Application of Jaya Algorithm for Minimizing Surface Finish in MQL-Based Green Machining of Titanium Alloy

Abstract

Abstract“Sustainable Manufacturing” is a recent concept to obtain efficiency in the manufacturing process in terms of economy and environmental friendliness with social aspects. Green manufacturing (GM) is a sub-set of sustainable manufacturing and aims to obtain environmentally friendly machining. Dry machining, minimum quantity lubrication (MQL) machining, and nanofluid machining are different approaches employed for minimizing the use of cutting fluid during machining, aiming toward the achievement of the green machining concept. Also, the use of cutting fluids causes several health hazards for human operators during the machining and handling processes. Machining of hard aero-space materials like titanium and nickel-based alloys with better surface finish requires a flood coolant system. This work aims to minimize the surface finish and cutting temperature and obtain optimum machining parameters during nanofluid-based MQL machining of titanium alloy Ti6Al4V. Turning experiments, conducted by Zaman et al. (Adv Mater Process Technol 1–21, 2020), are used for optimizing the process. A Box Behnken design of experiments with four factors at three levels is used, with 27 experimental runs. Model for Ra and T is developed with response surface methodology (RSM) as a function of four process variables (i.e., v, f, d, and C). The process is optimized using the Jaya algorithm, a popular parameter-free method. The results are compared with the conventional desirability function (DF) approach and found to be closer; the source codes developed were run in MATLAB and obtained optimum parameters with a fewer iterations.KeywordsGreen manufacturingNanofluid-based MQLTurningTi6Al4VJaya algorithm