Enhancing Mechanical Properties of High-Density Polyethylene with Multi-Walled Carbon Nanotubes: A Predictive Artificial Neural Network Approach

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dc.authorid0000-0003-0885-5903en_US
dc.authorid0000-0001-9016-8584en_US
dc.authorid0000-0003-2336-7924en_US
dc.contributor.authorEkinci, Şerafettin
dc.contributor.authorTaşyürek, Mustafa
dc.contributor.authorKahramanlı Örnek, Humar
dc.date.accessioned2023-12-28T06:22:43Z
dc.date.available2023-12-28T06:22:43Z
dc.date.issued2023 Ağustosen_US
dc.departmentSelçuk Üniversitesi, Teknoloji Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractComposite materials have been enhanced by incorporating Carbon Nano Tubes (CNTs) into polymers to achieve superior mechanical properties. High-density polyethylene (HDPE), a versatile polymer, can benefit from nanoparticle reinforcement to enhance its mechanical properties. In this research, multi-walled carbon nanotubes (MWCNTs) with weight fractions of 1%, 3%, and 5% were incorporated into polyethylene (PE) through melt blending using a twinscrew extruder. The resulting multi-walled carbon nanotube (MWCNT)/HDPE composite was molded into tensile test bars using the injection technique. Tensile tests were conducted on the samples using a hydraulic tester in accordance with ASTM D 638 standards. To predict properties such as elongation at break, maximum force, and maximum stress, four distinct Artificial Neural Network (ANN) models were developed. Statistical metrics such as R2 , MAE, and RMSE were employed to assess the performance of these models. The outcomes demonstrate that the model trained with the Levenberg–Marquardt (LM) algorithm exhibited superior predictive accuracy compared to the other models.en_US
dc.identifier.citationEkinci, Ş., Taşyürek, M., Kahramanlı Örnek, H., (2023). Enhancing Mechanical Properties of High-Density Polyethylene with Multi-Walled Carbon Nanotubes: A Predictive Artificial Neural Network Approach. Selcuk University Journal of Engineering Sciences, 22(02), 73-79.en_US
dc.identifier.endpage79en_US
dc.identifier.issue2en_US
dc.identifier.startpage73en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/51576
dc.identifier.volume22en_US
dc.institutionauthorEkinci, Şerafettin
dc.institutionauthorTaşyürek, Mustafa
dc.institutionauthorKahramanlı Örnek, Humar
dc.language.isoenen_US
dc.publisherSelçuk Üniversitesien_US
dc.relation.ispartofSelcuk University Journal of Engineering Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectANNen_US
dc.subjectCarbon nanotubesen_US
dc.subjectHigh density polyethyleneen_US
dc.subjectModelingen_US
dc.titleEnhancing Mechanical Properties of High-Density Polyethylene with Multi-Walled Carbon Nanotubes: A Predictive Artificial Neural Network Approachen_US
dc.typeArticleen_US

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