A method and system for integrated characterization of ceramic microstructure

Abstract

The application discloses a kind of integrated characterization method and system for ceramic microstructure, comprising: sampling and cutting of the ceramic sample to be measured;Metallographic grinding and surface polishing processing of the ceramic sample to be measured;Positioning mark of the ceramic sample to be measured;High-precision collection of surface three-dimensional topography height information of the ceramic sample to be measured region;Surface three-dimensional topography height information result filtering analysis;Surface three-dimensional topography height information display setting;Surface three-dimensional topography height information image recognition;Microstructure quantitative analysis based on image recognition result;Analysis result display obtains different microstructure quantitative analysis.The application is based on the collection of surface three-dimensional topography height information of the metallographic polishing surface of ceramic material, combined with filtering analysis and image recognition software and quantitative analysis algorithm, to obtain the integrated quantitative analysis of ceramic and ceramic matrix composite microstructure based on three-dimensional surface topography height information collection and image recognition analysis.

Description

Technical Field

[0001] This invention relates to the field of microstructure testing and characterization of ceramic materials, and more specifically, to an integrated characterization method and system for ceramic microstructure. Background Technology

[0002] Ceramic materials are a class of inorganic non-metallic materials typically formed from natural or synthetic compounds through processes such as high-temperature sintering, photocuring, liquid-solid phase transformation, and machining. Ceramic materials are often bonded by covalent and ionic bonds, generally have high melting points, and possess good mechanical properties. Furthermore, based on their various structural and functional characteristics, such as oxidation resistance, high melting point, ultra-high hardness, optical properties, and piezoelectric properties, they are widely used as important structural and functional materials in fields such as aerospace, nuclear industry, electronic communications, and biomedicine. For example, silicon carbide-based ceramics, due to their high strength, high hardness, oxidation resistance, and wear resistance, are widely used in high-temperature structural materials, wear-resistant materials, corrosion-resistant materials, and high-temperature heat exchange materials. Boron carbide-based ceramics, due to their ultra-high hardness, are used in armor materials for vehicles, ships, and helicopters, as well as lightweight bulletproof vests for personnel. Aluminum nitride-based ceramics, due to their high thermal conductivity, insulation properties, and functional characteristics, are used in integrated circuit heat sinks, luminescent materials, and substrate materials. Cubic boron nitride ceramics, with their high hardness and wear resistance, are used in cutting tools, grinding and polishing materials, etc. For scientific and engineering problems related to the design, modification, and fundamental research of ceramic materials, studying the microstructure of ceramic-based materials is extremely important. Developing methods for characterizing the microstructure of ceramic materials is a crucial approach to establishing the correlation between the microstructure and performance of ceramic-based materials.

[0003] Current methods for observing and characterizing the microstructure of ceramic materials include optical microscopy, scanning electron microscopy, transmission electron microscopy, and scanning transmission microscopy. With the increasing demand for rapid materials development, developing testing and measurement methods based on rapid development has become an important way to improve testing efficiency, shorten the R&D cycle, and reduce R&D costs. Currently, various characterization methods for the microstructure of materials have been established, such as quantitative identification and characterization methods combining scanning electron microscopy microstructure acquisition and image recognition quantitative analysis, and material microstructure identification and characterization methods combining optical microscopy microstructure acquisition and image recognition quantitative analysis. For ceramic materials, optical microscopy offers the advantages of direct and convenient metallographic observation. However, for some ceramics and ceramic matrix composites (such as B4C-SiC eutectic ceramic self-generated composites), the low optical intensity contrast makes clear observation of the microstructure difficult, or even impossible, thus affecting the efficiency of observation and characterization. Scanning electron microscopy (SEM) offers high resolution and faster acquisition speed than conventional SEM; however, for large-sized bulk ceramics, the acquisition time is too long and the data volume too large, making it suitable for fine characterization. Furthermore, the high-energy focused electron beam can cause micro-damage to the surface of the material being tested. To address the requirement for faster analytical characterization with higher efficiency, combining the acquisition of three-dimensional morphology of polished ceramic surfaces, the recognition of surface three-dimensional morphology images, and quantitative analysis can achieve rapid, efficient, non-destructive, and quantitative characterization of the microstructure of ceramic materials. Summary of the Invention

[0004] To address the aforementioned issues, the present invention aims to provide an integrated characterization method and system for ceramic microstructure. Based on conventional mechanical polishing of ceramic materials, and taking into account the differences in wear resistance response of different constituent phases, a white light interferometer is used to obtain the three-dimensional height information of the metallographic polished surface. On this basis, combined with surface three-dimensional information filtering analysis, image recognition, and quantitative analysis, an integrated quantitative analysis and characterization of the microstructure of the ceramic material in the corresponding region is obtained.

[0005] To achieve the above-mentioned technical objectives, the present invention provides an integrated characterization method for ceramic microstructures, comprising the following steps:

[0006] Prepare ceramic material samples and obtain polished surfaces for generating ceramic microstructures;

[0007] After marking the location of the test area on the polished surface, the three-dimensional morphology height information of the surface is collected.

[0008] The collected results are preprocessed and height-labeled to generate a test area image;

[0009] According to the needs of image recognition, select appropriate image recognition technology to identify and quantitatively analyze the features of target images. For example: (1) Use the threshold segmentation method to adjust the contrast of the test area image and display the phase separation, and perform multi-parameter analysis of the microstructure of the phase separation result to characterize the microstructure of ceramics; (2) Use the machine learning method to prepare, train, process, and quantitatively analyze the microstructure of ceramics and ceramic matrix composites by preparing, training, processing, and statistically analyzing the regional image data.

[0010] Preferably, during the preparation of ceramic material samples, sampling rules are set according to the shape, size, test area, and test direction of the ceramic material;

[0011] Based on the sampling rules, and according to the hardness and conductivity of the ceramic material, the cutting method, cutting feed rate, cutting path, etc. are selected to prepare ceramic material samples.

[0012] Preferably, during the process of generating a polished surface, appropriate abrasive paper and polishing liquid type and grade are selected according to the characteristics of the ceramic material to be tested, and the surface of the ceramic material sample is polished to generate a polished surface.

[0013] Preferably, during the acquisition of surface three-dimensional topography height information, an image with interference fringes is obtained by adjusting the Z-axis;

[0014] By adjusting the brightness and relative tilt of the image, the height information of the three-dimensional surface topography is acquired. The vertical scanning interference acquires 0 to 15 fringes, while the phase shift interference acquires less than 5 fringes.

[0015] Preferably, during the preprocessing of the acquired results, data processing methods such as Gaussian filtering and statistical filtering are selected according to actual needs to perform filtering analysis on the acquired results. By retaining high-frequency signals, the acquired results are preprocessed.

[0016] Preferably, during the process of height annotation of the preprocessed acquisition results, the preprocessed acquisition results are leveled to obtain the leveled height information, and the height display range is set to generate a test area image.

[0017] Preferably, during the process of performing multi-parameter analysis of the feature structure, the statistical distribution patterns of area fraction, equivalent diameter, roughness, perimeter, phase size, and microstructure corresponding to different layers are obtained, and multi-parameter analysis of the feature structure is performed.

[0018] Preferably, in this invention, an appropriate image recognition technology is selected according to actual needs, including conventional image recognition technology represented by threshold segmentation, machine learning image recognition technology represented by deep learning, etc.

[0019] This invention also discloses an integrated characterization system for ceramic microstructures, comprising:

[0020] The data acquisition module is used to obtain the polished surface for generating ceramic microstructure by preparing ceramic material samples, and to collect the three-dimensional morphology height information of the surface after marking the test area of ​​the polished surface.

[0021] The image processing module is used to preprocess and height-annotate the acquired results to generate test area images;

[0022] The characterization module is used to identify and quantitatively analyze the generated images using image recognition technology. Depending on the actual needs, it can use traditional threshold segmentation image recognition technology or machine learning-based image recognition technology to perform multi-parameter analysis of the feature structure of the microstructure and characterize the ceramic microstructure.

[0023] The present invention discloses the following technical effects:

[0024] This invention combines the differences in mechanical grinding response of ceramic material constituent phases, three-dimensional surface information acquisition, image filtering analysis—image recognition—quantitative analysis of image recognition feature structure, to achieve integrated characterization, identification, and quantitative analysis of ceramic material microstructure. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is an actual flowchart of the method described in this invention;

[0027] Figure 2 The results of observation under an optical microscope are for area 1 to be observed.

[0028] Figure 3 This is a contour map of the three-dimensional information of white light interference in the observed area 1 of the polished surface described in this invention;

[0029] Figure 4 According to Figure 3 White light interference three-dimensional information difference composition phase image recognition results;

[0030] Figure 5 The results show the size distribution of the constituent phases in observation area 1, which are obtained through quantitative analysis based on image recognition.

[0031] Figure 6The results are from optical microscopy observations of the micro-area 2 to be observed.

[0032] Figure 7 The white light interference three-dimensional information contour map of the observed area 2 of the polished surface described in this invention;

[0033] Figure 8 According to Figure 7 White light interference three-dimensional information difference composition phase image recognition results;

[0034] Figure 9 The results of quantitative analysis of the phase size distribution in observation area 2 based on image recognition results. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0036] like Figures 1-9 As shown, the present invention provides an integrated characterization method for ceramic microstructure, which achieves the characterization of ceramic microstructure through three-dimensional information acquisition and image recognition, specifically including the following steps:

[0037] S1, set ceramic sampling rules and cutting methods, and sample and cut ceramic materials;

[0038] S2, using a fully automatic metallographic grinding and polishing machine to perform metallographic grinding and polishing on ceramic samples;

[0039] S3, using fully automated microhardness testers or nanoindenters and other instrumented indentation devices to mark the location of the sample test area.

[0040] S4. Using a white light interferometer to collect surface three-dimensional topography height information of the region to be characterized;

[0041] S5 utilizes commercial data processing software to perform post-processing and filtering analysis on the collected results;

[0042] S6 sets the Z-axis display height range to enable selection display and image saving;

[0043] S7. As needed, select appropriate image recognition technology and use image recognition analysis software (or self-written program) to recognize the target image;

[0044] S8 utilizes image recognition and analysis software and commercial mathematical software to perform multi-parameter analysis of the characteristic structure of the microstructure of the phase separation results;

[0045] S9 displays the characterization results of the microstructure;

[0046] In step S1, the sampling rules and cutting methods are set, and the ceramic material is sampled and cut, specifically including:

[0047] Sampling rules are set according to the shape, size, and testing requirements (test area, orientation) of the ceramic material;

[0048] Based on the hardness and conductivity of the ceramic material, select the cutting method (diamond wire cutting, metal wire cutting), cutting feed rate, cutting path, etc.

[0049] Furthermore, in step S2, the ceramic sample is subjected to metallographic grinding and polishing using a fully automatic metallographic grinding and polishing machine, specifically including:

[0050] Based on the material's hardness and other properties, select the appropriate type and grade of sandpaper (e.g., silicon carbide sandpaper, 100#~2000#) and the type and grade of polishing slurry (e.g., silicon dioxide polishing slurry, 0.5μm~2μm).

[0051] Set the rotation speed (50rpm-300rpm) and grinding process of the fully automatic metallographic grinding and polishing machine;

[0052] Ceramic samples need to have a mirror-like surface, be smooth, and show no obvious scratches when observed under an optical microscope.

[0053] In step S3, the position of the test area of ​​the ceramic sample is marked using an instrumented indentation device such as a fully automated microhardness tester or a nanoindenter. Specifically, this includes:

[0054] Select an appropriate load based on the hardness of the ceramic material and the size of the test area;

[0055] Based on the shape and size of the test area, set an appropriate instrumentation pressing sequence and interval;

[0056] In step S4, a white light interferometer is used to acquire the three-dimensional surface topography of the region to be characterized, specifically including:

[0057] Turn on the white light interferometer 3D profilometer and open the Vision64 software. Place the sample on the sample stage and move it until the area to be measured appears in the field of view. Adjust the system focus by raising or lowering the z-axis until interference fringes appear on the sample surface. Adjust the brightness through the Intensity control window, ensuring that no red pixels appear on the image and that the image display is not too dark. Adjust the relative tilt between the ceramic sample and the reference mirror (Tip / tilt knob) to ensure that the fringes are visible. By adjusting the tilt, achieve 0-15 visible fringes for VSI measurement (vertical scanning interferometry mode) and less than 5 fringes for PSI measurement (phase shift interferometry mode). Select the measurement parameters and complete the measurement. Enter the Measurement Options page to set the Measurement Type, Magnification (Objective, Multiplier), Speed, Backscan, Length, Threshold, etc.

[0058] In step S5, commercial data processing software is used to perform post-processing and filtering analysis on the collected results according to specific needs, specifically including:

[0059] Using Vision64 commercial data analysis software, open the collected results file with the .OPDX extension, and then level the sample;

[0060] As needed, select processing methods such as Gaussian filtering and statistical filtering to perform filtering analysis on the sample, retain high-frequency signals, filter low-frequency signals, and save the results after filtering analysis.

[0061] In step S6, the Z-axis display height range is set to achieve selection display and image saving, specifically including:

[0062] Open the filter analysis results, level the results, and then set an appropriate Z-axis height display range based on the statistical distribution results of the leveled height information. This enables selective single-phase and multi-phase display of microstructures, and zooms in and stores the images for areas requiring quantitative microstructure analysis.

[0063] In step S7, according to actual needs, a suitable image recognition technology is selected, and the target image is recognized using image recognition analysis software (or self-developed program software), specifically including:

[0064] Since the image obtained in this embodiment has good contrast, the traditional threshold segmentation method is chosen for image recognition and analysis. This embodiment utilizes MIPAR image recognition and analysis software. After opening the software, select Adjust Contrast, then Auto, and subsequently find Basic Threshold and select Auto to achieve phase separation of the microscopic composition.

[0065] In step S8, image recognition analysis software and / or mathematical analysis software are used to analyze the feature structure parameters of the microstructure of the image recognition results, specifically including:

[0066] The segmented image is set into layers and named. Calibrate is selected in the scale settings to convert pixels to actual distances. Measure features are selected, and then the different layers are opened to perform statistical analysis on the feature parameters (such as area fraction, equivalent diameter, roughness, and perimeter). The segmented black and white image (B / W) is exported and imported into a self-written program in the commercial mathematical software MATLAB. The segmentation interval is set, and the average size and width of different segmented images are calculated.

[0067] Step S9 involves displaying the microstructure characterization results, specifically including:

[0068] The statistically derived feature parameters (Area Fraction, Equivalent Diameter, Roughness, Perimeter) have been directly imported into an Excel spreadsheet and can be used directly. To calculate the average size and width of different segmented images using MATLAB, the conversion between pixels and actual distances should be performed first. Then, the actual average size and width of different segmented images and the true size of the image should be imported into Origin plotting software. Based on the actual segmentation interval, a bar chart of the actual average size and width of different segmented images should be generated.

[0069] This invention provides an integrated characterization method for ceramic microstructure based on three-dimensional information acquisition and image recognition. It combines the wear rate response differences of the constituent phases of ceramics and ceramic matrix composites, high-precision acquisition of surface three-dimensional height information, image filtering analysis, image recognition and quantitative analysis to achieve integrated characterization of ceramic matrix composite microstructure observation, identification and quantitative analysis.

[0070] like Figure 1The following is a detailed implementation process of the present invention, which includes nine steps: (1) sampling and cutting of ceramic materials; (2) mechanical grinding and surface polishing of ceramic materials; (3) positioning and marking of the test area of ​​ceramic samples; (4) high-precision acquisition of the three-dimensional morphology of the test area surface; (5) filtering and analysis of the surface three-dimensional information acquisition results; (6) setting of three-dimensional information parameters and export of images; (7) image recognition and analysis of output results; (8) quantitative analysis of microstructure parameters; and (9) display of analytical results.

[0071] like Figure 2 ( Figure 6 The image shows the microstructure of a self-generated composite material of B4C-based eutectic ceramics produced by optical suspension melting and directional solidification. Optical microscopy observations reveal that the microstructure is lamellar. Figure 2 or irregular shapes Figure 6 However, the contrast effect of the tissue structure is poor, and the observation is not clear. After filtering and analysis of the same micro-area acquired by the white light interferometer 3D profilometer, it is shown that... Figure 3 ( Figure 7 As can be seen, due to the difference in wear resistance response between the white and black phases, the differences in surface three-dimensional information correspond well with the morphology of the microstructure, and the three-dimensional information projection results can be used as one of the bases for microstructure observation.

[0072] Will Figure 3 ( Figure 7 After exporting the image results, image recognition was performed using MIPAR image recognition software. Subsequently, the segmented and recognized image data was exported and imported into MATLAB software, where a self-written program was used to calculate the phase size of the microstructure and output the parametric statistical results. Figure 4 ( Figure 8 The image shown is the result of image segmentation and recognition, which corresponds well to the microscopic tissue structure. For example... Figure 5 ( Figure 9 The figure shows the quantitative analysis and statistical results of the white and black phases in the identification results.

[0073] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0074] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An integrated characterization method for ceramic microstructure, characterized in that, Includes the following steps: Prepare ceramic material samples and obtain polished surfaces for generating ceramic microstructures; After marking the location of the test area on the polished surface, the three-dimensional topographic height information of the surface is collected using a white light interferometer. During the acquisition of surface three-dimensional topography height information, an image with interference fringes is obtained by adjusting the Z-axis; the brightness and relative tilt of the image are adjusted to acquire surface three-dimensional topography height information. Among them, the vertical scanning interference acquires 0 to 15 fringes, and the phase shift interference acquires less than 5 fringes. The acquired results are preprocessed and height-labeled to generate a test area image. During the preprocessing of the acquired results, Gaussian filtering and statistical filtering are selected to perform filtering analysis on the acquired results. The acquired results are preprocessed by retaining high-frequency signals. The filtered analysis results are leveled, and then a suitable Z-axis height display range is set according to the statistical distribution results of the leveled height information to realize single-phase selective display and multi-phase display of microstructures, and to magnify and store images of areas that require quantitative analysis of microstructures. The ceramic microstructure is characterized by adjusting the contrast of the test area image and displaying the phase separation, and performing multi-parameter analysis of the microstructure of the phase separation results. During the multi-parameter analysis of the characteristic structure, the area fraction, equivalent diameter, roughness, perimeter, phase size, and microstructure statistical distribution law corresponding to different layers are obtained according to actual needs, and the multi-parameter analysis of the characteristic structure is performed.

2. The integrated characterization method for ceramic microstructure according to claim 1, characterized in that: During the preparation of ceramic material samples, sampling rules are set according to the shape, size, test area, and test direction of the ceramic material. Based on the sampling rules, and according to the hardness and conductivity of the ceramic material, the cutting method, cutting feed rate, and cutting path are selected to prepare the ceramic material sample.

3. The integrated characterization method for ceramic microstructure according to claim 2, characterized in that: During the process of generating the polished surface, appropriate sandpaper and polishing liquid types and models are selected according to the characteristics of ceramics and ceramic matrix composites, and the surface of the ceramic material sample is polished to generate the polished surface.

4. The integrated characterization method for ceramic microstructure according to claim 3, characterized in that: During the process of height annotation of the preprocessed acquisition results, the preprocessed acquisition results are leveled to obtain the leveled height information, and the height display range is set to generate a test area image.

5. The integrated characterization method for ceramic microstructure according to claim 1, characterized in that: The image recognition technologies employed include traditional image recognition technologies represented by threshold segmentation and machine learning image recognition technologies represented by deep learning.

6. An integrated characterization system for ceramic microstructures, applied to the integrated characterization method for ceramic microstructures according to any one of claims 1-5, characterized in that, include: The data acquisition module is used to obtain the polished surface used to generate ceramic microstructures by preparing ceramic material samples. After marking the location of the test area on the polished surface, the three-dimensional morphology height information of the surface is collected. The image processing module is used to preprocess and height-annotate the acquired results to generate test area images; The characterization module is used to identify and quantitatively analyze the generated images using image analysis and recognition technology. Depending on the actual needs, it can use traditional threshold segmentation image recognition technology or machine learning-based image recognition technology to perform multi-parameter analysis of the feature structure of the microstructure and characterize the ceramic microstructure.

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