Taguchi–Gray‐Based Optimization of Hot Rough Rolling: Energy Efficiency and Dimensional Accuracy

Energy efficiency and dimensional quality are two critical challenges in modern steel rolling mills, particularly during the rough rolling stage of hot rolling. This study addresses these challenges by presenting a multiresponse optimization approach based on the Taguchi–Gray relational analysis method. Key process parameters such as rolling speed, pass angle, and reduction ratio are systematically varied using an L9 orthogonal array, while energy consumption and material spread are evaluated as primary performance outputs. Based on these evaluations, a six‐pass rough rolling schedule is redesigned for N25 ribbed rebar, and the results are validated through both physical measurements and finite element method analyzes conducted using Simufact software. The optimal parameter combination led to an energy saving of 0.52 kWh per ton (≈7.3% improvement). For a rolling mill producing one million tons annually, this translates into a potential energy saving of 520 000 kWh per year. In addition, the standard deviation in rib geometry is reduced from 0.019 to 0.012, resulting in improved uniformity and elimination of rib overflow. These findings demonstrate that the proposed approach provides a practical pathway for improving both energy consumption and dimensional quality in industrial rolling operations.