Design of the ultrahigh molecular weight polyethylene composites with multiple nanoparticles: An artificial intelligence approach

Abstract

This study proposes a suitable composite material for acetabular cup replacements in hip joint that involves ultrahigh molecular weight polyethylene, a clinically proven material, as the matrix. To design new ultrahigh molecular weight polyethylene composites with multiple reinforcements for the improvement in mechanical and tribological performance, artificial neural network and genetic algorithm, the two artificial intelligence techniques, are employed. Published reports on the use of ultrahigh molecular weight polyethylene reinforced with multi-walled carbon nanotube and graphene are used as database to develop two artificial neural network models for Young's modulus and tensile strength. The optimum solutions are obtained using genetic algorithm, where the artificial neural network models are used as the objective functions. Two different composites, derived from the optimum solutions, are made reinforcing both multi-walled carbon nanotube and graphene. Tensile and wear tests show significant enhancement in the properties. The structures of the composites are also characterized, and wear mechanisms are discussed.