In-situ Porosity Detection in Additive Manufacturing
Paper i proceeding, 2025
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.
In-situ Monitoring; Stream Processing; Machine learning
Författare
Erik Sievers
Chalmers, Data- och informationsteknik, Dator- och nätverkssystem
Marina Papatriantafilou
Chalmers, Data- och informationsteknik, Dator- och nätverkssystem
Vincenzo Massimiliano Gulisano
Chalmers, Data- och informationsteknik, Dator- och nätverkssystem
Eduard Hryha
Chalmers, Industri- och materialvetenskap, Material och tillverkning
Lars Nyborg
Chalmers, Industri- och materialvetenskap, Material och tillverkning
Zhuoer Chen
Chalmers, Industri- och materialvetenskap, Material och tillverkning
Debs 2025 Proceedings of the 19th ACM International Conference on Distributed and Event Based Systems
211-222
9798400713323 (ISBN)
19th ACM International Conference on Distributed and Event-Based Systems, DEBS 2025
Gothenburg, Sweden,
Gothenburg, Sweden,
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Ämneskategorier (SSIF 2025)
Datavetenskap (datalogi)
Datorteknik
Datorsystem
Annan data- och informationsvetenskap
DOI
10.1145/3701717.3734463