Optimization of Machining Variables During Nano-powder-Mixed µ-EDM of NiTi Shape Memory Alloy

Nickel-titanium (NiTi) shape memory alloy (SMA) is one of the smart materials which has a vast application in the biomedical and aerospace industry. The inclusion of conductive powder into the dielectric enhanced the machining efficiency of micro-electrical discharge machining (µ-EDM). Thus, the present study investigates the impact of process parameters like gap voltage (V), powder concentration (PC), and pulse on time (ton) towards the material removal rate (MRR) and surface roughness (SR) amid graphene nano-powder-added µ-ED milling on NiTi SMA. It is decided to use Taguchi’s L9 orthogonal array for the experiment. It has been found that adding graphene nanoparticles considerably enhanced the MRR and decreased the SR of the milled micro-channel. Taguchi’s grey relational analysis (GRA) has been applied for multi-response optimization to find the maximum MRR and minimum SR. It is discovered that the GRA method improves all the responses and provides better machining efficiency.