| 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 |
Iterative Learning for Efficient Additive Mass Production |
| Author(s) |
Christos Margadji, Douglas Brion, Sebastian Pattinson |
| On-Site Speaker (Planned) |
Christos Margadji |
| Abstract Scope |
Material extrusion could enable on-demand production of complex and personalised parts but is limited by low reliability, particularly in higher-volume production. Machine learning-based methods may enhance reliability, but are often themselves insufficiently reliable for use in production. Foundation artificial intelligence models have enabled significant improvements in performance across many tasks. Here, a vision-based control system is reported, coupling active learning and uncertainty awareness with a foundation model to continually learn to build a specific part better. The resulting framework is called Iterative Learning, as it improves performance by learning from its own errors during repeated build cycles of the same part. The iterative learning approach is shown to enable robust error detection and correction while being more space, time and computationally efficient compared to a naïve fine-tuning approach. This provides a path showing how foundation models may be adapted to enhance reliability across a wider range of additive manufacturing processes. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |