Quantum Particle Swarm Optimization of ECDM Performances for Circuit Input Parameters During Micro-Machining of Silicon-Wafer

ECDM process accuracy mainly depends on the circuit configuration of the power system with correlating other machining variables. Semi-conducting job specimen like Silicon-wafer has effective utility in micro-fluidic labs. Here, mainly circuit input variables like duty factor (0.40–0.50), pulse-on-time (20–30 [Formula: see text]s), voltage (40–55 V), pulse frequency (200–800 Hz) and pulse off time (35–45 [Formula: see text]s) effectiveness at fixed concentration 25 wt.% of (NaOH [Formula: see text]KOH), inter-electrode gap (IEG) 40 mm and stand-off distance (SOD) 0.5 mm on machining rate as material removal rate (MRR) & (gm/h) & width of cut (WOC) ([Formula: see text]m) of silicon-wafer has been illustrated using WC (200 [Formula: see text]m) tool. The paper also analyzed the fitness of models with ANOVA for those machining outputs and optimized multi-performances for maximization of MRR and minimization of WOC. The paper also shows the convergence test and analysis of root mean square error (RMSE) and computing time for choosing the best evolutionary algorithms using response surface methodology (RSM), particle swarm optimization (PSO) and Quantum-PSO. Thermal effects and channel irregularities if any have been advocated by SEM analysis of Silicon-wafer. Maximized MR with Minimized WOC is achieved at 45 V, 25.65 [Formula: see text]s T on and T off 35 [Formula: see text]s, with frequency 800 Hz, [Formula: see text]40 mm IEG and 0.50 duty factor and 25 wt.% of NaOH & KOH and best cognitive models of Quantum particle swarm optimization with neural network (QPSO-NN) is advocated and validated for this experimentation as RMSE for MRR and WOC of satisfied value 1.484% and 1.511%.