Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition. (June 2022)

• Record Type: Journal Article
• Authors: Colligan, Andrew R.; Robinson, Trevor T.; Nolan, Declan C.; Hua, Yang; Cao, Weijuan
• Is Part Of: Computer aided design. Volume 147(2022)
• Publication Date: 2022-06
• Subjects: Machining feature recognition -- 3D deep learning -- Hierarchical graph convolution network -- Computer-aided process planning (CAPP) -- B-Rep -- CAD; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2022.103226
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: 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. Highlights: 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.