| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Unsupervised Multi-Modal Machine Learning for In-Process Monitoring of Additive Manufacturing |
| Author(s) |
Anthony Garland, Matthew McKinney, Benjamin White, Brad Boyce, Michael Heiden, Jesse Adamczyk, Dan Bolintineanu |
| On-Site Speaker (Planned) |
Anthony Garland |
| Abstract Scope |
This work introduces a machine learning framework for analyzing multi-modal data in additive manufacturing, specifically laser powder bed fusion (LPBF). It uses the CLIP model to learn joint embeddings from in-process monitoring data (images and audio) and post-build data (optical images, height maps, force-displacement curves) without labeled data. The framework effectively predicts changes in LPBF process parameters. The embeddings are physically meaningful, as verified through clustering and 2D projections. This approach has potential for process monitoring and quality assessment in additive manufacturing and shows an effective approach for using large amounts of unlabeled data. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |