Deakin home > Deakin University Library > Deakin Research Online > Predicting the rolling force in hot steel rolling mill using an ensemble model

Predicting the rolling force in hot steel rolling mill using an ensemble model

Frayman, Yakov, Rolfe, Bernard, Hodgson, Peter and Webb, G.I 2002, Predicting the rolling force in hot steel rolling mill using an ensemble model, in Artificial intelligence and applications: proceedings of the second IASTED International Conference, ACTA Press, New York, N.Y., pp. 143-148.


Title Predicting the rolling force in hot steel rolling mill using an ensemble model
Author(s) Frayman, Yakov
Rolfe, Bernard
Hodgson, Peter
Webb, G.I
Conference name IASTED International Conference (2nd : 2002 : Malaga, Spain)
Conference location Malaga, Spain
Conference dates 9-12 September 2002
Title of proceedings Artificial intelligence and applications: proceedings of the second IASTED International Conference
Editor(s) Hamza, M.H.
Publication date 2002
Start page 143
End page 148
Publisher ACTA Press
Place of publication New York, N.Y.
Keyword(s) Engineering Applications
Summary Accurate prediction of the roll separating force is critical to assuring the quality of the final product in steel manufacturing. This paper presents an ensemble model that addresses these concerns. A stacked generalisation approach to ensemble modeling is used with two sets of the ensemble model members, the first set being learnt from the current input-output data of the hot rolling finishing mill, while another uses the available information on the previous coil in addition to the current information. Both sets of ensemble members include linear regression, multilayer perceptron, and k-nearest neighbor algorithms. A competitive selection model (multilayer perceptron) is then used to select the output from one of the ensemble members to be the final output of the ensemble model. The ensemble model created by such a stacked generalization is able to achieve extremely high accuracy in predicting the roll separation force with the average relative accuracy being within 1% of the actual measured roll force.
ISBN 0889863520
9780889863521
Language eng
Field of Research 091099 Manufacturing Engineering not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005, Monash University
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004828

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 547 Abstract Views  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 09:41:59 EST