No abstract

The improved performance of polymers and their composites in industrial and structural applications by the addition of particulate fillers has shown a great promise and so has lately been the subject of considerable interest. In the present study, titanium oxide (TiO 2 ) particles of average size 75 μm are reinforced in unsaturated polyester resin to prepare particulate filled composites of three different compositions (with 0, 10, and 20 wt% of TiO 2 ). Dry sliding wear trials are conducted following 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 TiO 2 content on the wear rate of polyester composites under different test conditions is studied. 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 TiO 2 -polyester composites beyond the experimental domain.