Prediction of the tensile load of drilled CFRP by artificial neural network

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dc.authorid0000-0001-5519-2917en_US
dc.contributor.authorYenigün, Burak
dc.contributor.authorKılıckap, Erol
dc.date.accessioned2022-12-12T06:39:29Z
dc.date.available2022-12-12T06:39:29Z
dc.date.issued2018en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractThe application areas of carbon fiber reinforced plastics (CFRP) have been increasing day by day. The machining of CFRP with incorrect machining parameters leads in huge loss cost and time. Therefore, it is very important that the composite materials are machined with correct machining parameters. The aim of this paper is to examine the influence of drilling parameters on tensile load after drilling of CFRP. The drilling operations were carried out on Computer Numerical Control (CNC) by Tungsten Carbide (WC), High Speed Steel (HSS) and Brad Spur type drill bits with spindle speeds of 1000, 3000 and 5000 rpm and feed rates of 0.05, 0.10 and 0.15 mm/rev. The results indicate that the surface roughness, delamination and thrust force, were affected by drilling parameters therefore tensile load was also affected by the same parameters. It was observed that increase in surface roughness, delamination and thrust force all lead to the decrease of tensile load of CFRP. If the correct drilling parameters are selected; the decrease in tensile load of CFRP can be saved up to 25%. Furthermore, an artificial neural network (ANN) model has been used to predict of tensile load. The results of the ANN model are in close agreement with the experimental results.en_US
dc.identifier.citationYenigün, B. ve Kılıckap, E. (2018). Prediction of the tensile load of drilled CFRP by artificial neural network. Applied Sciences (Switzerland), 8(4).en_US
dc.identifier.doi10.3390/app8040549
dc.identifier.issn2076-3417
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85044858177
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://www.mdpi.com/2076-3417/8/4/549
dc.identifier.urihttps://hdl.handle.net/11468/11018
dc.identifier.volume8en_US
dc.identifier.wosWOS:000434996400076
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKılıckap, Erol
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCFRPen_US
dc.subjectDrillingen_US
dc.subjectSurface roughness tensile loaden_US
dc.titlePrediction of the tensile load of drilled CFRP by artificial neural networken_US
dc.titlePrediction of the tensile load of drilled CFRP by artificial neural network
dc.typeArticleen_US

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