Conference paper Open Access
Owen, Steven; Shead, Timothy M.; Martin, Shawn
We describe new machine-learning-based methods to defeature CAD models for tetrahedral meshing. Using machine learning predictions of mesh quality for geometric features of a CAD model prior to meshing we can identify potential problem areas and improve meshing outcomes by presenting a prioritized list of suggested geometric operations to users. Our machine learning models are trained using a combination of geometric and topological features from the CAD model and local quality metrics for ground truth. We demonstrate a proof-of-concept implementation of the resulting workflow using Sandia’s Cubit Geometry and Meshing Toolkit.
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