Prediction of properties over the length of the coil during thermo-mechanical processing using DNN

Abstract

Certain mechanical properties of steel, such as elongation percentage, yield strength and ultimate tensile strength, form the basis for classification of steel coils into various categories. Methods to improve the prediction of such properties, using multiple chemical and physical process parameters, have remained an integral area of research in steel plants. In this paper, an important parameter, that is, the run-out table cooling profile of a hot strip mill coil, is considered along with the customary process parameters. This additional parameter allows a deep neural network-based model to predict the mechanical properties of steel for multiple segments along the entire length of the coil instead of the established single-segment property prediction process with very high R 2 value.