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Found 8 papers, 1 papers with code
On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition
The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting.
Topological Feature Vectors for Chatter Detection in Turning Processes
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.
Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping
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.
Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector
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.
On Transfer Learning of Traditional Frequency and Time Domain Features in Turning
In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment.
Data-driven and Automatic Surface Texture Analysis Using Persistent Homology
Therefore, fast and automatic determination of the roughness level is essential to avoid costs resulting from surfaces with unacceptable finish, and user-intensive analysis.
Transfer Learning for Autonomous Chatter Detection in Machining
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.
Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform
Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95\%.
