Experimental optimisation of the tribological behaviour of Al/SiC/Gr hybrid composites based on Taguchi's method and artificial neural network

This paper presents the investigation of tribological behaviour of aluminium hybrid composites with Al-Si alloy A356 matrix, reinforced with 10 wt% silicon carbide and 0, 1 and 3 wt% graphite (Gr) with the application of Taguchi's method. Tribological investigations were realized on block-on-disc tribometer under lubricated sliding conditions, at three sliding speeds (0.25, 0.5 and 1 m/s), three normal loads (40, 80 and 120 N) and at sliding distance of 2400 m. Wear rate and coefficient of friction were measured within the research. Analysis of the results was conducted using ANOVA technique, and it showed that the smallest values of wear and friction are observed for hybrid composite containing 3 wt% Gr. The prediction of wear rate and coefficient of friction was performed with the use of artificial neural network (ANN). After training of the ANN, the regression coefficient was obtained and it was equal to 0.98905 for the network with architecture 3-20-30-2.