Deep learning for boundary representation CAD models

The ambition is to use deep learning for an automatic feature recognition algorithm to identify geometric features to help automate the CAD to analysis pre-processing task of defeaturing, as it is a necessary but time-consuming operation. The three main activities to achieve this ambition were: i) construct a shape representation that could both encode information carried by the B-Rep, while also being suitable as a direct input to a deep learning algorithm, ii) develop a deep learning algorithm that could take advantage of and learn from this shape representation, and iii) generate a dataset that can be used for the automatic feature recognition task.

In response to these objectives a new shape representation called a hierarchical B-Rep graph was constructed that encodes the geometry and topology of the B-Rep CAD model through a hierarchical graph, denoting information about a 2D surface mesh and B-Rep face topology. The neural architecture developed to learn from the hierarchical B-Rep graphs is called Hierarchical CADNet. It is composed of two spatial graph convolutional networks that operate on either level of the hierarchical B-Rep graph, while facilitating information sharing between the networks.