Machining effects and multi-objective optimization in Inconel 718 turning with unitary and hybrid nanofluids under MQL

Abstract

Designing tooling and cooling systems to prevent cutting tool damage is crucial while machining difficult-to-cut nickel alloys. This study investigates the machining effects during turning Inconel 718 using unitary aluminum oxide (Al2O3) and hybrid aluminum oxide+multi-walled carbon nanotube type (Al2O3+MWCNT) nanofluids under minimum quantity lubrication (NFMQL) through mathematical modeling and multi-objective optimization. The worn-out tools were analyzed for damage and wear mechanisms through images captured using optical and scanning electron microscopes. The study indicates that hybrid nanofluids outperform unitary nanofluids, which could be attributed to the better lubricating and cooling capabilities of MWCNT and the higher surface tension and thermal conductivity of Al2O3 nanoparticles. The cutting parameters were optimized by combining the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) and genetic algorithm. The study reveals an average error of less than 10% between experimental and predicted responses from the proposed optimization model. This study found lower cutting force up to 80 N, surface roughness of 0.6–0.7 µm, and tool life over 10 minutes with a cutting speed of 50–70 m/min and a lower feed and depth of cut of 0.1 mm/rev and 0.2 mm, respectively, using a hybrid Al2O3+MWCNT nanofluid under NFMQL conditions.