| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Physics-Based Compressive Sensing and Physics-constrained Dictionary Learning to Monitor Laser Powder Bed Fusion Process |
| Author(s) |
Yanglong Lu, Sungjin Hong, Sung-Hoon Ahn, Yan Wang |
| On-Site Speaker (Planned) |
Sungjin Hong |
| Abstract Scope |
The variability of qualities in additively manufactured products requires better process monitoring and control. The existing sensing techniques for laser powder bed fusion (LPBF) are limited by low spatial and temporal resolutions, as well as the accessibility of sensors. A new process monitoring framework that includes novel physics-based compressive sensing (PBCS) and physics-constrained dictionary learning (PCDL) is proposed. Based on multiphysics models, PBCS allows for the reconstruction of high-fidelity thermofluid latent field from low-fidelity temperature measurements with the seamless integration between models and experiments. In addition, PCDL is developed to reconstruct high-resolution 2D images from the low-resolution ones to further improve sensing efficiency. Machine health states can also be identified from low-fidelity sensor data. PBCS and PCDL mechanisms are demonstrated with thermal and optical images in the LPBF process. |
| Proceedings Inclusion? |
Undecided |