Parametric Study of RSM Modelling and Multiresponse Optimization of Milling Electrochemical Spark Micromachining (M-ECSMM) for Microchannel Fabrication on Silicon Wafers

Silicon wafers find applications in manufacturing large-scale micro-components widely available in consumer-based electronic devices. The milling-electrochemical spark micromachining (Milling-ECSMM) process is popular nowadays as an advanced micromanufacturing process for creating microchannels and several other geometric features. This paper presents a parametric effect of voltage, tool rotation, electrolyte concentration, and duty cycle on MRR, R_a, and TWR, using a tungsten carbide (WC) two-fluted flat-end milling cutter. The experimental model was developed using response surface methodology (RSM), and parametric multi-objective optimization was carried out using desirability function analysis (DFA) and genetic algorithm (GA). SEM and optical images were taken to identify the surface quality of the machined microchannel. The optimal combination of parameters obtained from GA as 90 V, 13 rpm, 64 g/l, and 40% DC gives better convergence and multi-objective response for MRR, TWR, and R_a as 0.409 mg/min, 0.0428 mg/min, and 0.572 µm, respectively.