Multi response optimization of process variables in MQL assisted face milling of EN31 alloy steel using grey relational analysis

Abstract

Temperature has immense effect on the surface conditions of the specimen in machining process. The traditional method for lowering the temperature used in industries is flood cooling which leads to lots of environmental and health concerns. In the present work, the effect of minimum quantity lubrication has been analyzed for the outputs like cutting temperature and surface roughness in machining of EN31 alloy steel. The technique used for this multi-response optimization is Taguchi based grey relational analysis. This technique is used to conclude the best possible levels of process variables (cutting depth, feed, cutting speed and lubricant flow rate). After validation of results, the optimal conditions for process parameters achieved are speed at 700 rpm, feed at 140 mm/min, depth of cut as 0.3 mm and lubricant flow rate as 150 ml/hr. Lubricant flow rate proves its significance for the aforementioned parameters.