About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Towards a Computational Digital Twin of Metals AM |
| Author(s) |
Anthony D. Rollett |
| On-Site Speaker (Planned) |
Anthony D. Rollett |
| Abstract Scope |
The Institute for Model-Based Qualification & Certification of Additive Manufacturing (IMQCAM) is building a computational digital twin (DT) for predicting fatigue in metals additive manufacturing (m-AM). 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. With several major components, the multi-institution/disciplinary team is devoting substantial effort not merely to data curation but also to data & model transfer which involves substantial discussion. Uncertainty quantification (UQ) is being applied to both process and micromechanical models. Simulation of microstructure across a wide range of process parameter values is presented as an example that includes a new approach to grain shape; the new high throughput algorithm enables UQ. A multi-stage probabilistic model for fatigue life is outlined. NASA support is gratefully acknowledged. |