About this Abstract |
| Meeting |
TMS Specialty Congress 2026
|
| Symposium
|
World Congress on Reproducibility, Qualification, and Standards Development of Additive Manufacturing and Beyond (RQSD 2026)
|
| Presentation Title |
A Digital Twin of Metals Additive Manufacturing That Accelerates Qualification & Certification |
| Author(s) |
Anthony D. Rollett, Somnath Ghosh |
| On-Site Speaker (Planned) |
Anthony D. Rollett |
| Abstract Scope |
Our NASA-supported multi-institution Institute for Model-based Q&C of Additive Manufacturing is developing a computational Digital Twin (DT) of metals additive manufacturing (MAM), from feedstock to location-specific fatigue performance. All component models of the DT incorporate uncertainty quantification (UQ) which has already revealed unanticipated compositional sensitivities. The project focuses on two exemplary materials, Ti-6Al-4V (Ti64) and nickel 718. A prior ULI project on MAM established a close connection between the process conditions, defect structure and fatigue performance for LPBF Ti64. We have demonstrated an advanced crystal plasticity model in a finite element framework for modeling short crack growth in MAM materials. We use a model-based material definition (MBMD) for the DT that incorporates integrated, interdisciplinary modeling workflows. We address the current challenges around data curation & exchange, communication between models, UQ, validation, and probabilistic approaches to prediction of fatigue and explain how the DT will accelerate Q&C. |
| Proceedings Inclusion? |
Undecided |