About this Abstract |
| Meeting |
2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
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| Symposium
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2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026)
|
| Presentation Title |
Toward a Physics-Resolving Digital Twin for Metal AM: Graph Neural Network Surrogates for Thermomechanical Field Prediction and Sensor-Driven Defect Detection |
| Author(s) |
Usman Tariq, Sung-Heng Wu, Frank Liou |
| On-Site Speaker (Planned) |
Usman Tariq |
| Abstract Scope |
Reliable qualification of metal additive manufacturing parts requires accurate prediction of thermal history, residual stress, and distortion. Physics-based finite element simulation captures this accurately but takes hours to days per build, making it incompatible with real-time control. This work develops a physics-aware two-stage graph neural network surrogate that converts finite element meshes into graphs with physics-informed features including laser proximity, boundary distance, and element birth timing. A DeeperGCN thermal model predicts full-field temperature across time steps and generalizes to unseen geometries without retraining. A recurrent graph neural network maps the predicted thermal history to full-field residual stress and displacement, preserving causal thermomechanical structure. These models enable rapid pre-build risk assessment orders of magnitude faster than finite element analysis. Looking ahead, the surrogate is designed to assimilate in-situ sensor data layer by layer, enabling subsurface inference, anomaly detection, and closed-loop process control as a physics-resolving digital twin for metal additive manufacturing. |
| Proceedings Inclusion? |
Planned: Post-meeting proceedings |