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| A Reproducible Digital Twin Framework for Additive Manufacturing | |
| </text> | |
| <text x="400" y="80" font-family="Arial, sans-serif" font-size="14" fill="white" text-anchor="middle"> | |
| Integrating physics-based models, surrogate models, data assimilation and AI workflow orchestration [cite: 54, 56] | |
| </text> | |
| <text x="400" y="105" font-family="Arial, sans-serif" font-size="11" fill="#e6f0f9" text-anchor="middle" font-style="italic"> | |
| Daniel Wheeler, Dilip Banerjee, Shengyen Li, Jon Guyer and Andrew Reid (NIST MML/EL) [cite: 57, 58] | |
| </text> | |
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| <text x="400" y="485" font-family="Arial, sans-serif" font-size="16" fill="#004a99" text-anchor="middle" font-weight="bold">AM-DT Hub</text> | |
| <text x="400" y="505" font-family="Arial, sans-serif" font-size="10" fill="#333" text-anchor="middle">Closed-loop autonomous system [cite: 119]</text> | |
| <text x="400" y="520" font-family="Arial, sans-serif" font-size="10" fill="#333" text-anchor="middle">Calibrates via real-time data [cite: 119]</text> | |
| <text x="400" y="540" font-family="Arial, sans-serif" font-size="9" fill="#004a99" text-anchor="middle">github.com/usnistgov/am-dt-modeling [cite: 784]</text> | |
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| <text x="50" y="185" font-family="Arial, sans-serif" font-size="14" fill="#004a99" font-weight="bold">Materials & Physics Models</text> | |
| <text x="55" y="210" font-family="Arial, sans-serif" font-size="10" fill="#333">• High-fidelity ANSYS/Fluent ground truth [cite: 202, 207]</text> | |
| <text x="55" y="225" font-family="Arial, sans-serif" font-size="10" fill="#333">• Open-source scaling: AdditiveFOAM & Adamantine [cite: 206, 237]</text> | |
| <text x="55" y="240" font-family="Arial, sans-serif" font-size="10" fill="#333">• Microstructure: ExaCA grain morphology [cite: 298, 299]</text> | |
| <text x="55" y="255" font-family="Arial, sans-serif" font-size="10" fill="#333">• Solves Process-Structure-Property relationships [cite: 186]</text> | |
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| <text x="450" y="185" font-family="Arial, sans-serif" font-size="14" fill="#004a99" font-weight="bold">AI & Surrogate Acceleration</text> | |
| <text x="455" y="210" font-family="Arial, sans-serif" font-size="10" fill="#333">• Fourier Neural Operators (FNO) for PDE learning [cite: 321, 323]</text> | |
| <text x="455" y="225" font-family="Arial, sans-serif" font-size="10" fill="#333">• Mesh-independent "coarse-to-fine" upsampling [cite: 324, 325]</text> | |
| <text x="455" y="240" font-family="Arial, sans-serif" font-size="10" fill="#333">• AI Orchestration via Model Context Protocol (MCP) [cite: 388, 731]</text> | |
| <text x="455" y="255" font-family="Arial, sans-serif" font-size="10" fill="#333">• Surrogate correction via DA nudging [cite: 329]</text> | |
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| <text x="50" y="685" font-family="Arial, sans-serif" font-size="14" fill="#004a99" font-weight="bold">Data Assimilation & UQ</text> | |
| <text x="55" y="710" font-family="Arial, sans-serif" font-size="10" fill="#333">• Ensemble Kalman Filter (EnKF) for state correction [cite: 434, 473]</text> | |
| <text x="55" y="725" font-family="Arial, sans-serif" font-size="10" fill="#333">• Real-time parameter learning (e.g., laser power) [cite: 486, 564]</text> | |
| <text x="55" y="740" font-family="Arial, sans-serif" font-size="10" fill="#333">• Course-correcting drift and model assumptions [cite: 433, 481]</text> | |
| <text x="55" y="755" font-family="Arial, sans-serif" font-size="10" fill="#333">• Innovation analysis: Sensor vs. Forecast [cite: 464, 577]</text> | |
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| <text x="450" y="685" font-family="Arial, sans-serif" font-size="14" fill="#004a99" font-weight="bold">Reproducible Infrastructure</text> | |
| <text x="455" y="710" font-family="Arial, sans-serif" font-size="10" fill="#333">• Nix for hermetic, multi-physics environments [cite: 757, 758]</text> | |
| <text x="455" y="725" font-family="Arial, sans-serif" font-size="10" fill="#333">• Snakemake workflow management [cite: 947, 948]</text> | |
| <text x="455" y="740" font-family="Arial, sans-serif" font-size="10" fill="#333">• CV extraction of AM-Bench melt pool features [cite: 520, 521]</text> | |
| <text x="455" y="755" font-family="Arial, sans-serif" font-size="10" fill="#333">• Target: High-fidelity validation on IN718 [cite: 303, 1059]</text> | |
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| <text x="60" y="910" font-family="Arial, sans-serif" font-size="14" fill="#004a99" font-weight="bold">Digital Twin Goals [cite: 60, 777]</text> | |
| <text x="70" y="935" font-family="Arial, sans-serif" font-size="11" fill="#333">• Bridge the validity gap between open-loop casting models and L-PBF regimes [cite: 135, 136]</text> | |
| <text x="70" y="955" font-family="Arial, sans-serif" font-size="11" fill="#333">• Establish exascale-ready thermal/multiphysics simulation pipelines [cite: 238, 239]</text> | |
| <text x="70" y="975" font-family="Arial, sans-serif" font-size="11" fill="#333">• Real-time qualification/disqualification of AM parts through closed-loop control [cite: 136]</text> | |
| <text x="70" y="995" font-family="Arial, sans-serif" font-size="11" fill="#333">• Foster an integrated ecosystem for experimental and computational frameworks [cite: 120, 783]</text> | |
| <text x="400" y="1060" font-family="Arial, sans-serif" font-size="12" fill="#004a99" text-anchor="middle" font-weight="bold">AI@NIST Day 2026 — October 26, 2026</text> | |
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