ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel

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dc.contributor.authorAkıncıoğlu, Sıtkı
dc.contributor.authorMendi, Faruk
dc.contributor.authorÇiçek, Adem
dc.contributor.authorAkıncıoğlu, Gülşah
dc.date.accessioned2020-04-30T22:39:25Z
dc.date.available2020-04-30T22:39:25Z
dc.date.issued2013
dc.departmentDÜ, Gümüşova Meslek Yüksekokuluen_US
dc.descriptionAKINCIOGLU, Sitki/0000-0003-4073-4837;en_US
dc.descriptionWOS: 000323105600016en_US
dc.description.abstractThis paper focuses on artificial neural network (ANN)-based modeling of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills. A number of drilling experiments were conducted at all combinations of different cutting speeds (50, 55, 60, and 65 m/min) and feed rates (0.063 and 0.08 mm/rev) to obtain training and testing data. The experimental results showed that the surface roughness (Ra) and roundness error (Re) values were obtained with the TiN monolayer- and TiAlN/TiN multilayer-coated drills, respectively. Using some of the experimental data in training stage, an ANN model was developed. To evaluate the performance of the developed ANN model, ANN predictions were compared with the experimental results. It was found that the determination coefficient values are more than 0.99 for both training and test data. Root mean square error and mean error percentage values were very low. ANN results showed that ANN can be used as an effective modeling technique in accurate prediction of the Ra and Re.en_US
dc.identifier.doi10.1007/s00170-012-4719-6en_US
dc.identifier.endpage207en_US
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.issue01.Apren_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage197en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-012-4719-6
dc.identifier.urihttps://hdl.handle.net/20.500.12684/2720
dc.identifier.volume68en_US
dc.identifier.wosWOS:000323105600016en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal Of Advanced Manufacturing Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectCemented carbide drillsen_US
dc.subjectSurface roughnessen_US
dc.subjectRoundness erroren_US
dc.titleANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steelen_US
dc.typeArticleen_US

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