Reliable roll force prediction in cold mill using multiple neural networks

The cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. The accurate prediction of roll force is essential for product quality. Currently, a suboptimal mathematical model is used. We trained two multilayer perceptrons, one to directly predict the...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on neural networks Vol. 8; no. 4; pp. 874 - 882
Main Authors Cho, S, Cho, Y, Yoon, S
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.1997
Subjects
Error correction
Intelligent networks
Mathematical model
Milling machines
Multilayer perceptrons
Neural networks
Steel
Strips
System testing
Thickness measurement
Online AccessGet full text

Cover

More Information
Summary:The cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. The accurate prediction of roll force is essential for product quality. Currently, a suboptimal mathematical model is used. We trained two multilayer perceptrons, one to directly predict the roll force and the other to compute a corrective coefficient to be multiplied to the prediction made by the mathematical model. Both networks were shown to improve the accuracy by 30-50%. Combining the two networks and the mathematical model results in systems with an improved reliability.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:1045-9227
1941-0093
DOI:10.1109/72.595885