Multi-Criteria Optimization of Cutting Parameters for Energy-Efficient Turning of AISI 4340 Steel under Dry, Minimum Quantity Lubrication, and Nanofluid-Assisted Minimum Quantity Lubrication Conditions

Achieving energy efficiency and high surface quality in the turning of hard-to-machine materials such as AISI 4340 steel requires effective decision-making frameworks. While single-objective optimization techniques are widely reported in the literature, comparative studies evaluating the consistency and reliability of multi-criteria decision-making (MCDM) methods remain limited. This study considers different machining conditions, including cooling strategies, cutting speed, and feed rate, and evaluates multiple performance indicators, namely tool wear, surface roughness, sound pressure level, and current consumption, using evaluation based on distance from average solution (EDAS), complex proportional assessment (COPRAS), grey relational analysis (GRA), weighted aggregated sum product assessment (WASPAS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods. Objective criterion weights were determined using the criteria importance through intercriteria correlation (CRITIC) method. All five MCDM methods consistently identified Experiment 8 (nanofluid-assisted minimum quantity lubrication [nano-MQL], 150 m/min cutting speed, and 0.10 mm/rev feed rate) as the optimal parameter combination. This condition achieved the lowest surface roughness (0.81 μm), minimum tool wear (0.150 mm), lowest sound pressure level (88.1 dB), and minimum current consumption (4.18 A) among all experimental trials. The consistency of the ranking results confirms the robustness and reliability of the proposed decision-making framework for energy-efficient machining applications.