Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world ap...

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Bibliographic Details
Published in:Machines (Basel) 2024-12, Vol.12 (12), p.923
Main Authors: Alberts, Matthew, St. John, Sam, Odie, Simon, Khojandi, Anahita, Jared, Bradley, Schmitz, Tony, Karandikar, Jaydeep, Coble, Jamie B.
Format: Article
Language:English
Subjects:
Accuracy
Adaptation
Chatter
Coupling
Cutting tools
Datasets
Efficiency
Geometry
High speed machining
Machine learning
Machining
Management science
Manufacturing
Modal analysis
Noise prediction
predictive analytics
Productivity
real world
Sensors
Signal processing
Simulation
stability
Surface properties
Technology application
Tool life
Vibration
vibration analysis
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Summary:Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines12120923