About this Abstract |
| Meeting |
TMS Specialty Congress 2026
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| Symposium
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World Congress on Reproducibility, Qualification, and Standards Development of Additive Manufacturing and Beyond (RQSD 2026)
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| Presentation Title |
Developing a Digital Twin for Laser-Based Additive Manufacturing
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| Author(s) |
Daniel Wheeler, Dilip Banerjee, Jon Guyer, Shengyen Li, Andrew Reid |
| On-Site Speaker (Planned) |
Daniel Wheeler |
| Abstract Scope |
We present recent work on the development of a digital twin (DT) for laser-based additive manufacturing (AM). The proposed approach uses high-fidelity (e.g. ANSYS Mechanical) and surrogate models to capture transient thermal behavior and materials microstructure in IN718. The system state is periodically realigned to experimentally observed data (from AM Bench data of IN718) using statistical data assimilation (DA) techniques. Estimating process parameters and system configurations as well as associated uncertainties are intrinsic by-products of the statistical DA techniques employed. Due to inherit limitations and performance gaps in physics-based surrogate models of complex systems (such as AM) real time feedback from experimentally observed data is vital for ensuring that a DT is closely aligned with its physical twin. The project leverages a number of open-source software tools, including Adamantine, ExaCA, AdditiveFOAM, TorchDA , NeuralOperator (surrogate models), Snakemake (workflow orchestration) and Nix (reproducible environments). |
| Proceedings Inclusion? |
Undecided |