About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Towards a Computational Digital Twin of Metals AM |
| Author(s) |
Anthony D. Rollett, Somnath Ghosh, Mahadevan Sankaran |
| On-Site Speaker (Planned) |
Anthony D. Rollett |
| Abstract Scope |
We present an update on computational digital twin being built by the Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM) under NASA support. Many of the component models for microstructure development in 3D printing and micro-mechanical response leading to prediction of fatigue are being both developed and calibrated. Crucially, with upwards of a dozen major components, the multi-institution team is devoting substantial effort not merely to data curation but also to data transfer which involves significant learning and effort on both sides of the exchange. Uncertainty quantification is being applied to both process and micromechanical models. The need to predict microstructure across a wide range of process parameter values motivates the use of reduced order models calibrated by physics-based models at the grain scale, e.g., Potts, which are themselves abstractions of models at the dendrite level, e.g., cellular automata. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Computational Materials Science & Engineering, Modeling and Simulation |