| 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 |
Defect-free Ceramic Hybrid-AM using Intelligent Layer Reworking |
| Author(s) |
Louis Masters, Dan Davie, Matthew Peter Shuttleworth, Tyler Green, Mehmet Dogar , Robert Kay |
| On-Site Speaker (Planned) |
Louis Masters |
| Abstract Scope |
Hybrid additive manufacturing of advanced ceramics facilitates the production of highly dense and precise parts by combining additive and subtractive processes. However, extrusion-based processes are susceptible to stochastic defects such as voids, which can degrade material properties, leading to premature failure and lower yield. This research demonstrates deep learning informed selective layer reworking for a ceramic hybrid additive manufacturing platform. Each layer was evaluated in-situ using a vision-based monitoring system, consisting of a camera and laser profilometer. Through closed-loop control, a decision was made autonomously to remove defective layers via subtractive operations, prior to reprinting. The deep learning model detected voids with a precision of 90%, and parts have been demonstrated to be free of extrusion-related voids after corrective action. This unlocks new opportunities for regulated industries hoping to exploit quality-assured ceramic components that benefit from freeform fabrication. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |