Analysis of thrust force in drilling B4C-reinforced aluminium alloy using genetic learning algorithm

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dc.authoridOzkul, iskender -- 0000-0003-4255-0564;
dc.contributor.authorTaşkesen, Ahmet
dc.contributor.authorAldaş, Kemal
dc.contributor.authorÖzkul, Iskender
dc.contributor.authorKütükde, Kenan
dc.contributor.authorZümrüt, Yavuz
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:28:24Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:28:24Z
dc.date.issued2014
dc.departmentMühendislik Fakültesi
dc.description.abstractThis paper presents an analysis for the prediction of thrust force in drilling of aluminium-based composites, reinforced with boron-carbide B4C produced with the powder-metallurgy (PM) technique. The formulation was derived on experimental bases. The experiments were conducted with various cutting tools and parameters on conditions of dry machining in a computer numerical control (CNC) vertical machining centre. The thrust forces were obtained by measuring the forces between the drill bit and the work pieces during the experiments. In the experiments, particle fraction, feed rate, spindle speed and drill bit type were used as input parameters, and thrust force was the output data for the gene expression programming (GEP) software. Customizing for formulation in order to describe the problem was generated by GEP, and it was analysed from different perspectives and verified the reliability of equation.
dc.description.sponsorshipGazi University [07/2008-8]
dc.description.sponsorshipThis research was supported by Gazi University under Project Number 07/2008-8. The authors wish to thank TOBB Economy and Technology University for providing laboratory facilities during the research work. The authors are grateful to Mitas Civata and Mr. Serdar Iskender for carrying out the tensile and impact tests on the fabricated composite materials.
dc.identifier.doi10.1007/s00170-014-6062-6
dc.identifier.endpage245en_US
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.issue01.Apren_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage237en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-014-6062-6
dc.identifier.urihttps://hdl.handle.net/20.500.12451/6033
dc.identifier.volume75en_US
dc.identifier.wosWOS:000343722400019
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofİnternational Journal of Advanced Manufacturing Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGene Expression Programming
dc.subjectComposite
dc.subjectDrilling
dc.subjectThrust Force
dc.titleAnalysis of thrust force in drilling B4C-reinforced aluminium alloy using genetic learning algorithm
dc.typeArticle

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