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DOE OSTI · 2523619

Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing

Schmitz, Tony
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Abstract

The Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing project objective is to automatically tune machining parameter predictions from physics-based models using process data and Bayesian machine learning. The intent is to enable a step change in aerospace manufacturing by combining machine learning, physics-based process models, and sensors/data in a comprehensive digital environment that simultaneously considers the computer numerically controlled (CNC) machining center capabilities, the workpiece material and geometry, and the workpiece support (fixturing). The project hypothesis is that this combination will enable improved performance in machining operations.

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42 ENGINEERING47 OTHER INSTRUMENTATION97 MATHEMATICS AND COMPUTINGBayesian machine learningDynamicsMachiningManufacturingPhysics-Guided Machine Learning

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Schmitz, Tony. 2024-10-31. Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing. https://doi.org/10.2172/2523619

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