Rolling force prediction for strip casting using theoretical model and artificial intelligence

Abstract

Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence. Solution zone was classified into two parts by kiss point position during casting strip. Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone, and deduce the analytic equation of unit compression stress distribution. The traditional hot rolling model was still used in the solid zone. Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position. The results show that calculation accuracy for verification data of 94.67% is in the range of ±7.0%, which indicates that the predicting accuracy of this model is very high.

Keywords

kiss point / Navier-Stokes equation / rheological properties / Bayesian method / generalization capabilities