A comparative study of a back propagation artificial neural network and a Zerilli-Armstrong model for pure molybdenum during hot deformation

被引：26

作者：

Chen, Cheng ^{[1
]}

Yin, Haiqing ^{[1
]}

Humail, Islam S. ^{[1
]}

Wang, Yuhui ^{[1
]}

Qu, Xuanhui ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Mat Sci & Engn, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China

来源：

INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS | 2007年 / 25卷 / 5-6期

关键词：

molybdenum; hot deformation; flow stress; neural network; Zerilli-armstrong model;

D O I：

10.1016/j.ijrmhm.2006.11.004

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

in this study, the hot deformation behavior of molybdenum was investigated by means of thermal simulation on a Gleeble-1500 machine. The experiments were carried out under different temperatures, ranging from 1100 to 1400 degrees C, and with a strain rate of IS-1 to 50S(-1). The flow stress under the above mentioned hot deformation conditions was predicted using a back propagation (BP) artificial neural network. The architecture of the network included three input parameters: strain rate, temperature and true strain, and just one output parameter: the flow stress. One hidden layer was adopted, which include nine neurons. Compared with the prediction method of flow stress using the Zerilli-Armstrong model, the prediction method using the BP artificial neural network had higher accuracy. (C) 2006 Elsevier Ltd. All rights reserved.

引用

页码：411 / 416

页数：6