Milling Parameter Optimization Based on Control of Aluminum Alloy 7475 Surface Roughness

摘要

Aiming at the problems that diversity and blindness of milling parameter selection, propose a milling parameter optimization method that based on control of surface roughness by coupling neural network and annealing genetic hybrid algorithm. This method makes advantages of experimental design technique, neural network and annealing genetic hybrid algorithm. High speed milling of aluminum alloy 7475 surface roughness experiments were designed based on uniform experimental design scheme. A neural network predictive model for surface roughness was created using experimental data. The predictive model and analytical definition of material remove rate had consisted of optimization problem. It was used annealing genetic hybrid algorithm to optimize model. Proved by test, the results show that the optimal parameter can improve surface quality. This indicates that the method can accurately milling parameter optimize.