Identification of end-milling chatter based on comprehensive feature fusion

The main barrier impeding the advancement of high-speed milling is chatter, which has a detrimental effect on the dimensional accuracy and quality of the finished workpiece. A reliable and precise chatter identification method is essential to improving the quality of machining. This paper presents a novel method for chatter identification using a comprehensive feature fusion of the Short-Time Fourier Transform (STFT) and the Fourier Synchrosqueezing Transform (FSST). The Wavelet Packet Transform (WPT) was used to pre-process the collected vibration and force signals. Wavelet packets with rich chatter information were then selected and reconstructed for further analysis. To reduce the effects of the rotating frequency and generate a hybrid spectrum with high resolution, a Gabor time-frequency filter is employed. As chatter indicators, standard deviation, skewness, and root mean square are computed. With a higher Time-Frequency Representation (TFR) resolution and a shorter computation time of 0.46 and 0.97 s across vibration and force signals, the proposed method outperforms conventional STFT and FSST. As a result, it can be used to reliably identify chatter from the onset, which is beneficial for machining monitoring.