About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Computational Materials for Qualification and Certification
|
| Presentation Title |
Metal Additive Manufacturing Simulations Driven by In-Situ Experimental Data for Qualification and Certification |
| Author(s) |
Theron M. Rodgers, Eric Clough, Yuksel Yabansu, Michael Sangid, Jacob Hochhalter, Narendran Raghavan, Daniel Moser, Aashique Rezwan, Nicole Aragon, Brooke Beck, Stephen Lin, Michael Stender |
| On-Site Speaker (Planned) |
Theron M. Rodgers |
| Abstract Scope |
Simulations of grain microstructure formation in metal additive manufacturing (AM) are essential for understanding process–structure relationships. However, current methods are calibrated to nominal process parameters and cannot capture differences between builds or parts fabricated with the same settings. We present a workflow that simulates microstructure evolution using a Monte Carlo solidification model coupled to a thermal model informed by in-situ data. This approach predicts build-specific lack-of-fusion defects, grain-structure variations at scan-strategy boundaries, and other heterogeneities. Resulting defect and microstructure twins enable part-specific fatigue life predictions. We also describe efforts to integrate subgrain heterogeneity into Inconel 625 simulations by linking solidification-induced misorientation formation, kernel average misorientation (KAM), and geometrically necessary dislocation (GND) density. These capabilities support process optimization, defect mitigation, and computation-driven qualification of AM parts.
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