AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies
This addresses a core bottleneck in CAD workflows for designers, offering a practical tool for commercial systems, though it is incremental as it builds on existing representation learning methods.
The paper tackles the problem of automating CAD assembly modeling by predicting pairwise constraints (mates) between parts using a representation learning scheme on parametric boundary representations (BREPs), achieving 72.2% accuracy in mate completion suggestions.
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.