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

Viswanth V. Srinivas, R. Ramanujam and G. Rajyalakshmi

International Journal of Business Excellence, 2020, vol. 22, issue 4, 431-450

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. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=111476 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbexc:v:22:y:2020:i:4:p:431-450

Access Statistics for this article

More articles in International Journal of Business Excellence from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().