Surface Roughness Prediction and Cutting Parameters Optimization in High-Speed Milling AlMn1Cu Using Regression and Genetic Algorithm

Surface roughness is an important indicator of the surface quality of machined workpieces. In this study, in order to find the functional relation between cutting parameters and surface roughness, a series of cutting experiments for AlMn1Cu are conducted to obtain surface roughness values in high-speed peripheral milling. Firstly, this paper presents the predictive mathematic model of surface roughness based on the cutting parameters. Secondly, the optimization model of cutting parameters in order to achieve the maximum material removal rate is built in this paper, and the genetic algorithm is employed to find the optimum cutting parameters leading to maximum material removal rate in the different range of surface roughnesspsilas values.