Chatter identification of the milling process considering dynamics of the thin-walled workpiece

Chatter happens due to the low rigidity of the machining system, and chatter identification is essential for improving the surface quality and productivity. More understanding on the dynamic characteristics of the machining system would help identify chatter more efficiently. This paper proposes a method for detecting chatter in the milling process of the thin-walled workpiece by monitoring and analyzing the cutting torque. A varying chatter detection threshold based on the workpiece geometries, tool path, and dynamic characteristics is utilized to identify chatter. A high-frequency filter and a median filter are utilized for signal denoising. The method is experimentally validated, which proved that it could predict chatter efficiently at its early infancy. The method could be a supplementary to other chatter identification methods.