Jamil, MuhammadKhan, Aqib MashoodHe, NingLi, LiangZhao, WeiSarfraz, Shoaib2020-01-172020-01-172019-10-09Jamil M, Khan AM, He N, et al., (2019) Multi-response optimisation of machining aluminium-6061 under eco-friendly electrostatic minimum quantity lubrication environment. International Journal of Machining and Machinability of Materials, Volume 21, Issue 5-6, 2019, pp. 459-4791748-5711https://doi.org/10.1504/IJMMM.2019.103137https://dspace.lib.cranfield.ac.uk/handle/1826/14949The emerging grave consequences of conventional coolants on health, ecology and product quality, have pushed the scientific research to explore eco-friendly lubrication technique. Electrostatic minimum quantity lubrication (EMQL) has been underscored as a burgeoning technology to cut-down bete noire impacts in machining. This research confers the adoption of a negatively charged cold mist of air-castor oil employed in turning of aluminium-6061T6 material by varying the cutting conditions, as per experimental designed through response surface methodology (RSM). For comprehensive sagacity, a range of cutting speed, feed, depth of cut and EMQL-flow rate were considered. Material removal rate, tool life, surface roughness and power consumption of machine tool were adopted as performance measures. To satisfy multi-criterion simultaneously, RSM-based grey relational analysis (GRA) was employed for multi-objective optimisation. Highest proportion of grey relational grade (GRG) as a single desideratum response function, provided a trade-off between performance measures with 15.56% improvement in GRG.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/tool lifesurface roughnessenergy consumptionsustainable manufacturingRSM-based grey relational analysisGrey relational analysisArticle