Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S

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dc.authorid0000-0003-1753-7712en_US
dc.contributor.authorÇelik, Yahya Hışman
dc.contributor.authorKılıçkap, Erol
dc.contributor.authorYardımeden, Ahmet
dc.date.accessioned2021-11-08T09:31:34Z
dc.date.available2021-11-08T09:31:34Z
dc.date.issued2017-10-15en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Makine Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut.en_US
dc.identifier.citationÇelik, Y.H., Kılıçkap, E., Yardımeden, A. (2017). Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S. Applied Sciences-Basel, 7 (10), pp.1-12. DOI:https://dx.doi.org/10.3390/app7101064en_US
dc.identifier.endpage12en_US
dc.identifier.issn2076-3417
dc.identifier.issue10en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://dx.doi.org/10.3390/app7101064
dc.identifier.urihttps://hdl.handle.net/20.500.12402/3881
dc.identifier.volume7en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherApplied Sciences-Baselen_US
dc.relation.isversionof10.3390/app7101064en_US
dc.relation.journalApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectCutting Forceen_US
dc.subjectTool Wearen_US
dc.subjectSurface Roughnessen_US
dc.subjectANN-RSMen_US
dc.titleMathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242Sen_US
dc.title.alternativeTi-6242S'nin frezelenmesinde yapay sinir ağı ve tepki yüzeyi metodolojisi kullanılarak kesme kuvveti, takım aşınması ve yüzey pürüzlülüğünün matematiksel modellemesi ve optimizasyonuen_US
dc.typeArticleen_US

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