Application of Machine Learning in Steel Processing

The steel industry is pivotal for global economic development, providing essential materials for construction, automotive, and other sectors. However, traditional production methods often result in inefficiencies, environmental degradation, and inconsistent product quality. Machine learning, with its ability to analyze vast datasets and predict outcomes, presents a promising solution. Recent advancements in ML have shown significant potential in optimizing industrial processes, improving quality control, and enhancing predictive maintenance. The proposed Ph.D. research aims to explore the comprehensive application of machine learning in the steel industry, focusing on process optimization, quality control, predictive maintenance, and sustainability. The study will involve the comprehensive analysis of the data from the steel plant and shop floor operations to develop and implement ML models to improve operational efficiency, reduce costs, and enhance environmental sustainability. This project will also involve the active collaboration with a potential steel plant in India or Australia.