| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
In-Situ Layer Temperature Monitoring Enabled by Machine Vision |
| Author(s) |
Chris O'Brien, Chad Duty, Kris Villez |
| On-Site Speaker (Planned) |
Chris O'Brien |
| Abstract Scope |
Layer deposition time is crucial for the success of the additively manufactured (AM) parts as it directly affects inter-layer adhesion, structural integrity, and subsequent production efficiency. For large-format AM (LFAM), its optimization is essential due to print size and increased thermal capacity from high volumetric deposition rates. Setting the layer deposition time too short results in material collapse. However, if layer deposition time is too long it leads to debonding. Our goal is to develop a control method that optimizes the layer deposition time in LFAM systems using infrared cameras for real-time temperature measurement. In this study, we prototype our method with a small-scale 3D printer to generate data for a deep learning model that enables tracking of the printer head and estimation of the temperatures at the time of deposition. The work is the first step towards an image-based optimization loop to dynamically control layer time in LFAM processes. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |