Interdepartmental Optimization in Steel Manufacturing: An Artificial Intelligence Approach for Enhancing Decision-Making and Quality Control

Recent advances in artificial intelligence have intensified efforts to improve quality management in steel manufacturing. In this paper, we present the development and results of a system that aims to learn from the decisions made by experts to anticipate the problems that affect the final quality of the product in the steel rolling process. The system integrates a series of modules, including event filtering, automatic expert knowledge extraction, and decision-making neural networks, developed in a phased approach. The experimental results, using a three-year historical dataset, suggest that our system can anticipate quality issues with an accuracy of approximately 80%, enabling proactive defect prevention and a reduction in production losses. This approach demonstrates the potential for industrial AI applications for predictive quality assurance, highlighting the technical foundations and potential for industrial applications.