Prediction of bearing strength of two serial pinned/bolted composite joints using artificial neural networks

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dc.contributor.authorŞen, Faruk
dc.contributor.authorKömür, M. Aydın
dc.contributor.authorSayman, Onur
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:29:48Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:29:48Z
dc.date.issued2010
dc.departmentMühendislik Fakültesi
dc.description.abstractThe aim of this study is to investigate the improvement of an artificial neural network (ANN) method for the prediction of bearing strength of two serial pinned/bolted E-glass reinforced epoxy composite joints. The experimental data from the previous study with different geometrical parameters without torque and various applied torque were used for developing the ANN model. Comparisons of ANN results with desired values showed that there is a good agreement between input and output variables of the experimental data. Therefore, ANN was illustrated to be a valid powerful tool for the prediction of bearing strength of two serial pinned/bolted composite joints.
dc.identifier.doi10.1177/0021998309353344
dc.identifier.endpage1377en_US
dc.identifier.issn0021-9983
dc.identifier.issue11en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage1365en_US
dc.identifier.urihttps://doi.org/10.1177/0021998309353344
dc.identifier.urihttps://hdl.handle.net/20.500.12451/6209
dc.identifier.volume44en_US
dc.identifier.wosWOS:000278072500006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications LTD
dc.relation.ispartofJournal of Composite Materials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Network
dc.subjectBearing Strength
dc.subjectBolted Joint
dc.subjectLaminated Composites
dc.titlePrediction of bearing strength of two serial pinned/bolted composite joints using artificial neural networks
dc.typeArticle

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