Application of machine learning methods in the metallurgical industry: A bibliometric analysis and systematic literature review

This research aimed to map the application of machine learning (ML) methods in the metallurgical industry from 2000 to 2024, identifying key topics, trends, and future directions. A mixed-methods approach, combining bibliometric analysis, text mining, and content analysis, was employed. A total of 341 articles from Scopus and 249 from Taylor & Francis were reviewed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses method and an ad hoc selection of 10 key articles. The analysis revealed five main research areas: (1) Advances in ML and materials science; (2) innovation in additive manufacturing; (3) applications of ML algorithms in steel metallurgy; (4) predictive analytics and modeling; and (5) artificial intelligence and deep learning applied to metallurgy. Based on these findings, five future research directions were proposed, including process optimization using deep learning, prediction of alloy behavior, sustainable waste management, integration of digital twins, and addressing ethical and regulatory challenges.