In this work, machining of microchannel in silica glass was successfully carried out using electro chemical discharge machining (ECDM) process. The experiments were planned according to L27 orthogonal array with applied voltage, stand-off distance (SOD), electrolyte concentration, pulse frequency and pulse-on-time (TON) as control factors. The material removal rate (MRR), overcut (OC) and tool wear rate (TWR) were considered as response characteristics. In this study the effects of control parameters on MRR, OC and TWR have been investigated. The multi-objective optimization of ECDM was carried out through grey relational analysis (GRA) method. Optimal combination of process parameters achieved from GRA was 45V applied voltage, 25wt.% electrolyte concentration, 1.5mm SOD, 400Hz pulse frequency and 45μs TON. ANOVA for GRG study revealed that the applied voltage (70.33%) was most significant factor affecting output responses followed by electrolyte concentration (11.69%), pulse frequency (4.98%) and SOD (4.13%). Furthermore, the regression equations were formulated for the optimum combination to predict the collaboration and higher-order effects of the control parameters. In addition confirmation test was conducted for the optimal setting of process parameters and the comparison of experimental results exhibited a good agreement with predicted values. The microstructural observation of machined surface for the optimum combination was carried out.

The production of miniature parts by the electrochemical discharge micromachining process ([Formula: see text]-ECDM) draws the most of attractions into the industrial field. Parametric influences on machining depth (MD), material removal rate (MRR), and overcut (OC) have been propounded using a mixed electrolyte (NaOH:KOH- 1:1) varying concentrations (wt.%), applied voltage ([Formula: see text]), pulse on time ([Formula: see text]s), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO[Formula: see text]). Analysis of variances has been analyzed to test the adequacy of the developed mathematical model and multiresponse optimization has been performed to find out maximum MD with higher material removal at lower OC using desirability function analysis as well as neural network (NN)-based Particle Swarm Optimization (PSO). The SEM analysis has been done to find unexpected debris. MD has been improved with better surface quality using a mixed electrolyte at straight polarity using a tungsten carbide (WC) cylindrical tool along with [Formula: see text], [Formula: see text], and [Formula: see text] axis movement by computer-aided subsystem and combining with the automated spring feed mechanism. PSO-ANN provides better parametric optimization results for micromachining by the ECDM process.