Tribological characteristics of additively manufactured 316 stainless steel against 100 cr6 alloy using deep learning

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dc.authoridShibi, Sherin/0000-0002-7942-2438
dc.authoridGupta, Munish/0000-0002-0777-1559
dc.authoridKORKMAZ, Mehmet Erdi/0000-0002-0481-6002
dc.contributor.authorGupta, Munish Kumar
dc.contributor.authorKorkmaz, Mehmet Erdi
dc.contributor.authorShibi, C. Sherin
dc.contributor.authorRoss, Nimel Sworna
dc.contributor.authorSingh, Gurminder
dc.contributor.authorDemirsoz, Recep
dc.contributor.authorJamil, Muhammad
dc.date.accessioned2024-09-29T16:00:51Z
dc.date.available2024-09-29T16:00:51Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractUnder different working conditions, the tribological characteristics of materials show a complicated and nonlinear relation. As a result, it is crucial to advance tribology by prioritising a data-driven strategy to estimate service capability in order to expedite the material design and preparation. With this aim, the present work firstly deals with the implementation of novel deep learning technologies in predicting tribological characteristics of additively manufactured and casted 316 stainless steel against 100 cr6 alloy. The coefficient of friction and frictional forces data from ball-on-flat experiments were used to develop the different deep learning models i.e., CNN, CNN-LSTM, and ATTENTION based CNN. Then, the wear tracks of tested samples were analysed with the SEM analysis. According to the findings of the wear rate, the AM material wears with an average of 58% less intensity than the casted material. In addition, the performance of the CNN Attention model demonstrated higher levels of accuracy and lower loss metrics in comparison to the CNN and CNN-LSTM classifiers.en_US
dc.description.sponsorshipNorwegian Financial Mechanism 2014-2021 [2014 2021, 2020/ 37/K/ST8/02795]; [2020/37/K/ST8/02795]en_US
dc.description.sponsorshipThe research leading to these results heas recieved funding form the nrowegian financial mechanishm 2014 2021 project contract no 2020/37/K/ST8/02795.en_US
dc.identifier.doi10.1016/j.triboint.2023.108893
dc.identifier.issn0301-679X
dc.identifier.issn1879-2464
dc.identifier.scopus2-s2.0-85169050254en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2023.108893
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5404
dc.identifier.volume188en_US
dc.identifier.wosWOS:001093004400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofTribology Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdditive manufacturingen_US
dc.subjectWearen_US
dc.subjectTribologyen_US
dc.subjectDeep learningen_US
dc.subjectSS 316en_US
dc.titleTribological characteristics of additively manufactured 316 stainless steel against 100 cr6 alloy using deep learningen_US
dc.typeArticleen_US

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