Experimental, modeling and optimization study on the mechanical properties of epoxy/high-impact polystyrene/multi-walled carbon nanotube ternary nanocomposite using artificial neural network and genetic algorithm

Abstract

AbstractIn spite of Epoxy resin’s good tensile strength, are brittle in nature and have poor resistance at the front of crack propagation. In enhancing simultaneously the mechanical strength and fracture toughness of epoxy-based nanocomposites, high-impact polystyrene (HIPS) as thermoplastic phase and multi-walled carbon nanotubes (MWCNT) as nanofiller phases are used incorporately to obtain ternary epoxy-based nanocomposites. Tensile, flexural, compression and impact are the four different mechanical properties. Artificial neural network was used to present models for predicting the mechanical behavior of epoxy/HIPS/MWCNT nanocomposites. Also, this model used as a fitness function of genetic algorithm as a powerful optimization method to find the optimum value of the above-mentioned mechanical properties. The effective parameters investigated were HIPS, MWCNT and hardener. From the result, it was found that the combination of HIPS and MWCNT nanofillers significantly increases tensile, compression and impact strength of neat resin by up to 52%, 43% and 334%, respectively, but flexural strength did not change positively. Also, elongation at break for tensile, flexural and compression rose to 223%, 36% and 26% of neat epoxy, respectively. The morphology of fracture surface was studied by scanning electron microscopy.