Classifying tensile strength of HSLA steel: an investigation through neural networks using Mahalanobis Distance

摘要

This paper addresses a comparative approach of classification of Thermomechanically Controlled Processed (TMCP) High Strength Low Alloy (HSLA) steels based on Mahalanobis-Taguchi System (MTS) principles and ensemble neural networks, including sensitivity analysis for variable selection. Later, a hybrid approach is developed, depending on the ability of Mahalanobis Distance (MD) in capturing the correlation structure of a multi-dimensional system, both for the Multi-Layered Perception (MLP) and for Radial Basis Function (RBF) networks. The results are found to be quite consistent in describing the role of input parameters for effective classification of such steel.