Empirical Model for Surface Roughness in Hard Milling of AISI H13 Steel Under Nanofluid-MQL Condition Based on Analysis of Cutting Parameters

In this study, the effect of machining parameters on surface roughness in hard milling of AISI H13 steel with a carbide-coated (TiAlN) cutting tool under nanofluid MQL condition was analyzed based on the integration of Taguchi method and response surface methodology (RSM). SiO2 nanoparticles were selected to add into cutting oil CT232. The experiment was carried out using the L27 orthogonal array of DOE method developed by G. Taguchi. The cutting parameters including cutting-velocity, feed-rate, depth-of-cut and hardness-of-workpiece at three levels were investigated to find out their influence on the surface roughness. An empirical model was presented to predict surface roughness under nanofluid MQL condition. Additionally, a multi-objective optimization was performed to obtain the minimum value of surface roughness and the maximum value of the material removal rate.