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

P. Das, F. Pettersson, Shubhabrata Dutta
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引用次数: 4

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.
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基于剪枝双模神经网络的HSLA钢板强度-延性平衡建模
在本文中,本研究尝试使用下层和上层修剪算法来发展模块化概念和修剪网络,以实现高强度低合金(HSLA)钢板的强度-延性平衡。高强度低合金钢的强度-延性平衡建模是工业研究中的一个热点问题。在大多数情况下,此类钢材的机械性能较差的原因无法明确确定。通过与标准全连接网络和剪枝网络的对比分析,发现剪枝模块化结构的性能得到了改善,并能更好地解释HSLA钢的冶金现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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