Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25

In manufacturing industry, the effect of cutting fluids has been known on the health, environment and productivity in machining operations such as turning, milling, drilling, etc. Minimum Quantity Lubrication (MQL) is an effective tool to minimize the damage of cutting fluids on health and environment in cutting processes. Thus, optimal process parameters must be determined under MQL cooling/lubrication condition to determine the maximum productivity. This paper presents an approach for optimization of machining parameters with multi-response outputs using design of experiment in turning. For experimental design, tests were planned based on Taguchi's L9 orthogonal array. During the turning of cobalt base super alloy Haynes 25 which is a difficult-to-cut material, process performance indicators such as flank wear, notch wear and surface roughness were measured. The process parameters which are cutting fluid (CFs), fluid flow rate (Q) and cutting speed (Vc) were simultaneously optimized by taking the multi-response outputs by Taguchi based grey relational analysis (GRA) into consideration. Taguchi's signal to noise ratio was applied with larger-the-better approach to obtain the best combination. Three mathematical models were created using response surface regression methodology. According to the multi-response optimization results, which were obtained from the largest signal to noise ratio of the grey relational grade (GRG), the optimum combination was vegetable base cutting fluid, 180 mL/h fluid flow rate and 30 m/min cutting speed to simultaneously minimize the tool wear patterns and surface roughness. In addition, it was found out that the percentage improvement in GRG with the multiple responses is 39.4%. It was clearly shown that the performance indicators are significantly improved using this approach.