  The increasing availability of sensor data at machine tools makes automatic
chatter detection algorithms a trending topic in metal cutting. Two prominent
and advanced methods for feature extraction via signal decomposition are
Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition
(EEMD). We apply these two methods to time series acquired from an acceleration
sensor at the tool holder of a lathe. Different turning experiments with
varying dynamic behavior of the machine tool structure were performed. We
compare the performance of these two methods with Support Vector Machine (SVM),
Logistic Regression, Random Forest Classification and Gradient Boosting
combined with Recursive Feature Elimination (RFE). We also show that the common
WPT-based approach of choosing wavelet packets with the highest energy ratios
as representative features for chatter does not always result in packets that
enclose the chatter frequency, thus reducing the classification accuracy.
Further, we test the transfer learning capability of each of these methods by
training the classifier on one of the cutting configurations and then testing
it on the other cases. It is found that when training and testing on data from
the same cutting configuration both methods yield high accuracies reaching in
one of the cases as high as 94% and 95%, respectively, for WPT and EEMD.
However, our experimental results show that EEMD can outperform WPT in transfer
learning applications with accuracy of up to 95%.
