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

Prasun Das, Frank Pettersson, Shubhabrata Dutta

    Research output: Contribution to journalArticleScientificpeer-review


    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.
    Original languageUndefined/Unknown
    Pages (from-to)354–372
    JournalInternational Journal of Artificial Intelligence and Soft Computing
    Issue number4
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed

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