The investigation of tribological behaviour of AA7178 base alloy matrix reinforced with varying weight percentage of nano TiO2 particles (0,1,2 and 3%) using artificial neural network (ANN) and Taguchi is presented in this paper. Scanning electron microscope(SEM) with Energy Dispersive Spectroscopy (EDAX) was used to study the microstructural as well as wear behaviour of the nanocomposite. The SEM image confirms that abrasive and adhesive wear was responsible for worn-out surface. ANN with Taguchi model was used to obtained the best values for input process parameters (sliding speed, distance, load and weight percentage) to minimize the output values (Coefficient of friction and wear rate). Coefficient of friction and wear rate was mainly affected by weight percentage of nano TiO2 by 60.95% and 57.33% respectively. The efficiency of ANN model was better compared to Taguchi model.
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