The comparative evaluation of the wear behavior of epoxy matrix hybrid nano-composites via experiments and machine learning models

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dc.authoridhttps://orcid.org/0000-0002-0768-7162
dc.authoridhttps://orcid.org/0000-0001-9830-7622
dc.authoridhttps://orcid.org/0000-0003-4708-6078
dc.authoridhttps://orcid.org/0000-0002-3617-9749
dc.authoridhttps://orcid.org/0000-0001-8559-3949
dc.contributor.authorAydın, Fatih
dc.contributor.authorKaraoğlan, Kürşat Mustafa
dc.contributor.authorPektürk, Hatice Yakut
dc.contributor.authorDemir, Bilge
dc.contributor.authorKarakurt, Volkan
dc.contributor.authorAhlatçı, Hayrettin
dc.date.accessioned2025-01-14T09:08:49Z
dc.date.available2025-01-14T09:08:49Z
dc.date.issued2025-04
dc.departmentMeslek Yüksekokulları, Türkiye Odalar ve Borsalar Birliği Teknik Bilimler Meslek Yüksekokulu
dc.description.abstractThis study evaluated the wear behavior of multiwall carbon nanotube (MWCNT) doped non-crimp fabric carbon fiber reinforced polymer (NCF-CFRP) composites produced through vacuum infusion. Compared to 0 wt% MWCNT reinforced composite, the wear loss of 1 wt% MWCNT reinforced composite under loads of 10 N and 30 N decreased by 48.1 % and 61.1 %, respectively, for sliding distance of 1000 m. Additionally, the study evaluated various Machine Learning models including Deep Multi-Layer Perceptron (DMLP), Random Forest Regression, Gradient Boosting Regression, Linear Regression (LR), and Polynomial Regression for predicting wear loss. The DMLP model exhibited enhanced predictive capabilities in the testing phase (R²=0.9726) compared to its training performance (R²=0.9531), while the LR model maintained stable performance characteristics between training (R²=0.9712) and testing (R²=0.9454) phases.
dc.identifier.citationAydın, F., Karaoğlan, K.M., Pektürk, H.Y., Demir, B., Karakurt, V., & Ahlatçı, H. (2024). The comparative evaluation of the wear behavior of epoxy matrix hybrid nano-composites via experiments and machine learning models. Tribology International.
dc.identifier.doi10.1016/j.triboint.2024.110451
dc.identifier.issn0301-679X
dc.identifier.issn1879-2464
dc.identifier.scopus2-s2.0-85211081204
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2024.110451
dc.identifier.urihttps://hdl.handle.net/20.500.14619/14986
dc.identifier.volume204
dc.identifier.wosWOS:001377903400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofTribology International
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCarbon fiber
dc.subjectMachine learning
dc.subjectMWCNT
dc.subjectQuadriaxial non-crimp fabric
dc.subjectWear behavior
dc.subjectWear loss prediction
dc.titleThe comparative evaluation of the wear behavior of epoxy matrix hybrid nano-composites via experiments and machine learning models
dc.typeArticle
oaire.citation.volume204

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