Intelligent Machining of Shape Memory Alloys

Shape memory alloys are important biomaterials but difficult-to-machine (DTM). Their machining needs to be done using intelligent techniques to obtain a better machinability. Hybrid optimization is one of such techniques which can perform modeling and optimization of machining parameters for the best values of machinability in-dicators. Wire electric discharge machining (WEDM) of shape memory alloy has been found as a prominent alternate to the conventional machining techniques; however it needs the assistance of intelligent techniques to machine such materials to obtain the optimum values of machinability indicators. In this paper, WEDM of shape memory alloy Ni_55.8 Ti was reported. WEDM was carried out by varying four process parameters i.e. servo voltage SV, pulse-on time P_on, pulse-off time P_off, and wire feed rate WF using Taguchi L₁₆ robust design of experiment technique. A hybrid optimization technique TOPSIS-Fuzzy-PSO has been successfully used to optimize these parameters (SV-50V; P_on-1µs; P_off-17 µs; WF-4 m/min) for the best possible values of material removal rate (MRR) – 0.049 g/min, maximum roughness – 11.45 µm, and recast layer – 22.10 µm simultaneously.