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Anomaly detection in three-axis CNC machines using LSTM networks and transfer learning

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Abstract

There is a growing interest in developing automated manufacturing technologies to achieve a fully autonomous factory. An integral part of these smart machines is a mechanism to automatically detect operational and process anomalies before they cause serious damage. The long-short-term memory (LSTM) network has shown considerable promise in the literature, with applications in the detection of tool wear and tool breakage to name a few. However, these methods require a significant amount of machine-specific training data to be successful, which makes these networks custom to a machine, requiring new networks and new data for each machine. Transfer learning is an approach where we use a network developed with a rich data set on one machine and re-train it with a smaller data set on a target machine. We have implemented this approach for chatter detection with a LSTM network, using sensor data and a rich data set from one machine, and then use a transfer learning methodology, similar sensors, and a smaller data set for the chatter detection algorithm on another machine. This allows for the transfer of knowledge from one machine to be applied to a similar machine, with some local optimization from transfer learning.

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Acknowledgements

We would like to thank the co-op students, Eric Jessee, Yang Li, and Sarah Ahmed, who helped with the fabrication and data collection for this project. Your assistance was greatly appreciated.

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Authors and Affiliations

  1. Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue, Waterloo, N2L 3G1, Ontario, Canada

    Eugene Li, Sanjeev Bedi & William Melek

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  1. Eugene Li
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  2. Sanjeev Bedi
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  3. William Melek
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Contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Eric Jessee, Sarah Ahmed, Yang Li, and Eugene Li. The data analysis and the first draft of the manuscript were written by Eugene Li, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Eugene Li.

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Sanjeev Bedi and William Melek contributed equally to this work.

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Li, E., Bedi, S. & Melek, W. Anomaly detection in three-axis CNC machines using LSTM networks and transfer learning. Int J Adv Manuf Technol 127, 5185–5198 (2023). https://doi.org/10.1007/s00170-023-11617-5

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