Abstract
This paper addresses the experimental-based optimisation of near dry EDM process on duplex stainless steel 2,205 grade material under minimum quantity lubrication (MQL) for improving the sustainability. Taguchi's L9 orthogonal array experimental design has been executed for varying input parameters like pulse-on time, pulse-off time, current and voltage. The machining performance is analysed by measuring the material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR). The obtained results are analysed by the artificial neural network (ANN) and grey relational analysis (GRA) for the multi-response optimisation. In multi-response optimisation, the optimum combination of parameters derived using GRA lead to the improvement of material removal rate at 6.1287 mm3/min and reduced electrode wear rate 0.0698 mm3/min at optimal parameters levels (TON = 450 μs, TOFF = 50 μs, current = 16 A, and voltage = 5 V). From the results, optimisation of MQL-based near dry EDM method proved some benefits in terms of improved sustainability.
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