In-situ Porosity Detection in Additive Manufacturing
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| Titel: | In-situ Porosity Detection in Additive Manufacturing |
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| Autoren: | Sievers, Erik, 1993, Papatriantafilou, Marina, 1966, Gulisano, Vincenzo Massimiliano, 1984, Hryha, Eduard, 1980, Nyborg, Lars, 1958, Chen, Zhuoer, 1989 |
| Quelle: | Additive Manufacturing using Metal Pilot Line (MANUELA) Demonstration of Infrastructure for Digitalization enabling industrialization of Additive Manufacturing (DiDAM) Relaxed Semantics Across the Data Analytics Stack (RELAX-DN) VR EPITOME - Sammanfattning och strukturering av kontinuerlig data i pipelines för samtidig behandling 19th ACM International Conference on Distributed and Event-Based Systems, DEBS 2025 , Gothenburg, Sweden Debs 2025 Proceedings of the 19th ACM International Conference on Distributed and Event Based Systems. :211-222 |
| Schlagwörter: | In-situ Monitoring, Stream Processing, Machine learning |
| Beschreibung: | Additive Manufacturing (AM) is a rapidly growing technology with applications in aerospace, automotive, and medical industries. Scalable AM requires in-situ quality monitoring to detect defects promptly. However, in-situ monitoring introduces scalability challenges due to high data volumes, rapid acquisition rates, and strict latency requirements. We introduce Hephaestus, a continuous in-situ monitoring system for data streams from optical monitoring sensors, able to detect porosity risks promptly and to balance accuracy and timeliness by adjusting the window of data used for porosity detection. Using data from two builds, we study this trade-off and the method's cost-benefit towards early cancellation decisions. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/547927 https://research.chalmers.se/publication/547927/file/547927_Fulltext.pdf |
| Datenbank: | SwePub |
Nájsť tento článok vo Web of Science | Abstract: | Additive Manufacturing (AM) is a rapidly growing technology with applications in aerospace, automotive, and medical industries. Scalable AM requires in-situ quality monitoring to detect defects promptly. However, in-situ monitoring introduces scalability challenges due to high data volumes, rapid acquisition rates, and strict latency requirements. We introduce Hephaestus, a continuous in-situ monitoring system for data streams from optical monitoring sensors, able to detect porosity risks promptly and to balance accuracy and timeliness by adjusting the window of data used for porosity detection. Using data from two builds, we study this trade-off and the method's cost-benefit towards early cancellation decisions. |
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| DOI: | 10.1145/3701717.3734463 |