Synergistic effects of TiC/GNP strengthening on the mechanical and tribological properties of Al6061 matrix composites coupled with process optimization by artificial neural network

This study presents the strengthening of Al6061 alloy with titanium carbide (TiC) and graphene nano-platelets (GNPs), and the synergetic effect of these reinforcements on the microstructure, mechanical, and wear properties. The raw materials were combined in powder form in a hot press under a range of conditions (sintering temperature: 450 °C, 500 °C, or 550 °C, sintering time: 15, 30, or 45 minutes; and sintering pressure: 50, 100, or 150 MPa) to produce strengthened alloys containing 4, 8, or 12 wt.% TiC and 0.5, 1, or 1.5 wt.% GNP. A range of scanning electron microscope (SEM) and energy dispersive x-ray spectroscopy (EDS) showed that the reinforcement was uniformly distributed across the matrix. Raman spectroscopy showed that not there were no structural defects introduced into GNP during the mixing process of composite powders. Wear tests showed that minimum wear loss was obtained with an Al6061/TiC (8 wt.%)/GNP (1 wt.%) composite sintered at 500 °C and 100 MPa for 15 minutes. This same composite also displayed a decrease in the coefficient of friction (COF) of up to 69% when compared to unreinforced material. Examination of the areas of wear by SEM showed mixed type wear to be the dominant wear mechanism. Artificial neural network (ANN) was used to identify the impacts of different production parameters on wear loss. The trained ANN model was found to be highly accurate in predicting the wear properties of Al6061/TiC/GNP composites and could be used to generate an optimum set of production parameters to minimize wear loss without the need for costly and time-consuming experimentation.