no code implementations • 12 Apr 2022 • Melih C. Yesilli, Jisheng Chen, Firas A. Khasawneh, Yang Guo
Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95\%.
no code implementations • 11 Apr 2022 • Melih C. Yesilli, Firas A. Khasawneh, Brian Mann
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.
no code implementations • 19 Oct 2021 • Melih C. Yesilli, Firas A. Khasawneh
Therefore, fast and automatic determination of the roughness level is essential to avoid costs resulting from surfaces with unacceptable finish, and user-intensive analysis.
no code implementations • 28 Aug 2020 • Melih C. Yesilli, Firas A. Khasawneh
In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment.
no code implementations • 27 Oct 2019 • Melih C. Yesilli, Sarah Tymochko, Firas A. Khasawneh, Elizabeth Munch
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.
no code implementations • 5 Aug 2019 • Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
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.
no code implementations • 21 May 2019 • Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
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.
1 code implementation • 3 May 2019 • Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting.