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Predicting the mean temperature of the transfer bar after rolling in the rougher using a neural network
MEFOS − Stiftelsen för Metallurgisk Forskning.
1998 (English)In: Journal of Materials Processing Technology, ISSN 0924-0136, E-ISSN 1873-4774, Vol. 80-81, p. 469-474Article in journal (Refereed)
Abstract [en]

The aim with this work has been to improve the temperature calculation used to pre-set the roll gap in a finishing train. The calculation is used for estimation of the rolling force. Currently an empirical model together with the measured surface temperature is used to calculate the threading temperature. In this study a predictor, based on thermal simulations of the process and neural networks, has been developed. The influence of slab dimensions, initial temperature, edging, reduction and number of passes has been studied and also delays have been considered. A part of the model has been tested on measured surface temperature and estimated mean temperatures at SSAB Tunnplat AB, Borlange, Sweden. © 1998 Elsevier Science S.A. All rights reserved.

Place, publisher, year, edition, pages
1998. Vol. 80-81, p. 469-474
Keywords [en]
Hot rolling, Neural networks, Temperature prediction, Thermal calculations
National Category
Materials Engineering
Identifiers
URN: urn:nbn:se:ri:diva-12739Scopus ID: 2-s2.0-11544361300OAI: oai:DiVA.org:ri-12739DiVA, id: diva2:972931
Available from: 2016-09-22 Created: 2016-09-22 Last updated: 2017-11-21Bibliographically approved

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Scopushttp://www.sciencedirect.com/science/article/pii/S0924013698002003
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