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Titolo:
Application of neural networks to predict the width variation in a plate mill
Autore:
Chun, MS; Yi, JJ; Moon, YH;
Indirizzi:
Res Inst Ind Sci & Technol, Rolling Proc Res Team, Pohang 790330, South Korea Res Inst Ind Sci & Technol Pohang South Korea 790330 790330, South Korea Pusan Natl Univ, Engn Res Ctr Net Shape & Die Mfg, Pusan 609735, South Korea Pusan Natl Univ Pusan South Korea 609735 Mfg, Pusan 609735, South Korea
Titolo Testata:
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
fascicolo: 1-3, volume: 111, anno: 2001,
pagine: 146 - 149
SICI:
0924-0136(20010425)111:1-3<146:AONNTP>2.0.ZU;2-W
Fonte:
ISI
Lingua:
ENG
Keywords:
neural network; width variation; plate mill; broadside rolling; finishing rolling;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
4
Recensione:
Indirizzi per estratti:
Indirizzo: Chun, MS Res Inst Ind Sci & Technol, Rolling Proc Res Team, San 32 Hyoja Dong, Pohang 790330, South Korea Res Inst Ind Sci & Technol San 32 Hyoja Dong Pohang South Korea 790330
Citazione:
M.S. Chun et al., "Application of neural networks to predict the width variation in a plate mill", J MATER PR, 111(1-3), 2001, pp. 146-149

Abstract

The width variation of steel plate during broadside and finish lulling passes in a plate mill has been investigated using neural networks. It was found that the width variation after broadside rolling and finish rolling is affected by the edging ratio, the broadside railing ratio. the longitudinal rolling ratio, the width deviation after the broadside pass, the temperature, the width-to-thickness ratio, and so on. Neural network modeling of a back propagation learning algorithm with one hidden layer has been conducted on the width variation prediction during the plate rolling. The prediction for the width variation according to rolling sequence was classified into two rolling processes; broadside rolling andfinishing rolling. A performance test showed that the well-trained neural network model can interpolate the width variation very effectively The prediction accuracy was improved by adjusting the adaptive learning algorithm. Based on these prediction models, the width variation of the final plate ismuch more decreased. (C) 2001 Elsevier Science B.V. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 28/11/20 alle ore 09:58:08