This article proposes a dynamic causal inference framework that integrates theoretical analysis, numerical simulation, and industrial data mining to address the root-cause tracing problem of time-delay effects in strip thickness and shape quality during hot rolling. First, we analyze the key process parameters, equipment states, and material characteristics influencing geometric quality and clarify their dynamic interaction mechanisms. Second, a delay-correlation matrix calculation method based on Dynamic Time Warping (DTW) and Mutual Information (MI) is developed to handle temporal misalignment in multi-source industrial signals and quantify the strength of delayed correlations. Furthermore, a transformer-based information gain approximation mechanism is designed to replace traditional explicit probability modeling and learn dynamic information-flow relationships among variables in a data-driven manner. Experimental verification on real production data demonstrates that the proposed framework can accurately identify time-delay causal pathways, providing an interpretable and engineering-feasible solution for quality control under complex operating conditions.
Zong et al. (Tue,) studied this question.