Feature recognition is critical to connect CAX tools in automation via the extract of significant geometric information from CAD models. However, to extract meaningful geometric information is not easy. There are still a couple of problems, such as lack of robustness, inability to learn, limited feature types, difficult to deal with interacting features, etc. To fix these problems, a new feature recognition method based on 2D convolutional neural networks (CNNs) is proposed in this paper. Firstly, a novel feature representation scheme based on heat kernel signature is developed. Then, the feature recognition problem is transferred into a graph learning problem by using a percentage similarity clustering and node embedding technique. After that, CNN models for feature recognition are trained via the use of a large dataset of manufacturing feature models. The dataset includes ten different types of isolated features and fifteen pairs of interacting features. Finally, a set of tests for method validation are conducted. The experimental results indicate that the proposed approach not only performs well on recognizing isolated features, but also is effective in handling interacting features. The state-of-the-art performance of interacting features recognition has been improved.

通过从CAD模型中提取重要的几何信息，特征识别对于自动连接CAX工具至关重要。但是，提取有意义的几何信息并不容易。仍然存在一些问题，例如缺乏鲁棒性，学习能力不足，特征类型有限，难以处理交互特征等。为解决这些问题，一种基于2D卷积神经网络（CNN）的新特征识别方法本文提出。首先，提出了一种基于热核特征的新颖特征表示方案。然后，利用百分比相似度聚类和节点嵌入技术将特征识别问题转化为图学习问题。之后，通过使用制造特征模型的大型数据集来训练用于特征识别的CNN模型。数据集包括十种不同类型的隔离特征和十五对交互特征。最后，进行了一组方法验证的测试。实验结果表明，所提出的方法不仅在识别孤立特征方面表现良好，而且在处理交互特征方面也很有效。交互特征识别的最新性能已得到改善。而且在处理交互功能方面也很有效。交互特征识别的最新性能已得到改善。而且在处理交互功能方面也很有效。交互特征识别的最新性能已得到改善。