Study on a Zerilli-Armstrong and an artificial neural network model for 4Cr5MoSiVl Quenched Steel at High Strain Rate

摘要：

The flow stress in compression of 4Cr5MoSiVl quenched Steel was investigated by means of a split Hopkinson pressure bar (SHPB) experiment apparatus under different temperature. According to the stress-strain curves, the Zerilli-Armstrong constitutive model was chosen and its relationship parameters were determined by Genetic Algorithm (GA) with adaptive population size. At the same time, the Back Propagation artificial neural network (BP ANN) was used for establishing constitutive model of 4Cr5MoSiVl quenched Steel. Compared with the experimental data, two constitutive equations can predict the flow stress very well, and the prediction method using the BP artificial neural network had higher accuracy. The research provides a necessary material characteristic parameters for finite element numerical simulation of 4Cr5MoSiVl quenched Steel, and the two prediction method can be widely used to establish other nonlinear relations of manufacturing procedure.

关键词： 4Cr5MoSiVl Quenched steel SHPB high strain rate Zerilli-Armstrong model artificial neural network Genetic Algorithm

作者: Jing Wang

作者单位: Changzhou Institute of Light Industry Technology Changzhou, China

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 258-261

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）