image that represents a deformation of the particle image. In this case, the erosion process is used as a separation process. Other separation processes, such as circular separation or watershed, may also be used.

[0028] It is often preferable that the number of regions formed by the separation of deformations in the particle image matches the number of particle images in the first image. However, due to reasons such as significant particle overlap during imaging, the cost of matching the two may be considerable, so complete separation is not always necessary. The precision of separation can be set according to the purpose. For example, the ratio of the number of particles obtained from the number of separated regions to the actual number of particles may be 80% to 120%, 90% to 110%, or 97% to 103%. The actual number of particles obtained from the first image is used as the denominator.

[0029] The fourth image is a binary image in which, for example, a region formed by the separation of a deformed particle image is represented as the foreground, and the remaining region as the background. An example of the fourth image is shown in Figure 6.

[0030] (S24) Next, based on the fourth image and the second image, a processed image (hereinafter also referred to as the fifth image) is created in which the region that is the deformation of the main part of the particle image is separated using mask processing.

[0031] For example, a fifth image can be obtained by applying a masking process to a second image using a fourth image. Here, masking refers to a process that manipulates the value of a certain pixel in one image based on another image, for example, using bitwise operations or similar means. An example of this is as follows: In two binary images, each consisting of a first value and a second value, a logical AND operation is performed on the first value between a pixel at a certain position in one binary image (original image) and a pixel at the same position in the other binary image (mask). Then, the original image is rewritten so that only the pixels at the positions where the logical AND is true have the first value. Here, for example, the above example is referred to as masking.

[0032] The second image is a binary image obtained by binarizing the first image. The fourth image is obtained by applying separation processing to the third image (a processed image in which regions representing deformations of the particle image are identified based on the second image). The "position where the logical AND is true" refers to the region where the product of the binarized values ​​results in an integer. For example, binarization allows for the identification of values ​​0 and 1. When two binary images consisting of the first and second values ​​are superimposed, and the values ​​of the overlapping region are multiplied together, the regions of 0 and 1 can be distinguished. This region of 1 is the position where the logical AND is true. Furthermore, when the second image is trinarized, the position where the logical AND is true is the region where the result of the multiplication is 1 and 2.

[0033] The fifth image is a binary image in which, for example, only the areas that are both foregrounds in the second image and the fourth image shown above are foregrounds, and the rest of the image is background. In this example, the fifth image is a binary image in which the region formed by separating the candidate area for the main part of the particle image is represented as the foreground, and the rest of the image is represented as the background. An example of the fifth image is shown in Figure 7.

[0034] (S41) Next, the particle characteristics are analyzed based on the fifth image described above.

[0035] For analyzing particle properties, methods such as image analysis software or algorithms may be used.

[0036] For example, the number of particles with a secondary portion and other particles can be determined by determining the number of outer contours of the region that is a deformation of the main part of the particle image and the number of inner contours of the region that is a deformation of the main part of the particle image (preferably the contour of the region that is a deformation of the secondary part of the particle image). In this case, the accuracy of the determination can be set according to the purpose. For example, the ratio of the number of particles with a secondary portion determined from the number of inner contours of the region that is a deformation of the main part of the particle image to the actual number of particles may be 80% or more and 120% or less, 90% or more and 110% or less, or 97% or more and 103% or less. The actual number of particles obtained from the first image is used as the denominator.

[0037] The fifth image separates the regions representing individual particles, and in addition, the loss of information contained in the particle image is suppressed during its creation. By analyzing the particle characteristics based on the fifth image, the number of particles, the presence or absence of sub-parts, shape, etc., can be determined more accurately.

[0038] The analysis method of this embodiment may be appropriately modified based on the above method. For example, instead of a single captured image, a plurality of captured images with the same represented object and its state may be used. Also, in this specification, descriptions such as "based on the first image" indicating that a certain operation is based on a certain image refer to using the image for the operation or using an image obtained by performing an operation on the image for the operation. For each of the above steps of the analysis method of the embodiment, in addition to the described operations, further processing such as noise removal and other filtering processes may be performed. Also, when an area that is a deformation of the main part of the particle image and other areas are distinguished in the captured image, step S21 may also serve as step S22. Also, prior to operations on the image, etc., duplication, composition, etc. of the image may be performed.

[0039] In the analysis method of this embodiment, the particles may be, for example, granulated powder, may be ceramic granulated powder, or may be silicon nitride granulated powder.

[0040] (Second Embodiment) The particle analysis method of the embodiment may include a plurality of analysis processes. An example of this, the second embodiment, is shown below.

[0041] The analysis method according to the second embodiment will be described with reference to FIGS. 8, 9, and 10.

[0042] The second embodiment is an example of the first embodiment. Here, only the points where the second embodiment is limited from the first embodiment will be described. For other points, the description regarding the first embodiment will be incorporated.

[0043] The analysis method of the second embodiment includes a step S1 of performing a first process and a step S2 of performing a second process. The first process performs at least analysis regarding the sub-part of the particle image. The second process may perform, for example, analysis of the number of particles. The order of step S1 and step S2 is not particularly limited.

[0044] (S1) Following the capture of the particle image (the first image), the first process is performed. Similar to the first embodiment, the first process performs steps S21, S22, S23, S24, and performs analysis (step S42: image analysis) based on the fifth image obtained after step S24.

[0045] Steps S21, S22, S23, S24, and S42 may create a processed image such that detection of a location where the secondary part of the particle image is deformed is advantageous in the analysis. For example, the binarization method and its parameters in step S21 and the separation method and its parameters in step S23 may be selected. Step S42 may perform the analysis in the same manner as step S41. Alternatively, step S42 may perform the analysis such that detection of a location where the secondary part of the particle image is deformed is advantageous.

[0046] (S2) In addition to the first process, a second process is performed. The content of the second process is not particularly limited. For example, it includes steps S31, S32, and S43. Based on the second image, step S31 creates a processed image (hereinafter also referred to as the sixth image) in which a region where the main part of the particle image is deformed and other regions are discriminated. The sixth image may be an image obtained by subjecting the second image to a shrinking process. Step S32 performs a separation process on the sixth image or an image similar thereto to create a processed image (hereinafter also referred to as the seventh image) in which a region where the main part of the particle image is deformed is separated. Based on the seventh image, step S43 performs an analysis (image analysis).

[0047] Steps S31 and S32 may create a processed image such that determination regarding the number of particles is advantageous in the analysis. For example, the binarization method, the separation method, and their parameters may be preferentially selected for separating regions. Step S43 may perform the analysis in the same manner as step S41. Alternatively, step S43 may perform the analysis such that detection of a location where the main part of the particle image is deformed is advantageous.

[0048] For example, when the overlap of particles is large and accurate analysis cannot be sufficiently performed by image analysis on a single processed image, in the above-described embodiment, by performing the first process and the second process respectively, it is possible to accurately perform both the analysis of the characteristics regarding the secondary part of the particles and the analysis of the number of particles.

[0049] (Third Embodiment) The method for manufacturing granulated powder according to the embodiment includes a step of inspecting the granulated powder using the method for analyzing particles according to the embodiment. The method for manufacturing a sintered body according to the embodiment includes each step of the method for manufacturing granulated powder according to the embodiment. A third embodiment, which is an example thereof, is shown below.

[0050] The third embodiment of the method for producing granulated powder and the method for producing a sintered body are, for example, a method for producing ceramic granulated powder and a method for producing a ceramic sintered body.

[0051] A method for producing granulated powder and a method for producing a sintered body according to the third embodiment will be described with reference to Figure 11.

[0052] The third embodiment of the method for producing granulated powder includes a step of blending raw materials (blending step) S101, a step of crushing and mixing the blended raw materials (mixing step) S102, a step of granulating the mixture (granulation step) S103, and a step of inspecting the granulated powder (granulated powder inspection step) S104.

[0053] In addition, the third embodiment of the method for manufacturing a sintered body includes a step S201 of molding and sintering granulated powder, and a step S202 of inspecting the sintered body.

[0054] (Step S101) The method for producing granulated powder according to the third embodiment begins by performing step S101.

[0055] In process S101, raw materials are prepared according to the purpose. For example, if the purpose is to manufacture a sintered body, the main raw materials and sintering aids are prepared. The types of raw materials may be selected according to the type of granulated powder to be manufactured. The main raw material is the component that is most abundant in the ceramic sintered body (main component). Examples of main raw materials include silicon nitride, aluminum nitride, aluminum oxide, zirconium oxide, and silicon carbide. Examples of sintering aids include rare earth compounds, aluminum compounds, magnesium compounds, titanium compounds, zirconium compounds, and hafnium compounds.

[0056] (Step S102) The method for producing granulated powder according to the third embodiment then proceeds to step S102.

[0057] In step S102, the prepared raw materials are mixed. At this time, crushing equipment such as a ball mill or bead mill may be used to crush and mix the raw materials. Either a wet or dry mixing method may be used. Additives such as binders and dispersants may also be added to the raw materials before mixing.

[0058] (Step S103) The method for producing granulated powder according to the third embodiment then proceeds to step S103.

[0059] In step S103, granulation is performed on a mixture of various raw materials to form granulated powder. Granulation may be performed using known means such as a spray dryer or other granulation equipment. The average particle size of the granulated powder is preferably 30 μm or larger. The larger the particle size of the granulated powder, the better the molding efficiency. Molding efficiency refers to the fluidity of the granulated powder and the efficiency of filling into the mold. There is no particular upper limit to the average particle size of the granulated powder, but it is preferably 10 mm or less. If the granulated powder is too large, problems such as difficulty in filling the mold may occur. For this reason, the average particle size of the granulated powder is preferably within the range of 30 μm to 10 mm, and more preferably within the range of 50 μm to 2 mm. The average particle size of the granulated powder may be measured using the first image.

[0060] (Step S104) The method for producing granulated powder according to the third embodiment then proceeds to step S104. In step S104, the granulated powder is inspected using the following inspection method.

[0061] Based on the inspection, the granulated powder may be recovered or reused. For example, the inspection method according to the embodiment may be used to inspect each lot for abnormal particles in the granulated powder, and if the proportion of abnormal particles is above a certain level, the granulated powder of the corresponding lot may be recovered and reused as a raw material in step S101. By inspecting the granulated powder and recovering and reusing the granulated powder that is judged to be defective based on the inspection, the yield can be improved by appropriately processing the defective granulated powder, and in addition, the quality of the granulated powder can be controlled more accurately. As mentioned above, if the average particle size of the granulated powder is increased, the granulated powder may become depressed spheres with holes, concave shapes, defects, etc. When molding using depressed spheres, variations in the molding density of the molded body occur. Furthermore, appearance defects and insufficient density may occur in the ceramic sintered body. For this reason, it is preferable to apply the inspection method according to the embodiment. In addition, it is preferable to use the above inspection method in the manufacturing method of silicon nitride sintered bodies. Silicon nitride is expensive among other main materials, and the effect of improving yield is significant.

[0062] Based on the inspection, feedback on the inspection results may be provided. For example, the above inspection method can be used to inspect each lot for abnormal particles in the granulated powder, and the inspection results can be compared with the manufacturing conditions such as the blending content and processing conditions in processes S101, S102, S103, etc. of the corresponding lot. By doing so, the manufacturing conditions such as the blending content and processing conditions in processes S101, S102, S103, etc., when newly manufacturing granulated powder may be adjusted. Alternatively, the combination of manufacturing conditions and inspection results can be recorded for a certain period, and the trends can be analyzed to identify areas for improvement in the manufacturing conditions. For example, the humidity inside the processing room can be measured when the yield is good and when it is bad. One method is to manage the humidity so that it matches the humidity when the yield is good.

[0063] As described below, this feedback may also refer to other information. For example, if the purpose is to manufacture a sintered body, the results of the sintered body inspection may also be referred to. By inspecting the granulated powder using the inspection method according to the embodiment and providing feedback on the obtained inspection results, the relationship between the manufacturing conditions and the properties of the granulated powder can be appropriately estimated, and the manufacturing process can be improved to obtain the desired granulated powder.

[0064] (Step S201) The method for manufacturing a sintered body according to the third embodiment then proceeds to step S201.

[0065] In step S201, the granulated powder is first molded. This molding may be carried out using known methods such as die pressing, cold isostatic pressing (CIP), rolling granulation, or injection molding.

[0066] In step S201, molding or other sintering is performed. In this case, sintering may be carried out using known means such as a sintering furnace.

[0067] (Step S202) The method for manufacturing a sintered body according to the third embodiment then proceeds to step S202.

[0068] In step S202, the sintered body is inspected. The inspection may be carried out according to the purpose, for example, the sintered body may be inspected for defects, appearance, density, strength, and other properties.

[0069] Similar to step 104, feedback on inspection results may be provided in this step based on the inspection. Also, as mentioned above, feedback may be provided by referring to both the inspection results in this step and the inspection results in step S104. For example, the inspection results of both may be compared with the manufacturing conditions to improve or analyze the manufacturing conditions, such as adjusting the raw material blend. By performing inspections of the sintered body and the granulated powder using the inspection method according to the embodiment, and providing feedback on the inspection results obtained from both, it is possible to appropriately estimate the relationship between the manufacturing conditions, the characteristics of the granulated powder, and the characteristics of the sintered body, and improve the manufacturing process so that desirable granulated powder can be obtained.

[0070] As an example, indentation spheres were detected in granulated powder produced during the manufacturing of silicon nitride sintered bodies.

[0071] Here, "depressed spheres" refers to granulated powder in which depressions (such as concave shapes) are present in the spheres. Ceramic granulated powder produced during the manufacture of silicon nitride sintered bodies and the like is usually spherical, but some is abnormally granulated and becomes depressed spheres. Furthermore, these depressed spheres can be a cause of defects such as segregation. Therefore, in the manufacture of silicon nitride sintered bodies, it is necessary to detect depressed spheres in the granulated powder and determine the quality of the granulated powder based on the results.

[0072] (Examples 1-3) First, granulated powder consisting of silicon nitride powder, a sintering aid, and a binder was prepared. Next, samples were taken from the granulated powder, and particle images were captured at a magnification of 77.6x using a digital microscope (3R Solution Co., Ltd. 3R-MSUSB390).

[0073] Based on the captured particle images, the number of sunken spheres was counted using the inspection method according to the embodiment. Binarization was used to create the second image, contour extraction and fill processes were used to create the third image, shrinkage was used to create the fourth image, and masking using logical AND was used to create the fifth image. For the fifth image, the number of sunken spheres was determined by determining the number of contours outside the region that is a deformation of the main part of the particle image and the number of contours inside the region that is a deformation of the main part of the particle image. A custom program based on the image processing library OpenCV was used for image creation and determination. The results are shown in Table 1, compared with the actual number of sunken spheres based on visual counting.

[0074] (Comparative Example 1) As a comparative example, the number of sunken spheres was counted using a different method on the particle image used in Example 1. A binary image was created under the same conditions as in the example, and then the binary image was separated to create a processed image in which the region representing deformation of the main part of the particle image was separated, which was used as the object of judgment. The number of sunken spheres was determined on this image under the same conditions as in Examples 1 to 3. A custom program based on the image processing library OpenCV was used for image creation and judgment. The results are shown in Table 1, compared with the actual number of sunken spheres based on visual counting.

[0075]

[0076] Examples 1 to 3 of the analysis method of the embodiment demonstrated that sinkholes could be detected with high accuracy. Furthermore, the error between the total number of particles counted visually in the first image and the actual number of particles counted visually was within 10%. In addition, the inspection method of the embodiment significantly reduced the inspection time compared to visual inspection.

[0077] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

Claims

1. A method for analyzing particles, comprising:

1. The steps of: photographing multiple particles to obtain a first image; 2. Based on the first image, obtaining a second image in which regions representing deformation of the main part of the particle image and other regions are identified by at least a predetermined threshold; 3. Based on the second image, obtaining a third image in which regions representing deformation of the particle image are identified; 4. Based on the third image, obtaining a fourth image in which regions representing deformation of the particle image are separated using a separation process; 5. Based on the fourth image and the second image, obtaining a fifth image in which regions representing deformation of the main part of the particle image are separated using a mask process; and 6. Analyzing particle characteristics based on the fifth image.

2. A method for analyzing particles according to claim 1, comprising the steps of: obtaining a seventh image in which a region that is the main part of the particle image is separated using a separation process based on the second image; and determining the number of particles based on the seventh image.

3. The particle analysis method according to claim 1 or claim 2, wherein the second image is obtained by binaryly identifying a region that is a deformation of the main part of the particle image and other regions based on the first image using a predetermined threshold value.

4. The method for analyzing particles according to any one of claims 1 to 3, wherein the plurality of particles are ceramic granulated powder.

5. The method for analyzing particles according to claim 4, wherein the ceramic granulated powder mainly consists of silicon nitride.

6. A method for producing granulated powder, comprising: a blending step of blending raw materials; a granulation step of granulating a mixture formed by mixing a plurality of raw materials including the blended raw materials to form granulated powder; and a granulated powder inspection step of inspecting the granulated powder, wherein the granulated powder inspection step is performed using the particle analysis method described in any one of claims 1 to 5.

7. The method for producing granulated powder according to claim 6, wherein granulated powder deemed to be defective in the granulated powder inspection step is used as the raw material in the blending step.

8. The method for producing granulated powder according to claim 6, wherein the blending of the raw materials in the blending step is adjusted based on the results of the granulated powder inspection step.

9. A method for manufacturing a sintered body, comprising: a blending step of blending raw materials; a granulation step of granulating a mixture formed by mixing a plurality of raw materials including the blended raw materials to form granulated powder; a granulated powder inspection step of inspecting the granulated powder; and a molding and sintering step of molding and sintering the granulated powder to obtain a sintered body, wherein the granulated powder inspection step is performed using the particle analysis method described in any one of claims 1 to 5.

10. The method for manufacturing a sintered body according to claim 9, wherein granulated powder deemed to be defective in the granulated powder inspection step is used as the raw material in the blending step.

11. A method for manufacturing a sintered body according to claim 9, wherein the blending of the raw materials in the blending step is adjusted based on the results of the granulated powder inspection step.

12. A method for manufacturing a sintered body according to claim 9, comprising a sintered body inspection step for inspecting the sintered body, and adjusting the blending of the raw materials in the blending step based on the results of the granulated powder inspection step and the sintered body inspection step.

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