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| Recognition method of parts machining features based on graph neural network |
Xinhua YAO( ),Tao YU,Senwen FENG,Zijian MA,Congcong LUAN,Hongyao SHEN |
| School of Mechanical Engineering, Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China |
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Abstract A method for recognizing machining features based on graph neural networks was proposed in order to address the difficulties in identifying intersecting features and accurately determining machining feature surfaces in existing deep learning-based approaches. Features of nodes and adjacent edges were extracted through a compression activation module, and a dual-layer attention network at the node and adjacent edge levels was constructed in order to segment the machining features corresponding to each node. The surface features and edge features of the part model were fully used combined with the topological structure of the part model. The recognition problem of non-face merged intersecting features was effectively addressed by employing attention mechanisms for deep learning on the feature information. The proposed method was experimentally compared with three other feature recognition methods on a dataset of parts with multiple machining features. The optimal results were obtained in terms of accuracy, average class accuracy and intersection-over-union metrics. The recognition accuracy exceeded 95%.
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Received: 03 July 2023
Published: 23 January 2024
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| Fund: 浙江省重点研发计划资助项目 （2021C01096）；浙江省杰出青年科学基金资助项目（LR22E050002）. |
基于图神经网络的零件机加工特征识别方法
针对现有基于深度学习的方法存在的难以识别相交特征、无法精确确定加工特征面的问题，提出基于图神经网络的加工特征识别方法. 通过压缩激励模块提取节点与邻接边的特征，构建节点级与邻接边级的双层注意力网络，分割每个节点对应的加工特征. 该方法充分利用了零件模型的面特征与边特征，结合零件模型的拓扑结构，基于注意力机制对特征信息进行深度学习，可以有效地解决非面合并相交特征的识别问题. 在多加工特征零件数据集上，将该方法与其他3种特征识别方法进行实验对比，在准确率、平均类准确率和交并比3项指标上均取得最优结果，识别准确率高于95%.
关键词：
加工特征,
属性邻接图,
图神经网络,
注意力机制,
深度学习
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