Abstract
Inconel 718 is a heat-resistant Ni-based superalloy widely used, particularly, in aircraft and aero-engineering applications. It has poor machinability due to its unique thermal and mechanical properties. For this reason, studies have been carried out from past to present to improve the machinability of Nickel-based (Ni) alloys. Further improvement can be achieved by applying hybrid multi-objective optimization strategies to ensure that cutting parameters and cooling/lubrication strategies are also adjusted effectively. That is why, in this research, the machinability of Inconel 718 is optimized under various sustainable lubricating environments i.e., dry medium, minimum quantity lubrication (MQL), nano-MQL, and cryogenic conditions at different machining parameters during end-milling process. Subsequently, the analysis of variance (ANOVA) approach was implanted to apprehend the impact of each machining parameter. Finally, to optimize machining environments, two advanced optimization algorithms (non-dominated sorting genetic algorithm II (NSGA-II) and the Teaching-learning-based optimization (TLBO) approach) were introduced. As a result, both methods have demonstrated remarkable efficiency in machine response prediction. Both methodologies demonstrate that a cutting speed of 90 m/min, feed rate of 0.05 mm/rev, and CO2 snow are the optimal circumstances for minimizing machining responses during milling of Inconel 718.