Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method

Abstract

Sustainability in any production emphasizes green-manufacturing techniques, improvement in quality with energy-efficient techniques, and environment-friendly processes. Titanium machining productivity is greatly influenced by speed, as high cutting velocity raises the temperatures in the shear zone and heat, owing to its low thermal conductivity. Hence in this work, an attempt is made to increase productivity by exploring the efficacy at transition speed for titanium alloy machining. Water-soluble lubricant is mist-sprayed as aerosols at a near-zero temperature in minor quantity, to minimize the temperatures generated during the cutting process at increased speed. Besides, an optimal decision variable vector optimizes multi-goals of machining Titanium grade 5 alloys under Minimum quantity cooling lubrication explored in this study in transitional speed zones. The response goals are the optimization of “vibration, surface quality, tool wear rate, and Material removal rate.” Multi goal optimization achieved by hybrid Taguchi coupled with Data Envelopment Analysis based Ranking (DEAR). The tool wear is very rapid at velocities of 200 mm/min. DEAR technique uses computed Multi performance rank index (MPRI) to predict the best data set at: (velocity, feed, doc) at (120 mm/min, 0.2 mm/rev, 1.0 mm). In this setting, the responses are compared in dry, flood, and MQL environment. It is observed a 30%, 60%, 40% improvement in surface finish, tool life, and vibrations compared to a dry environment and 13% and 3% of roughness and tool wear rate compared to a flood environment. Thus MQCL can be adopted for Ti6Al4V at transitional speeds.