Multi-response optimisation of machining aluminium-6061 under eco-friendly electrostatic minimum quantity lubrication environment. (9th October 2019)

• Record Type: Journal Article
• Authors: Jamil, Muhammad; Khan, Aqib Mashood; He, Ning; Li, Liang; Zhao, Wei; Sarfraz, Shoaib
• Is Part Of: International journal of machining and machinability of materials. Volume 21:Number 5/6(2019)
• Page Start: 459
• Page End: 479
• Publication Date: 2019-10-09
• Subjects: tool life -- surface roughness -- energy consumption -- sustainable manufacturing -- RSM-based grey relational analysis -- grey relational analysis -- GRA; Machining -- Periodicals; Metals -- Machinability -- Periodicals; 671.35
• Languages: English
• ISSNs: 1748-5711

Abstract

The 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.