On the wavelet analysis of cutting forces for chatter identification in milling

Abstract

Chatter vibrations in machining operations affect surface finishing and tool behaviour, particularly in the end-milling of aluminum parts for the aerospace industry. This paper presents a methodological approach to identify chatter vibrations during manufacturing processes. It relies on wavelet analyses of cutting force signals during milling operations. The cutting-force signal is first decomposed into an approximation/trend sub-signal and detailed sub-signals, and it is then re-composed using modified sub-signals to reduce measurement noise and strengthen the reference peak forces. The reconstruction of the cutting-force signal is performed using a wavelet denoising procedure based on a hard-thresholding method. Four experimental configurations were set with specific cutting parameters using a workpiece specifically designed to allow experiments with varying depths of cut. The experimental results indicate that resultant force peaks (after applying the threshold to the detailed sub-signals) are related to the presence of chatter, based on the increased correlation of such peaks and the surface roughness profiles, thereby reinforcing the applicability of the proposed method. The results can be used to control the online occurrence of chatter in end-milling processes, as the method does not depend on the knowledge of cutting geometry nor dynamic parameters.