Near dry turning of EN8 and EN31 steel: multi-objective optimization using grey relational analysis

Abstract

Steel is the most commonly employed material in various engineering applications, and their successful machining demands finding the optimized set of machining parameters along with appropriate cooling strategies. Moreover, the significance of process parameter optimization is progressively perceived in the wake of expensive CNC machine adaptation on the shop floor for machining. Further, a competent cooling strategy is essential with a minimal amount of coolant to obtain the best quality products. In the present work, the optimization of process parameters for Near Dry Turning (NDT) of two steel grades, EN8 and EN31, was done. NDT utilizes a minimal coolant with a major amount of compressed air. For competent cooling, Al2O3 nanofluid as coolant was used with compressed air. Speed, feed, and depth of cut were taken as the machining parameters for the turning process. Two response variables, the surface roughness of machined specimen and cutting zone temperature, were considered for the analysis. Three levels of each turning parameter were chosen, and the Taguchi L9 orthogonal array was adopted for the experimentation. The optimized turning parameter was found through the Grey Relational Analysis (GRA). Further, the applicability of compressed air was also presented to achieve sustainable and green machining to eliminate the negative impact on environmental footprints. For this purpose, results at the obtained optimized set of parameters were compared with plain base fluid and compressed dry air as coolants. The reduction in surface roughness of ∼12.3% and ∼14.6% for EN8 and EN31 steel were observed using nanofluid in near dry turning. Similarly, the reduction in cutting zone temperature was ∼7% in both cases. These results show the significance of process parameter optimization and the applicability of nanofluid in near dry turning of steels.