Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM

Abstract

The unique ability of shape memory alloy or smart alloy to memorize its previous form has drawn notable attention recently in a wide range of commercial applications. In efforts to precisely predict and compare few significant WEDM machinability aspects like surface roughness [arithmetic mean roughness (Ra), root mean square roughness (Rq) and maximum peak-to-valley height (Rz)] and micro-hardness (MH) of shape memory alloy nitinol, general regression neural network (GRNN) model was developed. Five critical machining parameter, namely pulse-on time (TON), discharge current (I), wire feed (WF), wire tension (WT) and flushing pressure (FP) were taken as machining input for the experiments. The grid search method was employed to minimize cross-validation error. The developed GRNN model predicted the responses within ±10% error indicating GRNN as a competent strategy to predict WEDM responses. A multi-criteria decision making (MCDM) approach, Fuzzy logic coupled with multi-objective optimization on the basis of ratio analysis (MOORA) is introduced to optimize different correlated responses. The parametric combination, TON = 12 µs, I = 10 A, WT = 12 N, WS = 150 mm/s. and FP = 8 Bar, were found to yield the preferred results. ANOVA test shows the efficiency of this hybrid MCDM model. Confirmation test has been done to validate the optimum process combination which demonstrates the improvement in WEDM responses. FESEM micrographs identifies lump of debris, micro cracks, pockmarks and recast layer in the machined surfaces.