Version 1
: Received: 23 November 2023 / Approved: 24 November 2023 / Online: 24 November 2023 (09:21:01 CET)
How to cite:
Alberts, M.; Coble, J.; Karandikar, J.; Khojandi, A.; Schmitz, T.; John, A. S. S.; Jared, B. Chatter Detection in Simulated Machining Data: A Vibration-Driven Machine Learning Approach. Preprints2023, 2023111591. https://doi.org/10.20944/preprints202311.1591.v1
Alberts, M.; Coble, J.; Karandikar, J.; Khojandi, A.; Schmitz, T.; John, A. S. S.; Jared, B. Chatter Detection in Simulated Machining Data: A Vibration-Driven Machine Learning Approach. Preprints 2023, 2023111591. https://doi.org/10.20944/preprints202311.1591.v1
Alberts, M.; Coble, J.; Karandikar, J.; Khojandi, A.; Schmitz, T.; John, A. S. S.; Jared, B. Chatter Detection in Simulated Machining Data: A Vibration-Driven Machine Learning Approach. Preprints2023, 2023111591. https://doi.org/10.20944/preprints202311.1591.v1
APA Style
Alberts, M., Coble, J., Karandikar, J., Khojandi, A., Schmitz, T., John, A. S. S., & Jared, B. (2023). Chatter Detection in Simulated Machining Data: A Vibration-Driven Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202311.1591.v1
Chicago/Turabian Style
Alberts, M., And Sam St. John and Bradley Jared. 2023 "Chatter Detection in Simulated Machining Data: A Vibration-Driven Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202311.1591.v1
Abstract
Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.
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.