| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
PRISM: Process Parameter Optimization for Selective Manufacturing |
| Author(s) |
Anthony Garland, Dale Cillessen, Kaitlynn Conway, Johnson Kyle, Brad Boyce, Jay Carroll |
| On-Site Speaker (Planned) |
Anthony Garland |
| Abstract Scope |
The process of selecting optimal process parameters for additive manufacturing (AM) of a new material can be a challenging task, particularly for materials with limited information in the literature. This study describes an iterative approach that uses machine learning algorithms to predict printability and the target material property when given a set of process parameters. These ML algorithms when combined with optimal design of experiments, enable the selection of optimal process parameters for additive manufacturing (AM) of new materials. The results demonstrate the effectiveness of our approach. This study provides a framework for future research in the selection of optimal process parameters for manufacturing processes and highlights the potential of machine learning in optimizing materials design and manufacturing processes. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |