  Chatter detection from sensor signals has been an active field of research.
While some success has been reported using several featurization tools and
machine learning algorithms, existing methods have several drawbacks such as
manual preprocessing and requiring a large data set. In this paper, we present
an alternative approach for chatter detection based on K-Nearest Neighbor (kNN)
algorithm for classification and the Dynamic Time Warping (DTW) as a time
series similarity measure. The used time series are the acceleration signals
acquired from the tool holder in a series of turning experiments. Our results,
show that this approach achieves detection accuracies that in most cases
outperform existing methods. We compare our results to the traditional methods
based on Wavelet Packet Transform (WPT) and the Ensemble Empirical Mode
Decomposition (EEMD), as well as to the more recent Topological Data Analysis
(TDA) based approach. We show that in three out of four cutting configurations
our DTW-based approach attains the highest average classification rate reaching
in one case as high as 99% accuracy. Our approach does not require feature
extraction, is capable of reusing a classifier across different cutting
configurations, and it uses reasonably sized training sets. Although the
resulting high accuracy in our approach is associated with high computational
cost, this is specific to the DTW implementation that we used. Specifically, we
highlight available, very fast DTW implementations that can even be implemented
on small consumer electronics. Therefore, further code optimization and the
significantly reduced computational effort during the implementation phase make
our approach a viable option for in-process chatter detection.
