Abstract
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.
Highlights
Bainite is a typical constituent of modern high strength steels, combining high strength and high toughness and thereby making it interesting for many applications
Bainitic reference samples are used and analyzed based on the workflows suggested by Webel et al and Gola et al, which is feature extraction by using Haralick textural parameters, complemented by using local binary pattern, followed by machine learning classification using a support vector machine, in order to demonstrate the feasibility of a classification of bainite subclasses
All six microstructure classes found in the samples were considered for the classification task: pearlite (P), granular bainite (GB), degenerated upper bainite (DUB), upper bainite (UB), lower bainite (LB), and martensite (M)
Summary
Bainite is a typical constituent of modern high strength steels, combining high strength and high toughness and thereby making it interesting for many applications. Gola et al [12] used a combination of morphological and not many approaches for an automated classification of steel microstructures, including bainite textural parameters with a support vector machine (SVM) to classify the carbon-rich second phase subclasses can be found in the literature. Bainite subclasses were not textural parameters with a support vector machine (SVM) to classify the carbon-rich second phase of considered, as all structures that were neither pearlite nor martensite were put into one bainite class. Banerjee et al [18] differentiate ferrite, bainite, and martensite by using intensity values [17] upper and lower bainite are distinguished by calculating morphological parameters of the as well as density of substructure particles, whereas Paul et al [19] use regional contour pattern and cementite precipitates. Bainitic reference samples are used and analyzed based on the workflows suggested by Webel et al and Gola et al, which is feature extraction by using Haralick textural parameters, complemented by using local binary pattern, followed by machine learning classification using a support vector machine, in order to demonstrate the feasibility of a classification of bainite subclasses
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