FATIGUE LIFE PREDICTIONS OF METAL MATRIX COMPOSITES USING ARTIFICIAL NEURAL NETWORKS

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dc.contributor.authorUygur, İlyas
dc.contributor.authorÇiçek, Adem
dc.contributor.authorToklu, Ethem
dc.contributor.authorKara, Resul
dc.contributor.authorSarıdemir, Suat
dc.date.accessioned2020-05-01T12:10:03Z
dc.date.available2020-05-01T12:10:03Z
dc.date.issued2014
dc.departmentDÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.descriptionKara, Resul/0000-0001-8902-6837en_US
dc.descriptionWOS: 000333983900016en_US
dc.description.abstractIn this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.en_US
dc.identifier.doi10.2478/amm-2014-0016en_US
dc.identifier.endpage103en_US
dc.identifier.issn1733-3490
dc.identifier.issn2300-1909
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage97en_US
dc.identifier.urihttps://doi.org/10.2478/amm-2014-0016
dc.identifier.urihttps://hdl.handle.net/20.500.12684/5955
dc.identifier.volume59en_US
dc.identifier.wosWOS:000333983900016en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPolska Akad Nauk, Polish Acad Sciences, Inst Metall & Mater Sci Pasen_US
dc.relation.ispartofArchives Of Metallurgy And Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMMCsen_US
dc.subjectFatigue life predictionen_US
dc.subjectArtificial neural networksen_US
dc.titleFATIGUE LIFE PREDICTIONS OF METAL MATRIX COMPOSITES USING ARTIFICIAL NEURAL NETWORKSen_US
dc.typeArticleen_US

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