Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling

Abstract

This work presents the highly nonlinear relationships between processing conditions and the specific cutting energy, arithmetical mean roughness, and means roughness depth with the aid of the Kriging models in the dry milling of SKD61 material. Four processing conditions include the depth of cut, spindle speed, feed rate, and nose radius. The aim of this paper is to optimize machining factors for decreasing specific cutting energy and improving the material removal rate while the roughness properties are predefined as constraints. An evolutionary algorithm entitled archive-based micro-genetic algorithm (AMGA) is applied to generate the optimal inputs. The results show that a set of feasible optimal solutions can be determined to observe a low specific cutting energy coupled with a smooth surface and high material removal rate. Furthermore, the hybrid approach comprising the Kriging model and AMGA can be considered as an intelligent approach for optimization of the milling processes.