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dc.contributor.authorErsözlü, İsmail
dc.contributor.authorÇelik, Sare
dc.date.accessioned2020-01-27T12:30:07Z
dc.date.available2020-01-27T12:30:07Z
dc.date.issued2019en_US
dc.identifier.isbn0023-432X
dc.identifier.issn1338-4252
dc.identifier.urihttps://hdl.handle.net/20.500.12462/10610
dc.descriptionÇelik, Sare (Balikesir Author)en_US
dc.description.abstractThe optimization of the friction welding parameters through experimental studies does not only cause loss of time and materials but also increases the cost. In this study, an Artificial Neural Network (ANN) model is developed for the analysis of the correlation between the friction welding parameters and tensile strength of both AISI 316 austenitic-stainless steel and Ck 45 steel. The input parameters of the model are friction time, friction pressure and upset pressure while tensile strength is the output. Experimental data are used to train and test the neural network. A good correlation was obtained between the experimental values and the ANN model prediction (R-2 = 0.9711). By using this model, the number of experiments to obtain optimal parameters of friction welding and number of tensile tests could be minimized.en_US
dc.language.isoturen_US
dc.publisherRedakcia Kovove Materialyen_US
dc.relation.isversionof10.4149/km_2019_3_199en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFriction Weldingen_US
dc.subjectAISI 316 Stainless Steelen_US
dc.subjectCk 45 Steelen_US
dc.subjectMechanical Propertiesen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.titleArtificial Neural Network application to the friction welding of AISI 316 and Ck 45 steelsen_US
dc.typearticleen_US
dc.relation.journalKovove Materialy-Metallic Materialsen_US
dc.contributor.departmentMühendislik Fakültesien_US
dc.identifier.volume57en_US
dc.identifier.issue3en_US
dc.identifier.startpage199en_US
dc.identifier.endpage205en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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