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Doyle, Aoife C., Egan, Darragh S., Ryan, Caitríona M., Parnell, Andrew and Dowling, Denis P. (2021) Application of the STRAY statistical learning algorithm for the evaluation of in-situ process monitoring data during L-PBF additive manufacturing. Procedia Manufacturing, 54. pp. 250-256. ISSN 2351-9789
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Abstract
This study investigates the use of a statistical anomaly detection method to analyse in-situ process monitoring data obtained during the Laser Powder Bed Fusion of Ti-6Al-4V parts. The printing study was carried out on a Renishaw 500M Laser-Powder Bed Fusion system. A photo diodebased system called InfiniAM was used to monitor the melt-pool emissions along with the operational behaviour of the laser during the build process. The analysis of the in-process data was carried out using an unsupervised machine learning approach called the Search and TRace AnomalY algorithm. The ability to detect defects during the manufacturing of metal alloy parts was demonstrated.
| Item Type: | Article |
|---|---|
| Additional Information: | Cite as:Aoife C. Doyle, Darragh S. Egan, Caitríona M. Ryan, Andrew C. Parnell, Denis P. Dowling, Application of the STRAY statistical learning algorithm for the evaluation of in-situ process monitoring data during L-PBF additive manufacturing., Procedia Manufacturing, Volume 54, 2021, Pages 250-256, ISSN 2351-9789, https://doi.org/10.1016/j.promfg.2021.07.039. |
| Keywords: | Defect detection; In-situ process monitoring; Powder bed fusion; Statistical anomaly detection; High dimensional data; Additive manufacturing; |
| Academic Unit: | Faculty of Science and Engineering > Mathematics and Statistics Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS |
| Item ID: | 17969 |
| Identification Number: | 10.1016/j.promfg.2021.07.039 |
| Depositing User: | Andrew Parnell |
| Date Deposited: | 21 Dec 2023 11:11 |
| Journal or Publication Title: | Procedia Manufacturing |
| Publisher: | Elsevier BV |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/17969 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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