Parametric optimization of electric discharge machining of Ni 55.65Ti based shape memory alloy using NSGA II with TOPSIS

Abstract

Nickel–Titanium based shape memory alloys (SMAs) are a unique category of smart materials that possess remarkable properties like super-elasticity, shape memory effect, high wear and corrosion resistance, thereby rendering them appropriate for uses such as composite structures, aerospace, and biomedical uses etc. These distinctive features make these alloys ‘difficult in machining’ using the traditional approach of machining process. In the present investigation, machinability aspects of Ni55.65Ti-SMAs using non-traditional electric discharge machining process has been studied. The effect of input process parameters i.e., pulse time ON, duty factor, peak current on the surface roughness and material removal rate has been investigated. The output responses viz. Minimum surface roughness along with maximum material removal rate having values of 6.828 μm and 4.552 mm3/sec respectively were achieved. Furthermore, empirical modelling and ANOVA study have been executed based on the full factorial design analysis. The non-dominated sorting genetic algorithm IIalgorithm IIalg2 commonly known as NSGA II, used crowding distance approach for multi-objective process optimization. The results obtained with NSGA-II were again ranked using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to give a more thorough understanding of the solution space and opt for the best possible solution.