Comparison of ANN and MARS in Prediction of Property of Steel Strips

Abstract

Soft Computing has become popular in Steel Industry for its applications in the areas of reduction in defects, prediction of properties, classification of the products and many others. In recent times, the prediction of properties of steel strip is an area of increased interest mainly because of its prospective benefits of reduction in testing cost, better control on properties, reduction of inventory, increase in yield, and improvement in delivery compliance. Prediction of mechanical properties is a complicated task, as it depends on the chemical composition of the steel, and a number of processing parameters. In general, a high degree of nonlinearity exists between the property and the factors influencing it. In the past only Artificial Neural Network (ANN) was used, sometimes along with the variable reduction technique such as principle components / factor analysis. However, Multivariate Adaptive Regression Splines (MARS) has never been used despite some of its known advantages over the ANN. In this work two predictive models have been developed - one based on ANN, and another, MARS. This paper discusses on the model development and the comparative performance analysis of these two. The analysis shows that the results from both the models are comparable. However, shorter training time and automatic selection of important predictor variables, give MARS an edge over ANN.