Multiobjective optimization of sustainable turning process using TOPSIS method

Abstract

The minimum-quantity-lubrication (MQL) cooling machining technique is an environmental damage-free manufacturing process. It is very complicated to determine the optimum cutting conditions for multiple-response problems. In the present work, the technique for order preference by similarity to ideal solution (TOPSIS) coupled with the Taguchi method was applied for the selection of optimum cutting conditions in the turning of 17-4 precipitated hardenable stainless steel under MQL. In this experimental investigation, the turning process parameters considered were cutting velocity (v), feed rate (f) and depth of cut (d), while the output responses were surface roughness (R a) and tool flank wear (V b). From the optimization study, it was found that v = 54 m/min, f = 0·096 mm/revolution and d = 0·2 mm were the optimum cutting conditions. From the conformation test results, it was observed that the optimum cutting conditions determined by the Taguchi-coupled TOPSIS method reduced R a and V b by 19·2 and 69%, respectively. The analysis of variance results showed that the turning performance was largely influenced by the feed rate. Linear regression analysis was also carried out to predict the responses as a function of input process variables.