| 引用本文: | 赵显文,莫轩东,夏铭远,胡小锋.融合图神经网络与概率编码的加工特征识别[J].哈尔滨工业大学学报,2025,57(4):116.DOI:10.11918/202401065 |
| ZHAO Xianwen,MO Xuandong,XIA Mingyuan,HU Xiaofeng.Machining feature recognition based on integration of graph neural network and probabilistic embedding[J].Journal of Harbin Institute of Technology,2025,57(4):116.DOI:10.11918/202401065 |
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| 摘要: |
| 为解决多加工特征交叉下的特征定位问题,提高复杂零件加工特征识别性能,提出实例分割框架下的加工特征识别方法Brep3pNet。首先,基于三维模型的边界表示,提取面点云、面邻接图等几何与拓扑数据,构建三维模型的图表示,利用点云学习网络以及图神经网络学习三维模型面级嵌入表示。其次,提出概率位置编码方法,引入位置先验信息将三维模型各面编码为与空间位置相关的三元高斯分布,基于Bhattacharyya核度量面间相似性,以实现加工特征的面级定位,生成候选实例。最后,设计得分网络用于预测实例生成质量,以此指导实例间的非极大抑制,去除冗余特征实例, 获得最终加工特征识别结果。本研究在MFCAD、MFCAD++、MFInstSeg和合成的回转类零件数据集等4个多特征数据集上对所提方法进行评估。研究结果表明:Brep3pNet相较于其他先进方法具有更好的特征定位能力,可以通过轻量的模型参数实现最优的特征识别准确率,展现了所提方法在相交特征识别上的应用潜力。 |
| 关键词: 加工特征识别 实例分割 点云 图神经网络 概率编码 |
| DOI：10.11918/202401065 |
| 分类号:TH166 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51975373) |
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| Machining feature recognition based on integration of graph neural network and probabilistic embedding |
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ZHAO Xianwen1,MO Xuandong1,XIA Mingyuan1,HU Xiaofeng1,2
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(1.School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2.Shanghai Key Laboratory of Advanced Manufacturing Environment (Shanghai Jiao Tong University), Shanghai 200240, China）
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| Abstract: |
| To solve the problem of feature localization with multi-feature intersection and improve the performance of machining feature recognition for complex parts, this paper proposes Brep3pNet, a machining feature recognition method under the framework of instance segmentation. Firstly, based on the boundary representation (B-rep) of 3D models, Brep3pNet extracts geometric and topological data such as face point clouds and face adjacency graphs to construct a graph representation of the 3D model. We utilize point cloud learning networks and graph neural networks to learn surface-level embedding representations of the 3D model. Secondly, a probabilistic positional embedding method is proposed, which introduces spatial position prior information to encode the face into ternary Gaussian distribution, and measures the similarity among those face embeddings by Bhattacharyya kernel for the purpose of locating machining features and generating candidate machining feature instances. Finally, a score network is designed to predict the quality of the instance generated, so as to guide the non-maximum suppression between instances to remove redundant feature instances, thereby obtaining the final machining features. Brep3pNet is evaluated on four multi-feature datasets, including MFCAD, MFCAD++, MFInstSeg and a synthetic dataset of rotary parts. The research results indicate that Brep3pNet outperforms other state-of-the-art methods on feature localization accuracy, and can achieve optimal feature recognition accuracy with lightweight model parameters, demonstrating its potential application in intersecting features recognition. |
| Key words: machining feature recognition instance segmentation point cloud graph neural network probabilistic embedding |
