Prediction of the optimal FSW process parameters for joints using machine learning techniques

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dc.authorid0000-0003-2651-5005en_US
dc.contributor.authorSarsilmaz, Furkan
dc.contributor.authorKavuran, Gürkan
dc.date.accessioned2022-03-22T13:08:36Z
dc.date.available2022-03-22T13:08:36Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.en_US
dc.identifier.citationSarsilmaz, F., & Kavuran, G. (2021). Prediction of the optimal FSW process parameters for joints using machine learning techniques. Materials Testing, 63(12), 1104-1111.en_US
dc.identifier.doi10.1515/mt-2021-0058
dc.identifier.scopus2-s2.0-85138468863en_US
dc.identifier.urihttps://doi.org/10.1515/mt-2021-0058
dc.identifier.urihttps://hdl.handle.net/20.500.12899/780
dc.identifier.wosWOS:000734912300004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKavuran, Gürkan
dc.language.isoenen_US
dc.publisherWalter de Gruyter GmbHen_US
dc.relation.ispartofMaterials Testingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFriction-stir weldingen_US
dc.subjectANNen_US
dc.subjectSVMen_US
dc.subjectaluminum alloysen_US
dc.subjectnelder-mead nonlinear optimization algorithmen_US
dc.titlePrediction of the optimal FSW process parameters for joints using machine learning techniquesen_US
dc.typeArticleen_US

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