Multi-response optimisation of machining aluminium-6061 under eco-friendly electrostatic minimum quantity lubrication environment
dc.contributor.author | Jamil, Muhammad | |
dc.contributor.author | Khan, Aqib Mashood | |
dc.contributor.author | He, Ning | |
dc.contributor.author | Li, Liang | |
dc.contributor.author | Zhao, Wei | |
dc.contributor.author | Sarfraz, Shoaib | |
dc.date.accessioned | 2020-01-17T15:26:15Z | |
dc.date.available | 2020-01-17T15:26:15Z | |
dc.date.issued | 2019-10-09 | |
dc.description.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. | en_UK |
dc.identifier.citation | Jamil 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. 459-479 | en_UK |
dc.identifier.issn | 1748-5711 | |
dc.identifier.uri | https://doi.org/10.1504/IJMMM.2019.103137 | |
dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/14949 | |
dc.language.iso | en | en_UK |
dc.publisher | Inderscience | en_UK |
dc.rights | Attribution-NonCommercial 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
dc.subject | tool life | en_UK |
dc.subject | surface roughness | en_UK |
dc.subject | energy consumption | en_UK |
dc.subject | sustainable manufacturing | en_UK |
dc.subject | RSM-based grey relational analysis | en_UK |
dc.subject | Grey relational analysis | en_UK |
dc.title | Multi-response optimisation of machining aluminium-6061 under eco-friendly electrostatic minimum quantity lubrication environment | en_UK |
dc.type | Article | en_UK |
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