Version 1
: Received: 31 July 2024 / Approved: 31 July 2024 / Online: 2 August 2024 (11:53:15 CEST)
How to cite:
---, M. P.; Mendes-Moreira, J. An Unsupervised Chatter Detection Method Based on AE and DBSCAN Clustering Utilizing Internal CNC Machine Signals. Preprints2024, 2024072595. https://doi.org/10.20944/preprints202407.2595.v1
---, M. P.; Mendes-Moreira, J. An Unsupervised Chatter Detection Method Based on AE and DBSCAN Clustering Utilizing Internal CNC Machine Signals. Preprints 2024, 2024072595. https://doi.org/10.20944/preprints202407.2595.v1
---, M. P.; Mendes-Moreira, J. An Unsupervised Chatter Detection Method Based on AE and DBSCAN Clustering Utilizing Internal CNC Machine Signals. Preprints2024, 2024072595. https://doi.org/10.20944/preprints202407.2595.v1
APA Style
---, M. P., & Mendes-Moreira, J. (2024). An Unsupervised Chatter Detection Method Based on AE and DBSCAN Clustering Utilizing Internal CNC Machine Signals. Preprints. https://doi.org/10.20944/preprints202407.2595.v1
Chicago/Turabian Style
---, M. P. and Joao Mendes-Moreira. 2024 "An Unsupervised Chatter Detection Method Based on AE and DBSCAN Clustering Utilizing Internal CNC Machine Signals" Preprints. https://doi.org/10.20944/preprints202407.2595.v1
Abstract
In manufacturing chatter is an unwanted phenomenon that can lead to product quality reduction and tool wear. Real time chatter detection is key to preventing these issues and improving overall machining efficiency. In this paper we propose an unsupervised chatter detection method using autoencoders (AE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm that uses internal signals of Computer Numerical Control (CNC) machines. The proposed method starts by using an AE to extract features from raw internal signals collected from CNC machines. This step reduces the dimensionality of the data and captures the underlying patterns of chatter. Then the extracted features are fed into DBSCAN clustering algorithm which is a density based algorithm that groups similar data points and identifies outliers. We tested the proposed method with real world data collected from various CNC machines. The results show that our unsupervised chatter detection method has high accuracy, precision and recall, can detect chatter and distinguish it from normal machining. Also the method is robust to noise and can adapt to dynamic machining conditions. In summary our work presents an unsupervised chatter detection method using AE and DBSCAN clustering that uses internal signals of CNC machines. This method is a reliable and efficient solution for real time chatter detection so manufacturers can improve product quality, optimize machining process and reduce tool wear during machining.
Keywords
machine learning; autoencoder; clustering; chatter detection; turning process; signal processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
