| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Machine-Learning-Driven Digital Twin Construction for Additive Manufacturing: A Review |
| Author(s) |
Fatemeh Elhambakhsh, Hyunwoong Ko |
| On-Site Speaker (Planned) |
Fatemeh Elhambakhsh |
| Abstract Scope |
Machine Learning (ML) on high-value data, both from cyber and physical systems, has significant potential for constructing a novel Digital Twin (DT) of Additive Manufacturing (AM). However, the use of ML has been largely hindered in the AM DT construction due to the limited understanding of the potential. To address the limitation, in this study, we thoroughly identify ML’s capability and newfound opportunities driven by ML in the DT construction for AM. This study reviews AM DTs’ key features and emerging applications, and state-of-the-art ML methods for the DT construction. This study also discusses open issues and outlooks on the future directions of the ML-driven DT construction for AM. This study helps maximize ML on cyber-physical AM data in automatically constructing spatiotemporally scalable DTs that improve the understanding of physical phenomena and control decisions in AM. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |