Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

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dc.contributor.authorKara, Fuat
dc.contributor.authorAslantaş, Kubilay
dc.contributor.authorÇiçek, Adem
dc.date.accessioned2020-04-30T23:21:15Z
dc.date.available2020-04-30T23:21:15Z
dc.date.issued2016
dc.departmentDÜ, Teknoloji Fakültesi, Makine ve İmalat Mühendisliği Bölümüen_US
dc.descriptionAslantas, Kubilay/0000-0003-4558-4516; KARA, Fuat/0000-0002-3811-3081en_US
dc.descriptionWOS: 000366805900005en_US
dc.description.abstractIn this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al2O3 + TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson-Cook (J-C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R-2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures. (C) 2015 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2015.09.034en_US
dc.identifier.endpage74en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage64en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2015.09.034
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4162
dc.identifier.volume38en_US
dc.identifier.wosWOS:000366805900005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectFinite element modelen_US
dc.subjectCutting temperatureen_US
dc.subjectOrthogonal cuttingen_US
dc.subjectCutting forcesen_US
dc.titlePrediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural networken_US
dc.typeArticleen_US

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