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

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 comprise...

Full description

Saved in:
Bibliographic Details
Published in:Computer aided design 2022-06, Vol.147, p.103226, Article 103226
Main Authors: Colligan, Andrew R., Robinson, Trevor T., Nolan, Declan C., Hua, Yang, Cao, Weijuan
Format: Article
Language:English
Subjects:
3D deep learning
B-Rep
Boundary representation
CAD
Computer aided design
Computer-aided process planning (CAPP)
Deep learning
Feature recognition
Graphical representations
Hierarchical graph convolution network
Machine learning
Machining
Machining feature recognition
Rapid prototyping
Shape recognition
Surface geometry
Topology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. •A novel representation and deep learning framework for learning from B-Rep CAD models.•A complex CAD model dataset with labeled machining features is proposed.•Improvements over current state-of-the-art deep learning frameworks for machining feature recognition.
ISSN:0010-4485
1879-2685
DOI:10.1016/j.cad.2022.103226