Work

Knowledge Integration in Steel Microstructure Analysis Using Unsupervised Image Segmentation and Supervised Machine Learning Techniques

Year: 2025

Type: article

Abstract: This study presents a methodology for analyzing bainitic microstructures in steel using image segmentation techniques and machine learning methods. Images of steel microstructures were processed using a superpixel segmentation algorithm, which generated image segments based on grayscale intensity. The histograms of values in grayscale of those segments served as input for classification models such as decision trees, random forests, and the KNN algorithm. Experimental results demonstrated that this method enables effective microstructure classification, including the identification of segments containing bainite. A comparison of algorithms revealed the superiority of random forests in terms of stability and accuracy. Results also show that small segments provide better final results. The obtained results indicate the potential for further development of this method using more advanced neural networks for automated steel microstructure analysis.

Source: Applied Sciences

DOI: 10.3390/app15041810

Authors: Tomasz Jażdżewski, Filip Hallo, Grzegorz Korpała, Krzysztof Regulski

Institutions: AGH University of Krakow, TU Bergakademie Freiberg

Language: English

Topic: Microstructure and Mechanical Properties of Steels

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Ministry of Science and Higher Education of the Russian Federation