Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method

Abstract

Blisk is a key component of new aero-engines. To improve machining efficiency, disc milling is used for roughing blisk tunnels. The multi-objective optimization is employed to optimize disc milling process. In this study, an integration-based approach that used grey relational analysis (GRA) coupled with radial basis function (RBF) neural network and particle swarm optimization (PSO) algorithm is applied to solve the optimization problem. To achieve smaller cutting force and greater material removal rate (MRR), the appropriate cutting speed, feed rate per tooth, and cutting height needed to be determined. A hybrid experiment scheme of three factors–five levels is carried out to generate data sample. Results of verified experiments indicate that GRA–RBF–PSO approach can improve performance better than original GRA.