Dry sliding wear response of LD slag filled poly-ether-ether ketone composites

Abstract

This work reports the development and wear performance evaluation of a new class of poly-ether-ether-ketone (PEEK) based composites filled with an industrial waste called LD slag (LDS). LDS is generated in great amounts from LD furnaces in steel plants worldwide during steel making. The slag particles of average size 100 μm are reinforced in PEEK resin to prepare particulate filled composites of three different compositions (0, 7.5 and 15 wt% of LDS). 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 LDS 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 LDS filled PEEK composites within and beyond the experimental domain.