Electrochemical drilling of NiTi: Parametric influence on machining quality using RSM and FPA

Abstract

Nickel–Titanium (NiTi) shape memory alloy is widely used in aerospace, biomedical, and micro-actuation applications due to its unique shape memory and superelastic properties; however, its poor machinability poses significant challenges for conventional machining processes. Electrochemical drilling (ECD) emerges as a promising non-traditional machining technique for producing high-quality micro-holes in NiTi without inducing tool wear and thermal damage. The present work focuses on determining the optimal parametric data set during ECD of NiTi shape memory alloy. The rate of material removal (RMR), overcut (OC), taper angle (TA), and circularity error (CE) are considered as performance measuring indices. Experiments are conducted using a central composite design (CCD) based RSM considering input parameters such as current (I), voltage (V), and inter-electrode gap (IEG). The accepted models are analyzed using an artificial neural network (ANN) to find out the error between the predicted Value and the Model data. To optimize the experiments, a metaheuristic flower pollination algorithm (FPA) has been implemented, both as a single-objective and multi-objective optimization technique to determine the best parameter setting (I = 22 Amp, V = 6 volts, IEG = 0.5 mm). The parametric optimal results obtained using the FPA algorithm are RMR (mm³/min) 0.48893, OC (mm) 0.054187, TA (degree) 0.33221, and circularity error (CE) (mm) 0.010429 with an objective function value of 0.64394. The experimental results found that an appropriate combination of V, IEG, and I can effectively minimize defects while maximizing RMR, thereby improving the overall quality and efficiency of the ECD process.