ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel

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dc.contributor.authorKara, Fuat
dc.contributor.authorAslantaş, Kubilay
dc.contributor.authorÇiçek, Adem
dc.date.accessioned2020-04-30T22:39:25Z
dc.date.available2020-04-30T22:39:25Z
dc.date.issued2015
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: 000347408400020en_US
dc.description.abstractIn this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCN + Al2O3 + TiN and Al2O3), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force (F (c)) and the feed force (F (f)) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for F (c), the optimal results for F (f) were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability.en_US
dc.identifier.doi10.1007/s00521-014-1721-yen_US
dc.identifier.endpage250en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage237en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-014-1721-y
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2719
dc.identifier.volume26en_US
dc.identifier.wosWOS:000347408400020en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCutting forcesen_US
dc.subjectOrthogonal machiningen_US
dc.subjectArtificial neural networken_US
dc.subjectCoating materialsen_US
dc.titleANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steelen_US
dc.typeArticleen_US

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