| 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 |
A Framework for Physics-guided Machine Learning to Extract and Transfer Process-structure-property Knowledge in Additive Manufacturing |
| Author(s) |
Hyunwoong Ko, Fatemeh Elhambakhsh |
| On-Site Speaker (Planned) |
Hyunwoong Ko |
| Abstract Scope |
Emerging research in Additive Manufacturing (AM) seeks to pursue Machine Learning (ML) that can improve the understanding of Process-structure-property (PSP) causality. To address the challenge, we provide a novel framework for physics-guided ML to extract and transfer PSP knowledge. The framework first uses an approach guided by physics knowledge graphs to generate the requirements for predictive PSP analytics. Then, the framework uses physics-informed ML to construct new PSP knowledge. The study enables ML to systematically couple physics knowledge with the versatility of cyber-physical AM data in PSP analytics. This study also provides a foundational basis for AM to synergically merge newfound knowledge about PSP from data with a priori physics knowledge. The framework continuously updates coupled PSP linkages to improve the understanding of dynamic AM processes. The continuous PSP learning accumulates structured newfound PSP knowledge in iterations for future ML and proactive control decisions. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |