Gabriel Bernard, Sofiane Achiche
, Sébastien Girard and J. R. René Mayer
Article (2021)
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Open Access to the full text of this document Published Version Terms of Use: Creative Commons Attribution Download (13MB) |
Abstract
Manufacturing processes can be monitored for anomalies and failures just like machines, in condition monitoring and prognostic and health management. This research takes inspiration from condition monitoring and prognostic and health management techniques to develop a method for part production process monitoring. The contribution brought by this paper is an automated technique for process monitoring that works with low sampling rates of 1/3 Hz, a limitation that comes from using data provided by an industrial partner and acquired from industrial manufacturing processes. The technique uses kernel density estimation functions on machine tools spindle load historical time signals for distribution estimation. It then uses this estimation to monitor the manufacturing processes for anomalies in real time. A modified version was tested by our industrial partner on a titanium part manufacturing line.
Uncontrolled Keywords
| Subjects: |
1700 Design and manufacturing > 1700 Design and manufacturing 2100 Mechanical engineering > 2100 Mechanical engineering |
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| Department: | Department of Mechanical Engineering |
| Funders: | Canadian Network for Research and Innovation in Machining Technology (CANRIMT2) - CRSNG/NSERC |
| Grant number: | NETGP 479639-15 |
| PolyPublie URL: | https://publications.polymtl.ca/9401/ |
| Journal Title: | Journal of Manufacturing and Materials Processing (vol. 5, no. 1) |
| Publisher: | MDPI |
| DOI: | 10.3390/jmmp5010026 |
| Official URL: | https://doi.org/10.3390/jmmp5010026 |
| Date Deposited: | 09 Feb 2023 16:26 |
| Last Modified: | 27 Sep 2024 21:21 |
| Cite in APA 7: | Bernard, G., Achiche, S., Girard, S., & Mayer, J. R. R. (2021). Condition monitoring of manufacturing processes under low sampling rate. Journal of Manufacturing and Materials Processing, 5(1), 26 (14 pages). https://doi.org/10.3390/jmmp5010026 |
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