Superalloy MAR-247 is mainly applied in the space industry and die industry. With its characteristics of mechanical property, fatigue resistance, and high temperature corrosion resistance, therefore, it is mainly applied in machine parts of high temperature and corrosion resistance, such as turbine blades and rotor of the aeroengine and turbine assembly in the nuclear power plant. However, considering that its properties of high strength, low thermal conductivity, being difficult to soften, and work hardening may reduce the life of cutting-tool and weaken the surface accuracy, the study provided minimizing experiment occurring during milling process for superalloy material. As a statistical approach used to analyse experiment data, this study used GM in the grey prediction model to conduct simulation and then predict and analyze its characteristics based on the experimental data, focusing on the tool life and surface accuracy. Moreover, with the superalloy machining parameters of the current effective application improved grey prediction model, it can decrease the errors, extend the tool life, and improve the prediction precision of surface accuracy.
