Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

Abstract

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100 m/min, a cutting depth of 0.2 mm, and a feed rate of 0.01 mm/tooth. With these settings, the predicted surface roughness could reach 0.0451 µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100 m/min, a cutting depth of 0.36 mm, and a feed rate of 0.0168 mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069 µm, and the predicted MRR could reach 775.06 mm3/min