Investigation and Optimization of Surface Roughness in Hard Milling of 9CrSi Steel Using Al?O? 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 Al?O? 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 Al?O? nanofluid-assisted MQL can effectively improve surface finish in the hard milling of 9CrSi steel.