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.

Andrew R. Colligan

Trevor T. Robinson

Declan C. Nolan

Yang Hua

Weijuan Cao

Computer-Aided Design

COMPUTER-AIDED DESIGN 是一份领先的国际期刊，为学术界和工业界提供有关计算机在设计中的应用研究和发展的重要论文。COMPUTER-AIDED DESIGN 邀请报告新研究以及新颖或特别重要应用的论文，广泛的主题，涵盖从概念创建到制造等设计过程的所有阶段。

CAD Computer Aided Design is a leading international journal that provides important papers on the application research and development of computers in design for both academia and industry. The journal invites reports of new research and papers on novel or particularly significant applications, covering a wide range of topics throughout all stages of the design process, from concept creation to manufacturing.

《CAD Computer Aided Design》是一本领先的国际期刊，专注于计算机在设计领域的应用研究与发展。该期刊欢迎提交新研究成果以及具有新颖性或特别重要性的应用论文，涵盖从概念创作到制造等设计过程的各个阶段。

Category	Quartile
计算机科学	3
计算机科学, 计算机软件工程	3

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

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Journal Info

Category	Quartile
COMPUTER SCIENCE, SOFTWARE ENGINEERING	2