Abstract
In several manufacturing and automotive industries, cutting fluids significantly affect productivity during the machining of the intrinsic components. The inherent nature and characteristics of cutting fluids also impact directly or indirectly the operational environments and health of humans when used for machining processes such as drilling, milling, and turning. A useful tool for reducing the damage caused by cutting fluids to the immediate environment and human health during cutting procedures is minimum quantity lubrication (MQL). To determine the best performance, the ideal process parameters must be established under MQL cooling/lubrication conditions. This study is focused on the performance evaluation of three cutting lubricants, which are neem, palm kernel and mineral oil in the turning process of AISI 1045 steel using a tungsten carbide tool with MQL. In addition to this, optimization of machining parameters with multi-response signals using Taguchi-based grey relational analysis was done. For experimental design, tests were developed using Taguchi's L9 orthogonal array and the signal-to-noise ratio was obtained using the smaller-the-better approach to achieve the optimal combination. Performance indicators including surface, roughness and cutting temperature were measured throughout the machining process. Neem oil performed best among the oils for surface roughness while mineral oil outperformed them all for cutting temperature, according to the experimental results. The optimum spindle speed, feed rate and depth of cut for neem, palm kernel and mineral oils are 870 rev/min, 0.25 mm/rev and 1.25 mm; 870 rev/min, 0.10 mm/rev and 0.75 mm; and 415 rev/min, 0.10 mm/rev and 1.00 mm, respectively.