About this Abstract |
| Meeting |
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
|
| Symposium
|
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
|
| Presentation Title |
An On-Demand Resin Characterization and Process Planning Framework for Reactive Extrusion Additive Manufacturing |
| Author(s) |
Daphne Lin, Mohammad Hossein Zamani, Zoubeida Ounaies, Carolyn Seepersad |
| On-Site Speaker (Planned) |
Daphne Lin |
| Abstract Scope |
Advances in additive manufacturing, such as the introduction of Reactive Extrusion Additive Manufacturing (REAM), have enabled functional grading of materials which allows for complex, multi-modal designs that respond to external stimuli. The challenge that arises when printing quality functionally graded parts is maintaining a printable material while transitioning between design-prescribed materials. Traditionally, each printed material would be experimentally characterized and tuned for a given AM process; however, this becomes infeasible when the number of potential materials becomes too large as is the case in functional grading. This work presents an on-demand resin characterization and process planning framework for REAM which leverages transfer learning and machine learning techniques to 1) characterize an entire resin formulation space through fewer characterization experiments and 2) plan resin formulation changes to minimize rheological differences and print instability. The framework was validated through experimental functionally graded prints which demonstrate increased part quality compared to baseline prints. |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |