Application of MQL for developing sustainable EDM and process parameter optimisation using ANN and GRA method
by Viswanth V. Srinivas; R. Ramanujam; G. Rajyalakshmi
International Journal of Business Excellence (IJBEX), Vol. 22, No. 4, 2020

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 (T_ON = 450 μs, T_OFF = 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.

Online publication date: Mon, 30-Nov-2020

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