Ekinci, ŞerafettinTaşyürek, MustafaKahramanlı Örnek, Humar2023-12-282023-12-282023 AğustEkinci, 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.https://hdl.handle.net/20.500.12395/51576Composite 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.eninfo:eu-repo/semantics/openAccessANNCarbon nanotubesHigh density polyethyleneModelingArticle2227379