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Open AccessFeature PaperArticle

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

1
Department of Materials Science, Chair of Functional Materials, Saarland University, 66123 Saarbrücken, Germany
2
Material Engineering Center Saarland, Saarland University, 66123 Saarbrücken, Germany
3
AG der Dillinger Hüttenwerke, Werkstraße 1, 66763 Dillingen/Saar, Germany
*
Author to whom correspondence should be addressed.
Metals 2020, 10(5), 630; https://doi.org/10.3390/met10050630
Received: 7 April 2020 / Revised: 1 May 2020 / Accepted: 9 May 2020 / Published: 12 May 2020
Bainite is an essential constituent of modern high strength steels. In addition to the still great challenge of characterization, the classification of bainite poses difficulties. Challenges when dealing with bainite are the variety and amount of involved phases, the fineness and complexity of the structures and that there is often no consensus among human experts in labeling and classifying those. Therefore, an objective and reproducible characterization and classification is crucial. To achieve this, it is necessary to analyze the substructure of bainite using scanning electron microscope (SEM). This work will present how textural parameters (Haralick features and local binary pattern) calculated from SEM images, taken from specifically produced benchmark samples with defined structures, can be used to distinguish different bainitic microstructures by using machine learning techniques (support vector machine). For the classification task of distinguishing pearlite, granular, degenerate upper, upper and lower bainite as well as martensite a classification accuracy of 91.80% was achieved, by combining Haralick features and local binary pattern. View Full-Text
Keywords: bainite; microstructure classification; textural parameters; Haralick parameters; local binary pattern; machine learning; support vector machine bainite; microstructure classification; textural parameters; Haralick parameters; local binary pattern; machine learning; support vector machine
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MDPI and ACS Style

Müller, M.; Britz, D.; Ulrich, L.; Staudt, T.; Mücklich, F. Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques. Metals 2020, 10, 630.

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