Multi-response optimization in face milling of EN-31 steel using analytical hierarchy process-based GRA

In recent times, manufacturing sectors are endeavouring to achieve optimal product quality while minimizing costs. For the accomplishment of this objective, they are focusing on the elimination of the surface finishing and heat treatment processes up to an extent and, opting for concurrent optimization of multiple output responses related to product qualities and machining cost. In machining output optimizations, especially in concurrent optimizations, assigning an equal weightage to all responses leads to substandard results. Keeping in mind, this research work is carried out to eliminate the inadequacy of such optimizations by coupling the multi-response optimization technique Grey Relational Analysis (GRA) with the Analytical Hierarchy Process (AHP) for assigning the different weight fractions to the selected output responses which are tool flank wear, surface roughness and surface microhardness. Face milling experiments on EN-31 steel are conducted engaging hBN nanoparticles-based minimum quantity lubrication system. Employing subjective method-based multi-response optimization, the selected output responses are successfully optimized as per their relative priority and the best parametric conditions are obtained as LFR = 150 ml/h, SD = 40 mm, and 0.5 wt% of hBN nanoparticles in lubricant. Also, it has been noted that the presence of hBN nanoparticles provides effective cooling and lubrication at the cutting zone.