A new method for on-line chatter detection is presented. The proposed method characterizes the significant transition from high dimensional to low dimensional dynamics in the cutting process at the onset of chatter. Based on the observation that cutting signals contain fractal patterns, a wavelet-based maximum likelihood (ML) estimation algorithm is applied to on-line chatter detection. The presented chatter detection index γ is independent of the cutting conditions and gives excellent detection accuracy and permissible computational efficiency, which makes it suitable for on-line implementation. The validity of the proposed method is demonstrated through the tests with extensive actual data obtained from turning and milling processes.
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