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.
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.