High dimensional data-driven optimal design for hot strip rolling of c-mn steels

Abstract

Recently, hot strip rolling processes are required to be agile and accurate in order to meet more and more diverse market demands. Under such circumstances, the traditional processing optimization by timeconsuming pilot experiments becomes difficult. To realize this target, core models of processing and mechanical properties are often established by neural network methods, which are used to handle nonlinear multi-variant systems. In modeling processing and mechanical properties, we found that the abnormal values in industrial big data could result in wrong predictions for the relationships between processing and properties. In the present work, therefore, data processing has been developed to prevent misleading predictions, which was performed by eliminating the redundant, abnormal, and imbalanced data before modeling. The Bayesian neural network was used to construct the modeling of mechanical properties for hot rolled C-Mn steels, which demonstrated that the accuracies between the measured and predicted values were within ±10% and ±5% for strength and elongation, respectively, providing a reliable model for the optimal process design. By applying the multi-objective optimization algorithm named Strength Pareto Evolutionary Algorithm 2 (SPEA2), the hot strip rolling processes for C-Mn steels were optimized in order for either stabilizing variations of properties or upgrading mechanical properties. Industrial trials were extensively carried out, showing good agreements with the optimized processes.