A neural network-assisted finite element analysis of cold flat rolling
- 作者：P.P. Gudur ; U.S. Dixit
- 关键词：Cold rolling ; Neural network ; Radial basis function ; Finite element method ; Deformation field ; Rigid plastic
- 刊名：Engineering Applications of Artificial Intelligence
- 出版年：2008
- 期刊代码：51_09521976
- 类别：cp
- 出版时间：February 2008
- 卷：21
- 期：1
- 页码：43-52
- 文件大小：463 K
摘要
The finite element analysis of the cold flat rolling process is well established. However, the requirement of large computational time makes it unsuitable for online applications. Recently, there have been some applications of modeling the rolling process by means of neural networks. In most of the previous works, trained networks predict only roll force and roll torque. The input data for training the neural network have been obtained either through experiments or from finite element method (FEM) code. In this work, the neural networks have been used for predicting the velocity field and location of neutral point. The training data are obtained from a rigid-plastic finite element code. The trained network provides a suitable guess for the velocity field and location of the neutral point, that is further refined by the finite element code. The post-processor of the FEM code computes roll force, roll torque, strain distribution, etc. This procedure provides highly accurate solution with reduced computational time and is suitable for on line control or optimization.