Light weight, low density with high mechanical properties and corrosion resistance, aluminum is the most important material and is commonly used for high performance applications such as aerospace, military and especially automotive industries. The researchers who participate in these industries are working hard to further decrease the weight of end products according to legal boundaries of greenhouse gases. A lot of research was undertaken to produce thin sectioned aluminum parts with improved mechanical properties. Several alloying element addition were investigated. Yet, nowadays aluminum has not met these expectations. Thus, composite materials, particularly metal matrix composites, have taken aluminum’s place due to the enhancement of mechanical properties of aluminum alloys by reinforcements. This paper deals with the overview of the reinforcements such as SiC, Al2O3 and graphene. Graphene has recently attracted many researcher due to its superior elastic modulus, high fatigue strength and low density. It is foreseen and predicted that graphene will replace and outperform carbon nanotubes (CNT) in near future.

Metal matrix composites (MMC) are finding application in many fields such as aerospace and automobile industries. This is due to their advantages such as light weight and low cost. Among all the available non-traditional machining processes, wire electric discharge machining (WEDM) is found to be a suitable method for producing complex or intricate shapes in composite materials. In this study, an aluminum metal matrix composite (AMMC) with 6% and 8% weight (wt) fraction of Al₂O₃ is prepared through the stir casting process. The fabricated AMMC specimen is machined using WEDM, considering various process parameters such as wt % of reinforcement, gap voltage (Vg), peak current (IP) wire tension (WT) and dielectric pressure (Pd). Output responses such as the machining rate (MR) and surface roughness (R_a) of the slots are analyzed by conducting L₁₈ mixed orthogonal array (OA) experiments. The experiments are analyzed using techniques for order preference by similarity to ideal solution (TOPSIS) and analysis of variance (ANOVA). Based on the analyses, the optimum combination of process parameters for better MR and R_a is as follows: wt % =  6 gm, Vg = 53 V, Ip = 8 A, WT = 11 g, Pd = 13 bar. The optimum level of process parameters for MR and R_a are 1.5 mm/min and 3.648 µm, respectively. Based on ANOVA, the peak current is found to have a significant influence on MR and R_a. Moreover, based on a scanning electron microscope (SEM) image, the presence of micro-ridges, reinforcement, micro-craters, micro-cracks, recast layers and oxide formation are all analyzed on the surface being machined.