机械科学与技术2025，Vol.44Issue(1)：30-39,10.DOI:10.13433/j.cnki.1003-8728.20240087
SSGCN-混合式图卷积网络:用于三维CAD模型的加工特征识别
SSGCN-hybrid Graph Convolutional Networks for 3D CAD Model Machining Feature Recognition
摘要
Abstract
A hybrid spectral domain and spatial domain graph convolution network(SSGCN)algorithm is proposed to solve the problem of 3D CAD model machining feature recognition in the CAD/CAPP/CAM integration process.The graph data structure is constructed with the surface of the 3D model as the node and the edge as the connection between the nodes.The geometric attribute information of the surface is extracted,and the node attribute matrix is constructed by custom coding as the input of the network.The hybrid graph convolutional network is constructed by extracting the adjacency matrix and degree matrix of graph structure.Through the Python-OCC related algorithms and Boolean operation,an algorithm for batch generation of machining feature model dataset with face labels is designed.The machining feature model dataset with face labels is used to train the network and test the machining feature model,and a good recognition effect has been obtained.关键词
CAD模型/图卷积网络/加工特征识别/邻接矩阵Key words
CAD model/graph convolutional network/machining feature recognition/adjacency matrix分类
机械制造引用本文复制引用
王洪申,王尚旭,强会英..SSGCN-混合式图卷积网络:用于三维CAD模型的加工特征识别[J].机械科学与技术,2025,44(1):30-39,10.基金项目
国家自然科学基金项目(61962035) （61962035）
