For this task, we turn to the field of topological data analysis (TDA), which encodes information about the shape and structure of data.
Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95\%.
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.
Therefore, fast and automatic determination of the roughness level is essential to avoid costs resulting from surfaces with unacceptable finish, and user-intensive analysis.
In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment.
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.
As the field of Topological Data Analysis continues to show success in theory and in applications, there has been increasing interest in using tools from this field with methods for machine learning.
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.
We present the results for several choices of the topological feature vectors, and we compare our results to the WPT and EEMD methods using experimental turning data.
The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting.
Specifically, we show how persistent homology, a tool from TDA, can be used to yield a compressed, multi-scale representation of the graph that can distinguish between dynamic states such as periodic and chaotic behavior.
Chaotic Dynamics Computational Geometry Information Theory Information Theory Data Analysis, Statistics and Probability
Specifically, we begin by characterizing relative compactness with respect to the bottleneck distance, and then provide explicit theoretical methods for constructing compact-open dense subsets of continuous functions on persistence diagrams.
Bi-variate density estimates of the randomly projected time-series in the $p$-$q$ plane described in Gottwald and Melbourne's approach for 0/1 detection are used to generate a gray-scale image.
The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods.