Modelling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis

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dc.contributor.authorÇiçek, Adem
dc.contributor.authorKıvak, Turgay
dc.contributor.authorSamtaş, Gürcan
dc.contributor.authorÇay, Yusuf
dc.date.accessioned2020-04-30T23:19:17Z
dc.date.available2020-04-30T23:19:17Z
dc.date.issued2012
dc.departmentDÜ, Cumayeri Meslek Yüksekokuluen_US
dc.descriptionWOS: 000306718800007en_US
dc.description.abstractIn this study, the effects of cutting parameters (i.e., cutting speed, feed rate) and deep cryogenic treatment on thrust force (Ff) have been investigated in the drilling of AISI 316 stainless steel. To observe the effects of deep cryogenic treatment on thrust forces, M35 HSS twist drills were cryogenically treated at -196 degrees C for 24 h and tempered at 200 degrees C for 2 h after conventional heat treatment. The experimental results showed that the lowest thrust forces were measured with the cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. The scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. The ANN results showed that the SCG learning algorithm with five neurons in the hidden layer produced the coefficient of determinations (R-2) of 0.999907 and 0.999871 for the training and testing data, respectively. In addition, the root mean square error (RMSE) was 0.00769 and 0.009066, and the mean error percentage (MEP) was 0.725947 and 0.930127 for the training and testing data, respectively.en_US
dc.identifier.doi10.5545/sv-jme.2011.297en_US
dc.identifier.endpage498en_US
dc.identifier.issn0039-2480
dc.identifier.issue07.Augen_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage492en_US
dc.identifier.urihttps://doi.org/10.5545/sv-jme.2011.297
dc.identifier.urihttps://hdl.handle.net/20.500.12684/3710
dc.identifier.volume58en_US
dc.identifier.wosWOS:000306718800007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAssoc Mechanical Engineers Technicians Sloveniaen_US
dc.relation.ispartofStrojniski Vestnik-Journal Of Mechanical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectartificial neural networksen_US
dc.subjectregression analysisen_US
dc.subjectcryogenic treatmenten_US
dc.subjectmachiningen_US
dc.subjectthrust forceen_US
dc.subjectpredictive modellingen_US
dc.titleModelling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysisen_US
dc.typeArticleen_US

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