TY - JOUR
T1 - A data-driven framework for predicting machining stability
T2 - employing simulated data, operational modal analysis, and enhanced transfer learning
AU - Coble, Jamie
AU - Alberts, Matthew
AU - St. John, Sam
AU - Odie, Simon
AU - Khojandi, Anahita
AU - Jared, Bradley
AU - Schmitz, Tony
AU - Karandikar, Jaydeep
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
PY - 2024
Y1 - 2024
N2 - Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. This research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.
AB - Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. This research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.
KW - Chatter
KW - Machining dynamics
KW - Operational modal analysis
KW - Predictive maintenance
KW - Receptance coupling
KW - Stability
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85211505140&partnerID=8YFLogxK
U2 - 10.1007/s00170-024-14841-9
DO - 10.1007/s00170-024-14841-9
M3 - Article
AN - SCOPUS:85211505140
SN - 0268-3768
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
ER -