Manufacturing feature recognition with a 2D convolutional neural network

Abstract

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.