  Chatter detection has become a prominent subject of interest due to its
effect on cutting tool life, surface finish and spindle of machine tool. Most
of the existing methods in chatter detection literature are based on signal
processing and signal decomposition. In this study, we use topological features
of data simulating cutting tool vibrations, combined with four supervised
machine learning algorithms to diagnose chatter in the milling process.
Persistence diagrams, a method of representing topological features, are not
easily used in the context of machine learning, so they must be transformed
into a form that is more amenable. Specifically, we will focus on two different
methods for featurizing persistence diagrams, Carlsson coordinates and template
functions. In this paper, we provide classification results for simulated data
from various cutting configurations, including upmilling and downmilling, in
addition to the same data with some added noise. Our results show that Carlsson
Coordinates and Template Functions yield accuracies as high as 96% and 95%,
respectively. We also provide evidence that these topological methods are noise
robust descriptors for chatter detection.
