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.

Dry Sliding Wear Behaviour of Magnesium nanocomposites using Response Surface Methodology

In this study, a pure magnesium material reinforced with 0.5, 1, 1.5, and 2 weight % of CaO was prepared through disintegrated melt deposition technique. Nanocomposites were investigated for their sliding wear behaviour in dry condition at room temperature. Amount of CaO, Load, sliding distance, and Sliding velocity were selected as input design parameters at their five-level in central composite design using Minitab 18.1 statistical software. The influence of design parameters on wear loss is reported through the Response Surface Methodology (RSM). ANOVA was used to confirm the soundness of the developed regression equation. The results indicate the contribution of linear, quadratic, and interaction terms of design parameters on response. 3D response surface and 2D contour plots are indicated the interaction effect. The result shows that an increase in sliding velocity contributes to a decrease in the wear loss of the composites because of the emergence of protective oxidative layer at the surfaces of the pins, which is confirmed through FESEM and EDAX analysis of the pin surfaces. Wear loss of the material decreased as amount of CaO increased. The ANOVA analysis concluded that the sliding distance and load contribute significantly to wear loss of the composites and their percentage of contribution is 64.02 % and 3.69%.