Toggle Main Menu Toggle Search

Open Access padlockePrints

Neural network modelling and prediction in multipass steel processing

Lookup NU author(s): Professor Elaine Martin, Emeritus Professor Julian Morris


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Operations comprising a sequence of single passes whereby relative motion occurs between a workpiece and a shaping tool on each pass is termed a multipass process. This paper describes the development of a neural network modelling approach for the representation of the complex dynamic interactions that are characteristic of multipass processes. The developments are then applied to a world-scale steel beam rolling mill for the prediction of motor torque and rolling force. Two neural network structures were designed to satisfy different operational requirements. The first was to provide online single pass ahead predictions, while the second was for off-line multipass ahead predictions. Although the results obtained using the 'best' single network model were promising, significant prediction improvements were achieved by combining (stacking) multiple neural networks that were trained using different network topologies. © IMcchE 2004.

Publication metadata

Author(s): Martin EB, Fraser AW, Morris AJ

Publication type: Article

Publication status: Published

Journal: Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Year: 2004

Volume: 218

Issue: 3

Pages: 121-132

ISSN (print): 0954-4089

ISSN (electronic): 2041-3009

Publisher: Sage


DOI: 10.1243/0954408041323476


Altmetrics provided by Altmetric