BRepGAT: Graph neural network to segment machining feature faces in a B-rep model

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jinwon Lee, Changmo Yeo, Sang-Uk Cheon, Jun Hwan Park, D. Mun
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引用次数: 0

Abstract

In recent years, there have been many studies using artificial intelligence to recognize machining features in 3D models in the CAD/CAM field. Most of these studies converted the original CAD data into images, point clouds, or voxels for recognition. This led to information loss during the conversion process, resulting in decreased recognition accuracy. In this paper, we propose a graph-based network called BRepGAT to segment faces in an original B-rep model containing machining features. We define descriptors that represent information about the faces and edges of the B-rep model from the perspective of feature recognition. These descriptors are extracted from the B-rep model and transformed into homogeneous graph data, which is then passed to graph networks. BRepGAT recognize machining features on a face-by-face based on the graph data input. Our experimental results using the MFCAD18++ dataset showed that BRepGAT achieved state-of-the-art recognition accuracy (99.1%). Furthermore, BRepGAT showed relatively robust performance on other datasets besides MFCAD18++.
BRepGAT:在 B-rep 模型中分割加工特征面的图神经网络
近年来,在计算机辅助设计/制造(CAD/CAM)领域,有许多利用人工智能识别三维模型中加工特征的研究。这些研究大多将原始 CAD 数据转换为图像、点云或体素进行识别。这导致了转换过程中的信息丢失,从而降低了识别精度。在本文中,我们提出了一种名为 BRepGAT 的基于图的网络,用于分割包含加工特征的原始 B-rep 模型中的人脸。我们从特征识别的角度定义了描述符,这些描述符代表了 B-rep 模型中的面和边的信息。这些描述符从 B-rep 模型中提取并转换为同质图数据,然后传递给图网络。BRepGAT 根据输入的图数据逐面识别加工特征。我们使用 MFCAD18++ 数据集进行的实验结果表明,BRepGAT 达到了最先进的识别准确率(99.1%)。此外,BRepGAT 在 MFCAD18++ 之外的其他数据集上也表现出了相对稳健的性能。
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
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