Neural Network Modelling to Characterize Steel Continuous Casting Process Parameters and Prediction of Casting Defects

Abstract

The current work outlines application of a data-driven multilayer perceptron-based artificial neural network (ANN) model to characterize the influence of melt compositions, tundish temperature, tundish superheat, casting speed and mould oscillation frequency on the important processing parameters such as mould powder consumption rate, oscillation mark depth and metallurgical length during continuous casting process. A two-layer feedforward back-propagation neural network model has been developed for predicting the probability of occurrence of defect in the cast product. The network training architecture has been optimized using a gradient-based algorithm, namely the back-propagation algorithm. The neural network predictions are found to be in good agreement with regard to oscillation mark depth, mould powder consumption rate, metallurgical length and probability of occurrence of defect using data obtained from an operating Indian steel plant (Rashtriya Ispat Nigam Limited, Visakhapatnam). The ANN model prediction has been validated successfully with multiple linear regression analysis carried out on each data set.