Performance assessment of some selected vegetable oils as lubricants in turning of AISI 1045 steel using a Taguchi-based grey relational analysis approach

Abstract

This study is focused on the performance evaluation of three cutting lubricants, which are neem, palm kernel and mineral oil in the turning of AISI 1045 with MQL. 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.