Multi-responses Optimization of Dry Milling of SKD61 for Low Machining Power and Surface Roughness

Abstract

Optimized process parameters play a significant role in improving the energy efficiency and machined part quality. This paper systematically investigates the nonlinear relationships between machining parameters and responses, including machining power Pc and surface roughness Ra of the dry milling (DM) using the response surface model (RSM). Three process parameters considered include the spindle speed S, depth of cut ap, and feed rate fz. A set of physical experiments was carried out with SKD61 steel on a CNC milling machine using the wiper insert. The target of the current complex optimization is to find the low machining power and surface roughness. Finally, an evolutionary algorithm entitled non-dominated sorting genetic algorithm II (NSGA-II) was used to generate a set of feasible optimal solutions and determine the best machining conditions. The results show that an appropriate trade-off solution can be drawn with regard to the low cutting power and surface roughness. Furthermore, the integration of RSM model and NSGA-II can be considered as a powerful approach for modeling and optimizing dry milling processes.