Data-driven simulations of flank wear of coated cutting tools in hard turning

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dc.authorid0000-0003-1099-4363en_US
dc.authorid0000-0002-7394-1499
dc.authorid0000-0001-8293-0022
dc.contributor.authorÇakan, Ahmet
dc.contributor.authorEvrendilek, Fatih
dc.contributor.authorÖzkaner, Vedat
dc.date.accessioned2021-06-23T19:42:26Z
dc.date.available2021-06-23T19:42:26Z
dc.date.issued2015
dc.departmentBAİBÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractInsurance of surface quality and dimensional tolerances in finish hard turning necessitates the development of accurate predictive models. This study aimed at modeling flank wear of multilayer-coated carbide inserts in finish dry hard turning of AISI 4340 and AISI 52100 hardened steels based on 28 artificial neural networks (ANNs) and the best-fit multiple non-linear regression (MNLR) model. Online-monitored flank wear of multilayer-coated carbide inserts was modeled as a function of the three cutting speeds of 70, 98 and 142 m min(-1), and the two workpieces under the constant feed rate and cutting depth of 0.027 mm min(-1) and 0.2 mm, respectively. Out of the 28 ANNs, 18 ANNs appeared to be capable of better predictions for tool flank wear than the best-fit MNLR model. Probabilistic neural network (PNN) outperformed all the remaining models based on all the training, cross-validation and testing dataset-related performance metrics.en_US
dc.identifier.doi10.5755/j01.mech.21.6.12199
dc.identifier.endpage492en_US
dc.identifier.issn1392-1207
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-84955083347en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage486en_US
dc.identifier.urihttps://doi.org/10.5755/j01.mech.21.6.12199
dc.identifier.urihttps://hdl.handle.net/20.500.12491/8488
dc.identifier.wosWOS:000369210700009en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorEvrendilek, Fatih
dc.language.isoenen_US
dc.publisherKaunas Univ Technolen_US
dc.relation.ispartofMechanikaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCarbide Toolsen_US
dc.subjectOnline Monitoringen_US
dc.subjectData-Driven Modelingen_US
dc.subjectFinish Turningen_US
dc.titleData-driven simulations of flank wear of coated cutting tools in hard turningen_US
dc.typeArticleen_US

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