CAD Defeaturing Using Machine Learning
| Autor: | Owen, Steven, Shead, Timothy M., Martin, Shawn |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| DOI: | 10.5281/zenodo.3653425 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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