Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools
It is demonstrated that optical microscopy images of steel materials could be effectively categorized into classes on preset ferrite/pearlite-, ferrite/pearlite/bainite-, and bainite/martensite-type microstructures with image pre-processing and statistical analysis including the machine learning tec...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-12-01
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| Series: | Science and Technology of Advanced Materials |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/14686996.2019.1610668 |
| _version_ | 1818130773118550016 |
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| author | Dmitry S. Bulgarevich Susumu Tsukamoto Tadashi Kasuya Masahiko Demura Makoto Watanabe |
| author_facet | Dmitry S. Bulgarevich Susumu Tsukamoto Tadashi Kasuya Masahiko Demura Makoto Watanabe |
| author_sort | Dmitry S. Bulgarevich |
| collection | DOAJ |
| description | It is demonstrated that optical microscopy images of steel materials could be effectively categorized into classes on preset ferrite/pearlite-, ferrite/pearlite/bainite-, and bainite/martensite-type microstructures with image pre-processing and statistical analysis including the machine learning techniques. Though several popular classifiers were able to get the reasonable class-labeling accuracy, the random forest was virtually the best choice in terms of overall performance and usability. The present categorizing classifier could assist in choosing the appropriate pattern recognition method from our library for various steel microstructures, which we have recently reported. That is, the combination of the categorizing and pattern-recognizing methods provides a total solution for automatic quantification of a wide range of steel microstructures. |
| first_indexed | 2024-12-11T08:10:22Z |
| format | Article |
| id | doaj.art-ee0b300f22364790ad39868794259846 |
| institution | Directory Open Access Journal |
| issn | 1468-6996 1878-5514 |
| language | English |
| last_indexed | 2024-12-11T08:10:22Z |
| publishDate | 2019-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj.art-ee0b300f22364790ad398687942598462022-12-22T01:14:55ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142019-12-0120153254210.1080/14686996.2019.16106681610668Automatic steel labeling on certain microstructural constituents with image processing and machine learning toolsDmitry S. Bulgarevich0Susumu Tsukamoto1Tadashi Kasuya2Masahiko Demura3Makoto Watanabe4National Institute for Materials ScienceNational Institute for Materials ScienceThe University of TokyoNational Institute for Materials ScienceNational Institute for Materials ScienceIt is demonstrated that optical microscopy images of steel materials could be effectively categorized into classes on preset ferrite/pearlite-, ferrite/pearlite/bainite-, and bainite/martensite-type microstructures with image pre-processing and statistical analysis including the machine learning techniques. Though several popular classifiers were able to get the reasonable class-labeling accuracy, the random forest was virtually the best choice in terms of overall performance and usability. The present categorizing classifier could assist in choosing the appropriate pattern recognition method from our library for various steel microstructures, which we have recently reported. That is, the combination of the categorizing and pattern-recognizing methods provides a total solution for automatic quantification of a wide range of steel microstructures.http://dx.doi.org/10.1080/14686996.2019.1610668metallurgymachine learningmicrostructuresoptical microscopypattern recognition |
| spellingShingle | Dmitry S. Bulgarevich Susumu Tsukamoto Tadashi Kasuya Masahiko Demura Makoto Watanabe Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools Science and Technology of Advanced Materials metallurgy machine learning microstructures optical microscopy pattern recognition |
| title | Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools |
| title_full | Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools |
| title_fullStr | Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools |
| title_full_unstemmed | Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools |
| title_short | Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools |
| title_sort | automatic steel labeling on certain microstructural constituents with image processing and machine learning tools |
| topic | metallurgy machine learning microstructures optical microscopy pattern recognition |
| url | http://dx.doi.org/10.1080/14686996.2019.1610668 |
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