Wear prediction of 3D-printed acrylonitrile butadiene styrene-carbon nanotube nanocomposites at elevated temperatures

Basit Öğe Kaydı
dc.authorid0000-0002-2280-6529
dc.authorid0000-0002-3361-6528
dc.authorid0000-0003-0792-249X
dc.contributor.authorFeratoglu, Kamil
dc.contributor.authorIstif, Ilyas
dc.contributor.authorGumus, Omer Yunus
dc.date.accessioned2026-02-12T21:05:24Z
dc.date.available2026-02-12T21:05:24Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn this study, multi-wall carbon nanotube (MWCNT) reinforced acrylonitrile butadiene styrene (ABS) nanocomposite filaments are produced. Filaments are examined through thermogravimetric analysis (TGA) and definitive scanning calorimetry (DSC) analysis. Produced nanocomposite filaments are used in the fused deposition modeling (FDM) process to manufacture parts. Wear tests are conducted on 3D-printed parts using wear test apparatus with an attached heating module under different ambient temperatures. Hence, the influence of CNT reinforcement, along with different FDM process parameters and varying test conditions on the wear behavior of 3D-printed ABS-CNT parts, are examined. Worn surfaces of the specimens are examined by scanning electron microscopy (SEM). Nonlinear autoregressive exogenous (NARX) models are proposed for the prediction of the wear behavior of 3D-printed ABS-CNT nanocomposites. While wear rate is taken as output, ambient temperature and amount of nanofiller are accounted as input parameters along with the variation of coefficient of friction (COF) which is obtained from measured frictional force and three input-one output model structure is proposed for NARX. The use of multiple input-single output (MISO) model structure and examining the wear behavior of 3D-printed ABS-CNT samples under different wear test conditions with different FDM process parameters are the novelties in this work.
dc.identifier.doi10.1515/polyeng-2022-0225
dc.identifier.endpage332
dc.identifier.issn0334-6447
dc.identifier.issn2191-0340
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85150415388
dc.identifier.scopusqualityQ2
dc.identifier.startpage318
dc.identifier.urihttps://doi.org/10.1515/polyeng-2022-0225
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6945
dc.identifier.volume43
dc.identifier.wosWOS:000952598800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofJournal of Polymer Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectartificial neural network
dc.subjectfused deposition modelling
dc.subjectidentification
dc.subjectnanocomposite polymers
dc.subjectwear behaviour
dc.titleWear prediction of 3D-printed acrylonitrile butadiene styrene-carbon nanotube nanocomposites at elevated temperatures
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu