Year
1996
Degree Name
Master of Engineering (Hons.)
Department
Department of Mechanical Engineering
Recommended Citation
Luo, Shizhuang, Neural networks modelling of in-bar width in hot strip mill, Master of Engineering (Hons.) thesis, Department of Mechanical Engineering, University of Wollongong, 1996. https://ro.uow.edu.au/theses/2521
Abstract
The steel industry is restructuring to seek higher quality products, rather than a simple increase of the amount of steel produced. Artificial Intelligence provides the necessary tools to improve the current steel processing technology. This thesis deals with the rolling process in a Hot Strip Mill. Width control is considered as a major quality variable, hence this thesis concentrates on Roughing Mill Width Control. Currently, a statistical model is used for width control, which is providing a limited quality performance. Therefore, an accurate model is required in place of any current statistical model. This thesis presents the research work towards an applicational in-bar width model at a roughing mill. This work is under the Australia Research Council (ARC) collaborative project with BHP steel. Flat Products Division, Hot Strip Mill. Neural Networks have been proven to be a very good tool for modelling and prediction, for its nonlinear feature, good interpolation ability and adaptabihty to novelty situation. Hence, a Neural Network was is selected as the tool to perform modelling of the width deformation process. Statistics analysis was carried out to assist improving model accuracy and reduce network redundancy.
Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.