Prediction on the wear rate of epoxy composites reinforced micro-filler of the natural material residue using Taguchi – neural network

Abstract

The abrasive wear rate of epoxy composites reinforced with fillers sourced from recycled natural waste consisting of pollen of palm (PPW) and seashells (SSW) was studied. Due to the importance of polymer composites used in the tribological couplings of machinery structures, as well as their possible use in brake pads as alternative materials for harmful components in environmentally polluted asbestos, the current research seeks to develop the tribological properties of composite materials reinforced with natural fillers and environmentally friendly. The research investigated the effect of two factors, the weight percentage of natural filler wt. % (0.5 %,1 %, and 1.5 %) and testing loads (1000 g, 2000 g, 3000 g) upon the wear resistance of epoxy composites. The importance of developing epoxy compounds is evident, especially since their work does not require lubricating conditions in various industrial fields, and therefore the development of their bonding properties will increase their operational life and achieve economic benefit for the industrial sector and the environment at the same time. The epoxy composites were subjected to abrasive wear tests under dry friction conditions using a pin-on-disc system. Signal-to-noise (S/N) analysis is adopted to study the influence of the two factors, wt. % and test loads, upon the tribological wear resistance of epoxy composites. A predictive model depending on the regression equation was developed to predict the wear resistance of epoxy composites. The results showed an improvement in the wear resistance of the composite material compared to the epoxy sample without filling by about 47 %. The optimum condition for wear resistance of epoxy composites has been achieved with a weight ratio of (1.5 %) and an applied load of 1000 g