Influence of Nanographite on Dry Sliding Wear Behaviour of Novel Encapsulated Squeeze Cast Al-Cu-Mg Metal Matrix Composite Using Artificial Neural Network

This paper investigates the dry sliding wear behaviour of squeeze cast Al-Cu-Mg reinforced with nanographite metal matrix composites. The experimental study employed the Taguchi method. The Taguchi method plays a significant role in analyzing aluminium matrix composite sliding tribological behaviour. Specifically, this method was found to be efficient, systematic, and simple relative to the optimization of wear and friction test parameters such as load (10, 20, and 30), velocity (0.75, 1.5, and 2.25 m/s), and nanographite (1, 3, and 5 wt%). The optimization and results were compared with the artificial neural network. An orthogonal array L27 was employed for the experimental design. Analysis of variance was carried out to understand the impact of individual factors and interactions on the specific wear rate and the coefficient of friction. The wear mechanism, surface morphologies, and composition of the composites have been investigated using scanning electron microscopy with energy-dispersive X-ray spectroscopy. Results indicated that wt% addition of nanographite and increase of sliding speed led to a decrease in the coefficient of friction and wear rate of tested composites. Furthermore, individual parameter interactions revealed a smaller impact. The interactions involved wt% of nano-Gr and sliding speed, sliding speed and normal load, and wt% of nano-Gr and normal load. This inference was informed by the similarity between the results obtained ANN, ANOVA, and the experimental data.