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

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.

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“…Interaksi alotropi yang terjadi antara logam besi dengan elemen pemadu, seperti karbon, yang membuat baja dan besi tuang memiliki ciri khas yang ada pada diri mereka. [4]…”

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“…Given the confusion matrix C, the performance of each combination is assessed by evaluating the four metrics namely 'Accuracy', 'Precision', 'Recall' and 'F Measure'. 'Accuracy' is defined as the ratio of the total number of instances whose class labels are correctly identified to the total number of instances present in the validation dataset and is expressed as [20,41,42].…”

“…'Precision (O)' is defined as the ratio of the number of observations whose class label i is correctly predicted by the classifier to the total number of observations that are assigned to the class i by the classifier, and 'Recall (R)' is defined as the proportion of observations of class i that are correctly predicted as class i by the classifier [41].…”

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