Title: Application of MQL for developing sustainable EDM and process parameter optimisation using ANN and GRA method

Authors: Viswanth V. Srinivas; R. Ramanujam; G. Rajyalakshmi

Addresses: School of Mechanical Engineering, VIT University, Vellore-63201, India ' School of Mechanical Engineering, VIT University, Vellore-63201, India ' School of Mechanical Engineering, VIT University, Vellore-63201, India

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.

Keywords: minimum quantity lubrication; MQL; sustainability; electrical discharge machining; grey relational analysis; GRA; multi-response optimisation.

DOI: 10.1504/IJBEX.2020.111476

International Journal of Business Excellence, 2020 Vol.22 No.4, pp.431 - 450

Received: 02 Feb 2018
Accepted: 22 May 2018
Published online: 30 Nov 2020 *

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