Enhancing Mechanical Properties of High-Density Polyethylene with Multi-Walled Carbon Nanotubes: A Predictive Artificial Neural Network Approach
Yükleniyor...
Dosyalar
Tarih
2023 Ağustos
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Selçuk Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Composite 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.
Açıklama
Anahtar Kelimeler
ANN, Carbon nanotubes, High density polyethylene, Modeling
Kaynak
Selcuk University Journal of Engineering Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
22
Sayı
2
Künye
Ekinci, 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.
