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Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach

Department of Civil & Environmental Engineering, North Dakota State University, ND 58105, USA
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Metals 2019, 9(5), 546; https://doi.org/10.3390/met9050546
Received: 19 March 2019 / Revised: 19 April 2019 / Accepted: 8 May 2019 / Published: 10 May 2019
(This article belongs to the Special Issue Advances in Structural Steel Research)
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Abstract

Automatic identification of metallurgical phases based on thresholding methods in microstructural images may not be possible when the pixel intensities associated with the metallurgical phases overlap and, hence, are indistinguishable. To circumvent this problem, additional visual information about the metallurgical phases, referred to as textural features, are considered in this study. Mathematically, textural features are the second order statistics of an image domain and can be distinct for each metallurgical phase. Textural features are evaluated from the gray level co-occurrence matrix (GLCM) of each metallurgical phase (ferrite, pearlite, and martensite) present in heat-treated ASTM A36 steels in this study. The dataset of textural features and pixel intensities generated for the metallurgical phases is used to train supervised machine learning classifiers, which are subsequently employed to predict the metallurgical phases in the microstructure. Naïve Bayes (NB), k-nearest neighbor (K-NN), linear discriminant analysis (LDA), and decision tree (DT) classifiers are the four classifiers employed in this study. The performances of all four classifiers were assessed prior to their deployment, and the classification accuracy was found to be >97%. The proposed technique has two unique advantages: (1) unlike pixel intensity-based methods, the proposed method does not misclassify the grain boundaries as a metallurgical phase, and (2) the proposed method does not require the end-user to input the number of phases present in the microstructure.
Keywords: Gray level co-occurrence matrix (GLCM), ASTM A36; steel microstructure; textural features; machine learning classifiers Gray level co-occurrence matrix (GLCM), ASTM A36; steel microstructure; textural features; machine learning classifiers
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Naik, D.L.; Sajid, H.U.; Kiran, R. Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach. Metals 2019, 9, 546.

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