  There has been an increasing interest in leveraging machine learning tools
for chatter prediction and diagnosis in discrete manufacturing processes. Some
of the most common features for studying chatter include traditional signal
processing tools such as Fast Fourier Transform (FFT), Power Spectral Density
(PSD), and the Auto-correlation Function (ACF). In this study, we use these
tools in a supervised learning setting to identify chatter in accelerometer
signals obtained from a turning experiment. The experiment is performed using
four different tool overhang lengths with varying cutting speed and the depth
of cut. We then examine the resulting signals and tag them as either chatter or
chatter-free. The tagged signals are then used to train a classifier. The
classification methods include the most common algorithms: Support Vector
Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Gradient Boost
(GB). Our results show that features extracted from the Fourier spectrum are
the most informative when training a classifier and testing on data from the
same cutting configuration yielding accuracy as high as %96. However, the
accuracy drops significantly when training and testing on two different
configurations with different structural eigenfrequencies. Thus, we conclude
that while these traditional features can be highly tuned to a certain process,
their transfer learning ability is limited. We also compare our results against
two other methods with rising popularity in the literature: Wavelet Packet
Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). The latter
two methods, especially EEMD, show better transfer learning capabilities for
our dataset.
