Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

• Main Authors: Dmitry S. Bulgarevich, Susumu Tsukamoto, Tadashi Kasuya, Masahiko Demura, Makoto Watanabe
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-12-01
• Series: Science and Technology of Advanced Materials
• Subjects: metallurgy; machine learning; microstructures; optical microscopy; pattern recognition
• Online Access: http://dx.doi.org/10.1080/14686996.2019.1610668

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.