Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition

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Deep learning approaches have been shown to be capable of recognizing shape features (e.g. machining features) in Computer-Aided Design (CAD) models in certain circumstances, yet still have issues when the features intersect, and in exploiting the geometric and topological information which comprises the boundary representation (B-Rep) of the typical CAD model. This paper presents a novel hierarchical B-Rep graph shape representation which encodes information about the surface geometry and face topology of the B-Rep. To learn from this new shape representation, a novel hierarchical graph convolutional network called Hierarchical CADNet has been created, which has been shown to outperform other state-of-the-art neural architectures on feature identification, including machining features that intersect, with improvements in accuracy for some more complex CAD models.
Original languageEnglish
Article number103226
Number of pages16
JournalCAD Computer Aided Design
Issue number103226
Early online date19 Feb 2022
Publication statusPublished - 01 Jun 2022


  • Machining feature recognition
  • 3D deep learning
  • Hierarchical graph convolution network
  • Computer-aided process planning (CAPP)
  • B-Rep
  • CAD


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