Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

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dc.authoridMaleki, Erfan/0000-0002-5995-1869
dc.authoridUNAL, Okan/0000-0001-6392-0398
dc.authoridReza Kashyzadeh, Kazem/0000-0003-0552-9950
dc.contributor.authorMaleki, Erfan
dc.contributor.authorUnal, Okan
dc.contributor.authorSeyedi Sahebari, Seyed Mahmoud
dc.contributor.authorReza Kashyzadeh, Kazem
dc.contributor.authorDanilov, Igor
dc.date.accessioned2024-09-29T16:08:07Z
dc.date.available2024-09-29T16:08:07Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 mu m were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10-15 mu m had the highest effects on fatigue life improvement.en_US
dc.identifier.doi10.3390/jmse10020128
dc.identifier.issn2077-1312
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85123533113en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/jmse10020128
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7369
dc.identifier.volume10en_US
dc.identifier.wosWOS:000762270000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofJournal of Marine Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectfatigue lifeen_US
dc.subjectcoatingen_US
dc.subjectdeep neural networken_US
dc.subjectoptimizationen_US
dc.subjectpredictionen_US
dc.titleApplication of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatingsen_US
dc.typeArticleen_US

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