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.
