Machining of difficult-to-machine materials produces huge amount of slurry and harmful aerosol concentration in the atmosphere, which are investigated in this work. In this study, an integrated framework was designed for the multi-response parametric optimization of powder-mixed electrical discharge machining of tungsten carbide alloy keeping into account both manufacturing and environmental aspects. Experiments were conducted using Taguchi L27 orthogonal array (OA), and process optimization was achieved using grey, grey-fuzzy, and grey-adaptive neuro fuzzy inference system–integrated approach. The multi-objective optimization techniques provide optimal parameter settings, i.e. medium pulse-on time (50 µs), low dielectric level (40 mm), medium current intensity (6 A), and high flushing pressure (0.6 kg/cm2). Results conclude that medium input discharge energies (pulse-on time and current intensity) are optimal for machining difficult-to-machine materials to produce low aerosol concentration. The comparison between the grades was performed to show the effectiveness of optimization approaches relative to each other. Finally, confirmation experiments were also conducted to indicate the effectiveness of the adopted optimization approach.
Fabrication of Tungsten Carbide Alloy Micro-Pin with Environment-Responsive ECM using Ultralow-Concentration Electrolyte
