MRR optimization in Nano-MQL milling of hardened 90CrSi tool steel using CUO nanoparticles = Tối ưu hóa tốc độ bóc tách vật liệu trong quá trình phay thép 90crsi tôi cứng sử dụng bôi trơn Nano-MQL với hạt CUO

Abstract

This study aims to optimize the material removal rate (MRR) in the hard milling of 90CrSi tool steel under nano-minimum quantity lubrication (Nano-MQL) conditions. A Box-Behnken Design (BBD) was employed to investigate the effects of cutting speed, feed per tooth, depth of cut, and CuO nanoparticle size on MRR. A total of 45 experiments were conducted using a nano-composite coated carbide tool (Ø12 mm, 6 flutes) on hardened 90CrSi steel with a hardness of 56-60 HRC. The nano-lubricant was prepared by dispersing 2 wt.% CuO nanoparticles into canola oil and delivered to the cutting zone at a flow rate of 100 mL/h with a compressed air pressure of 4 atm. A second-order regression model was developed to analyze the influence of each factor and their interactions on MRR. Main effects plots and surface response analysis were also performed to visualize trends and identify optimal conditions. The results showed that cutting speed and depth of cut were the most significant contributors to MRR, while CuO nanoparticle size had a secondary but positive effect. The regression model demonstrated high predictive accuracy, providing a valuable tool for parameter selection in sustainable machining of hard materials.