Enhancing mechanical properties of high-density polyethylene with multi-walled carbon nanotubes: A predictive artificial neural network approach

Humar Kahramanli Ornek; Serafettin Ekinci; Mustafa Tasyurek

Enhancing mechanical properties of high-density polyethylene with multi-walled carbon nanotubes: A predictive artificial neural network approach

Humar Kahramanli Ornek, Serafettin Ekinci, Mustafa Tasyurek

Abstract

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 twin-screw 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.

Keywords

ANN; Carbon nanotubes; High density polyethylene Modeling

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References

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