Dry Sliding Wear Response of Fly Ash Filled Poly-Ether-Ether-Ketone Composites

Abstract

This paper reports the development and wear performance evaluation of a new class of poly-ether-ether-ketone (PEEK) based composites filled with fly ash, an industrial waste. Fly ash is generated in large amounts from furnaces in coal based thermal power plants worldwide during power generation. The fly ash particles of average size 100 micron are reinforced in PEEK resin to prepare particulate filled composites of three different compositions (0, 7.5 and 15 wt% of fly ash). Dry sliding wear trials are conducted following a well-planned experimental schedule based on design of experiments (DOE) using a standard pin-on-disc test set-up. Significant control factors predominantly influencing the wear rate are identified. Effect of fly ash content on the wear rate of PEEK composites under different test conditions is studied. The results of the experiments are compared with that of results obtained from wear test of TiO2-PEEK composites under same experimental conditions. An Artificial Neural Networks (ANN) approach taking into account training and test procedure to predict the dependence of wear behavior on various control factors is implemented. This technique helps in saving time and resources for large number of experimental trials and predicts the wear response of fly ash filled PEEK composites within and beyond the experimental domain.