Shot Peening Process Effects on Metallurgical and Mechanical Properties of 316 L Steel via: Experimental and Neural Network Modeling

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dc.authoridMaleki, Erfan/0000-0002-5995-1869
dc.contributor.authorMaleki, E.
dc.contributor.authorUnal, O.
dc.date.accessioned2024-09-29T15:54:37Z
dc.date.available2024-09-29T15:54:37Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn the present study, a comprehensive investigation was accomplished on shot peening of AISI 316 L steel with a wide range of Almen intensity and surface coverage. Various experiments were performed to characterize the microstructure and mechanical properties of the peened specimens. For the modeling of the process, artificial neural network was used and the obtained experimental results were employed as data-set to develop the network. Modeling results have remarkable agreement with the experiments and then parametric analysis were applied based on the predicted values of the model. Graphicen_US
dc.identifier.doi10.1007/s12540-019-00448-3
dc.identifier.endpage276en_US
dc.identifier.issn1598-9623
dc.identifier.issn2005-4149
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85074029309en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage262en_US
dc.identifier.urihttps://doi.org/10.1007/s12540-019-00448-3
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4186
dc.identifier.volume27en_US
dc.identifier.wosWOS:000616325200005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKorean Inst Metals Materialsen_US
dc.relation.ispartofMetals and Materials Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAISI 316L stainless steelen_US
dc.subjectShot peeningen_US
dc.subjectSimulationen_US
dc.subjectOptimizationen_US
dc.titleShot Peening Process Effects on Metallurgical and Mechanical Properties of 316 L Steel via: Experimental and Neural Network Modelingen_US
dc.typeArticleen_US

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