Performance evaluation of vegetable oil based nano cutting fluids in machining using grey relational analysis-A step towards sustainable manufacturing
Economic analysis, Grey relational analysis, Machining, MQL, Vegetable oil based nanocutting fluids
Journal of Cleaner Production
With the rising disadvantages of conventional cutting fluids, search for environmental-friendly cutting fluids is underway. The present work deals with the application of vegetable oil based nano cutting fluids during turning of AISI 1040 steel, in view of environmental conscious machining. Cutting fluids formulated by dispersing nano suspensions of molybdenum di sulphide in coconut oil, sesame oil and canola oil are applied to machining zone at varying cutting conditions, as suggested by Taguchi's L27 orthogonal array. For statistical analyses, base fluid, nano particle inclusions, cutting speed and feed rate are considered as four factors at three levels each. Cutting force, cutting temperatures, tool flank wear and surface roughness are considered as four responses that represent machining performance indices. Multi objective optimization is done by implementing Taguchi based Grey Relational Analysis. It is used to assess the optimum machining conditions. In the present work coconut oil +0.5% nano molybdenum sulphide when applied at a cutting speed of 40 m/min, feed rate of 0.14mm/rev and 0.5% nano particle inclusions resulted in improved machining performance. The order of influence of input parameters on machining performance is observed to be, type of base fluid, level of nanoparticle inclusion, cutting speed and feed. Cost analysis for the application of nanocutting fluids is done in the present work to assess the viability of these fluids in industry.
Original Publication Date
DOI of published version
UNI ScholarWorks, Rod Library, University of Northern Iowa
Rapeti, Padmini; Pasam, Vamsi Krishna; Rao Gurram, Krishna Mohana; and Revuru, Rukmini Srikant, "Performance evaluation of vegetable oil based nano cutting fluids in machining using grey relational analysis-A step towards sustainable manufacturing" (2016). Faculty Publications. 1042.