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.
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