Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Review: A fast rigid-plastic finite element method for online application in strip rolling
Finite Elements in Analysis and Design
Engineering Applications of Artificial Intelligence
A NR-BFGS method for fast rigid-plastic FEM in strip rolling
Finite Elements in Analysis and Design
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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.