Assembly modeling is a core task of computer aided design (CAD), comprising
around one third of the work in a CAD workflow. Optimizing this process
therefore represents a huge opportunity in the design of a CAD system, but
current research of assembly based modeling is not directly applicable to
modern CAD systems because it eschews the dominant data structure of modern
CAD: parametric boundary representations (BREPs). CAD assembly modeling defines
assemblies as a system of pairwise constraints, called mates, between parts,
which are defined relative to BREP topology rather than in world coordinates
common to existing work. We propose SB-GCN, a representation learning scheme on
BREPs that retains the topological structure of parts, and use these learned
representations to predict CAD type mates. To train our system, we compiled the
first large scale dataset of BREP CAD assemblies, which we are releasing along
with benchmark mate prediction tasks. Finally, we demonstrate the compatibility
of our model with an existing commercial CAD system by building a tool that
assists users in mate creation by suggesting mate completions, with 72.2%
accuracy.

该研究提出了一种基于边界表示 (BREPs) 的装配建模方法，使用 SB-GCN 进行表征学习，以预测 CAD 类型的 mates 关系，并显示该模型与商业 CAD 系统的兼容性。