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With an increase in the population and industrialization, a lot of valuable natural resources are depleted to prepare and manufacture products. However industrialization on the other hand has waste disposal issues, causing dust and environmental pollution. In this work, Aluminium Metal Matrix Composite is prepared by reinforcing 10 wt% and 20 wt% of wet grinder stone dust particles an industrial waste obtained during processing of quarry rocks which are available in nature. In the composite materials design wear is a very important criterion requiring consideration which ensures the materials reliability in applications where they come in contact with the environment and other surfaces. Dry sliding wear test was carried out using pin-on-disc apparatus on the prepared composites. The results reveal that increasing the reinforcement content from 10 wt% to 20 wt% increases the resistance to wear rate.

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Nickel-based superalloys are gaining importance for their growing usage in aerospace industries. Amidst the various advanced machining processes, electro discharge machining (EDM) is considered to be an important one for its ability to machine materials irrespective of its intrinsic properties. In this study, Inconel 718 is considered as a work material, and an L18 orthogonal array (OA) experimental plan is utilized to machine the work material. The influential factors, which affect the EDM performance characteristics, are identified using analysis of variance (ANOVA). Not much work has been done regarding using grey-Taguchi technique for order of preference by similarity to ideal solution (TOPSIS) methods, although these methods can be easily applied for multi-objective optimization. These methods provide the best results with the available sparse data. The best combination of machining factors is determined using grey-Taguchi and TOPSIS methods. Based on the conducted experiments, voltage (V) and pulse off-time (t_off) show a notable contribution on output performance. The optimal combination of input parameter through grey-Taguchi is found to be 10 A, 30 V, 200 μs, and 20 μs respectively, for the EDM parameters: current (I), V, pulse on-time (t_on) and t_off for improved response. Moreover, the best parameter setting (I = 10 A, V = 30 V, t_on =100 μs and t_off = 20 μs) is identified using the TOPSIS method for the performance measures machining rate (MR), tool wear rate (TWR), overcut (OC), and taper overcut (TOC). Further tool wear behaviour is also studied through scanning electron microscope (SEM) images by varying the voltage.