Sustainable machining of Ti-6Al-4V using cryogenic cooling: an optimized approach

Abstract Cryogenic machining is an effective, sustainable cooling approach in machining hard-to-cut materials. In this work, two multi-objective optimization techniques, namely; non-dominated sorting genetic algorithm, and grey relational analysis, were used to optimize the cutting performance during turning Ti-6Al-4V alloys under flood and cryogenic cooling. The machining performance was optimized in terms of surface roughness, material removal rate, tool performance and cutting forces. The optimal solutions, including cutting conditions and cooling technique, were determined for different machining strategies (i.e. roughing, finishing, and productivity). It was found that cryogenic cooling offers better cutting performance with a higher optimization index than flood approach