Reliable roll force prediction in gold mill using multiple neural networks

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...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 8; no. 4; pp. 874 - 882
Main Authors Cho, Sungzoon, Cho, Yongjung, Yoon, Sungchul
Format Journal Article
LanguageEnglish
Published 01.01.1997
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Summary: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.
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ISSN:1045-9227