Data-Driven Framework for Anomaly Identification and Process Optimization in Continuous Casting
A data-driven framework is proposed to address the above limitations by identifying key anomalous parameters and inferring feasible parameter adjustments under operational constraints, with interpretability analysis incorporated to enhance consistency with metallurgical knowledge.