A novel paradigm for predicting and interpreting uneven roll wear in the hot rolling steel industry

Abstract

In the hot rolling industry, uneven roll wear significantly influences schedule free rolling and product quality, necessitating more precise wear prediction to improve the capabilities of hot rolling production. However, existing methods, laden with limitations, struggle to predict uneven roll wear precisely and transparently. To address these challenges, we present a novel paradigm that combines a computer simulation technique, classical wear theory and a data-driven approach for predicting uneven work roll wear in the hot rolling industry. Initially, a finite element model is constructed to simulate hot rolling processing. Subsequently, an Archard-theory-based work roll wear model is derived to calculate the theoretical wear loss using the simulation results. Following this, based on the theoretical wear loss, a deep ensemble model containing three base predictors is established. Notably, Shapley additive explanations (SHAP) and ensemble mechanism analysis are implemented to explain the predictive process of the wear loss. The comparative experimental results demonstrate the deep ensemble method achieves a 2 % accuracy improvement over other machine learning models. Additionally, the wear prediction results for a real case of a roll change period prove that, at the peak position of wear profile, the proposed paradigm surpasses the existing model by 7.2 %. Significantly, the feature contributions and process interpretable analysis based on SHAP make the proposed paradigm both transparent and reliable.