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

Prasun Das, Frank Pettersson, Shubhabrata Dutta

    Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer 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.
    Sidor (från-till)354–372
    TidskriftInternational Journal of Artificial Intelligence and Soft Computing
    StatusPublicerad - 2014
    MoE-publikationstypA1 Tidskriftsartikel-refererad

    Citera det här