In-situ Porosity Detection in Additive Manufacturing
Paper in 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
Author
Erik Sievers
Chalmers, Computer Science and Engineering (Chalmers), Computer and Network Systems
Marina Papatriantafilou
Chalmers, Computer Science and Engineering (Chalmers), Computer and Network Systems
Vincenzo Massimiliano Gulisano
Chalmers, Computer Science and Engineering (Chalmers), Computer and Network Systems
Eduard Hryha
Chalmers, Industrial and Materials Science, Materials and manufacture
Lars Nyborg
Chalmers, Industrial and Materials Science, Materials and manufacture
Zhuoer Chen
Chalmers, Industrial and Materials Science, Materials and manufacture
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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Subject Categories (SSIF 2025)
Computer Sciences
Computer Engineering
Computer Systems
Other Computer and Information Science
DOI
10.1145/3701717.3734463