| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Aluminum Alloys: Development and Manufacturing Supplier Forum
|
| Presentation Title |
A physical twin of a multi-laser powder bed fusion system for correlative photodiode sensing, high-speed optical and synchrotron X-ray imaging |
| Author(s) |
Chu Lun Alex Leung, Samy Hocine, Sebastian Marussi, Wei Li, Da Guo, Rubén Lambert-Garcia, Elena Ruckh, Marta Majkut, Alexander Rack, Andy Farndell, Nick Jones, Peter D Lee |
| On-Site Speaker (Planned) |
Chu Lun Alex Leung |
| Abstract Scope |
Laser powder bed fusion (LPBF) has transformed the design paradigm for building engineering components with unparallelled complexity using a focused laser beam and powder feedstock. To improve the productivity and quality of the LPBF components, there is a growing interest in the development of a multi-laser powder bed fusion system (mLPBF); however, our understanding of the beam-matter and multi-phase interactions during processing remains poorly understood. Here, we showcase the design and application of a physical twin that replicates a commercial mLPBF system coupled with correlative photodiode sensing, and optical and ultra-fast synchrotron X-ray imaging capabilities. Our results reveal melt pool dynamics, spatter behaviour, and pore evolution mechanisms during LPBF and mLPBF. Additionally, we have developed and performed an efficient and robust deep-learning model, AM-Segnet, for image segmentation and feature quantification to provide insights into the process dynamics. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Additive Manufacturing, Machine Learning, Characterization |