  Large-amplitude chatter vibrations are one of the most important phenomena in
machining processes. It is often detrimental in cutting operations causing a
poor surface finish and decreased tool life. Therefore, chatter detection using
machine learning has been an active research area over the last decade. Three
challenges can be identified in applying machine learning for chatter detection
at large in industry: an insufficient understanding of the universality of
chatter features across different processes, the need for automating feature
extraction, and the existence of limited data for each specific
workpiece-machine tool combination. These three challenges can be grouped under
the umbrella of transfer learning. This paper studies automating chatter
detection by evaluating transfer learning of prominent as well as novel chatter
detection methods. We investigate chatter classification accuracy using a
variety of features extracted from turning and milling experiments with
different cutting configurations. The studied methods include Fast Fourier
Transform (FFT), Power Spectral Density (PSD), the Auto-correlation Function
(ACF), Wavelet Packet Transform (WPT), and Ensemble Empirical Mode
Decomposition (EEMD). We also examine more recent approaches based on
Topological Data Analysis (TDA) and similarity measures of time series based on
Discrete Time Warping (DTW). We evaluate the transfer learning potential of
each approach by training and testing both within and across the turning and
milling data sets. Our results show that carefully chosen time-frequency
features can lead to high classification accuracies albeit at the cost of
requiring manual pre-processing and the tagging of an expert user. On the other
hand, we found that the TDA and DTW approaches can provide accuracies and F1
scores on par with the time-frequency methods without the need for manual
preprocessing.
