Artificial neural network application for modeling the rail rolling process

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dc.authoridEsen, Ismail/0000-0002-7853-1464
dc.authoridAltinkaya, Huseyin/0000-0003-1956-1695
dc.authoridOrak, Ilhami Muharrem/0000-0002-7219-4209
dc.contributor.authorAltinkaya, Huseyin
dc.contributor.authorOrak, Ilhami M.
dc.contributor.authorEsen, Ismail
dc.date.accessioned2024-09-29T15:57:10Z
dc.date.available2024-09-29T15:57:10Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_US
dc.description.abstractRail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabuk, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making. (C) 2014 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2014.06.014
dc.identifier.endpage7146en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-84904293488en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage7135en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2014.06.014
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4627
dc.identifier.volume41en_US
dc.identifier.wosWOS:000340689700015en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectHot rollingen_US
dc.subjectRail rollingen_US
dc.titleArtificial neural network application for modeling the rail rolling processen_US
dc.typeArticleen_US

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