Pruned–bimodular neural networks for modelling of strength–ductility balance of HSLA steel plates

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Prasun Das, Frank Pettersson, Shubhabrata Dutta
Publication year: 2014
Journal: International Journal of Artificial Intelligence and Soft Computing
Journal acronym: IJAISC
Volume number: 4
Issue number: 4
Start page: 354
End page: 372


Abstract

In this paper, an attempt has been made in this study to grow the concept of modularity along with pruned networks for strength-ductility balance of high strength low alloy (HSLA) steel plates using lower and upperlayer pruning algorithms. Modelling of strength-ductility balance in case of high strength low alloy steel is a major concern in industrial research. In most cases, the cause of inferior mechanical properties of such steel products could not be clearly identified. The comparative analysis with standard fully-connected network and pruned network reveals an improved performance for pruned-modular architecture and explains the metallurgical phenomenon of HSLA steel in a better way.

Last updated on 2019-15-10 at 01:29