Intelligent Machining of Shape Memory Alloys

Abstract

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 Ni55.8 Ti was reported. WEDM was carried out by varying four process parameters i.e. servo voltage SV, pulse-on time Pon, pulse-off time Poff, 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; Pon-1µs; Poff-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.