Development of a neural network model for a cold rolling process

被引：27

作者：

Gunasekera, JS ^{[1
]}

Jia, ZJ

Malas, JC

Rabelo, L

机构：

[1] Ohio Univ, Dept Mech Engn, Athens, OH 45701 USA

[2] Litens Automot Grp, Prod Engn, Woodbridge, ON L4L 5T9, Canada

[3] USAF, Res Lab, Mat & Mfg Directorate, Wright Patterson AFB, OH 45433 USA

[4] Calif Polytech Inst, San Luis Obispo, CA USA

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 1998年 / 11卷 / 05期

关键词：

metal forming; rolling; neural networks; optimization; modeling;

D O I：

10.1016/S0952-1976(98)00025-6

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes the development of a neural network model for the Aat rolling process. This neural network was based on the backpropagation paradigm. A nonlinear mathematical model based on the slab method was developed to guide and supervise the learning procedures. A near-optimal neural network structure was determined by using a development process that emphasized second-order derivative information. The application of this process yielded improvements in the learning errors, prediction errors, and training times. A robust and accurate model was obtained as a result of this process. (C) 1998 Elsevier Science Ltd. All rights reserved.

引用

页码：597 / 603

页数：7