Investigation and Optimization of Surface Roughness in Hard Milling of 9CrSi Steel Using Al₂O₃ Nanofluid-Assisted MQL

This study investigates and optimizes surface roughness in the hard milling of 9CrSi steel (50–55 HRC) under minimum quantity lubrication (MQL) using Al₂O₃ nanofluid with coconut oil as the base fluid. The experiments were designed based on the Taguchi L9 orthogonal array to analyze the effects of cutting speed (60–100 m/min), depth of cut (0.2–0.6 mm), and feed per tooth (0.020–0.030 mm/tooth) on surface roughness (Ra). The results show that Ra ranged from 0.25 to 0.40 µm under the investigated conditions. Analysis of signal-to-noise ratios and ANOVA revealed that the depth of cut had the most significant influence on surface roughness, followed by feed per tooth, while cutting speed had a smaller effect. The optimal cutting parameters for minimizing surface roughness were determined as a cutting speed of 100 m/min, a depth of cut of 0.2 mm, and a feed per tooth of 0.020 mm/tooth. A regression model with a high coefficient of determination (R² = 96.88%) was developed to predict surface roughness. The results confirm that Al₂O₃ nanofluid-assisted MQL can effectively improve surface finish in the hard milling of 9CrSi steel.