Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis

• Published in: Applied sciences 2023-03, Vol.13 (6), p.3479
• Main Authors: Muñoz-Rodenas, Jorge, García-Sevilla, Francisco, Coello-Sobrino, Juana, Martínez-Martínez, Alberto, Miguel-Eguía, Valentín
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Annealing; Carbon steel; Classification; Datasets; Deep learning; Experiments; Heat treatment; Heat treatments; Heterogeneity; Image classification; Image quality; Learning algorithms; Learning strategies; Light microscopy; low-carbon steels; Machine learning; Microscopy; Microstructural analysis; Microstructure; Neural networks; Optical microscopy; optical microstructure; Quality control; Quality management; Quenching; Steel, Structural; Support vector machines; Transfer learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13063479

This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples contain the typical constituents expected, and these are different for each treatment. Images are obtained by optical microscopy at 400× magnification and from different low-carbon steels to generate the data with some heterogeneity. Learning models are created with an image dataset for classification into three classes based on the respective heat treatments. Likewise, we develop two kinds of models by using, on the one hand, classical machine-learning methods based on the “bag of features” technique and, on the other hand, convolutional neural networks (CNN) with a transfer-learning approach by using GoogLeNet and ResNet50. We demonstrate the superiority of deep-learning techniques (CNN) over classical machine-learning methods.