Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition
Hierarchical CADNet: Learning from B-Reps for Machining Feature Recognition
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DOI：
10.1016/j.cad.2022.103226
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发表时间：
2022-02-19
影响因子：
4.3
通讯作者：
Cao, Weijuan
中科院分区：
文献类型：
--
作者：
Colligan, Andrew R.;Robinson, Trevor T.;Cao, Weijuan
深度学习方法已被证明能够在某些情况下识别计算机辅助设计（CAD）模型中的形状特征（例如，机加工特征），但当特征相交时，以及在利用包括典型CAD模型的边界表示（B-Rep）的几何和拓扑信息时，仍然存在问题。本文提出了一种新的分层B-Rep图形形状表示，它编码了有关B-Rep的表面几何形状和面拓扑结构的信息。为了从这种新的形状表示中学习，创建了一种称为分层CADNet的新型分层图卷积网络，该网络已被证明在特征识别方面优于其他最先进的神经架构，包括相交的加工特征，对于一些更复杂的CAD模型，精度有所提高。(c)2022爱思唯尔有限公司保留所有权利。
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.(c) 2022 Elsevier Ltd. All rights reserved.