Multi-response optimization of machining performance and energy efficiency in sustainable Nanofluid-MQL face milling of hardened AISI 4140 steel

Abstract

Hard milling has emerged as an alternative to conventional grinding for finishing hardened steels because of its productivity and cost advantages. However, achieving high surface quality and stable machining performance under sustainable lubrication conditions remains challenging. In this study, the machining performance of hardened AISI 4140 steel was experimentally evaluated during face milling under three lubrication conditions: dry cutting, minimum quantity lubrication (MQL), and multi-walled carbon nanotube (MWCNT)-reinforced nanofluid-assisted MQL using biodegradable peanut oil as the base fluid. The effects of cutting speed ( V ) and feed rate ( f ) on surface roughness (Ra), vibration, sound intensity, cutting temperature, power consumption, and energy consumption were analyzed. Chip morphology and tool wear mechanisms were also examined. The results showed that increasing V and f deteriorated surface quality and increased vibration, temperature, and power demand. Among the tested strategies, Nanofluid-MQL provided the best overall performance. The lowest Ra value (0.090 µm) was obtained with Nanofluid-MQL, whereas the highest value (0.423 µm) was obtained under dry cutting. Compared with dry machining, Nanofluid-MQL reduced cutting temperature and power consumption by up to 12.62 % and 7.03 %, respectively. SWARA–CoCoSo optimization identified 90 m min ⁻¹ and 0.05 mm tooth ⁻¹ under Nanofluid-MQL as the optimum condition.