AutoMate: a dataset and learning approach for automatic mating of CAD assemblies

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.装配建模是计算机辅助设计（CAD）的核心任务，约占CAD工作流程总工作量的三分之一。因此，优化该流程在CAD系统研发中具备极大的应用潜力，但当前基于装配的建模研究无法直接适配现代CAD系统，原因在于其未采用现代CAD的主流数据结构：参数化边界表示（BREPs）。CAD装配建模将装配体定义为零件间成对约束的系统，这类约束被称为配合（mates），其定义基于BREP拓扑结构，而非现有研究中通用的世界坐标系。我们提出了SB-GCN，一种可保留零件拓扑结构的BREP表征学习方案，并利用学习得到的表征预测CAD装配配合类型。为训练该模型，我们构建了首个大规模BREP CAD装配体数据集，并将其与基准配合预测任务一同公开发布。最后，我们通过开发一款辅助用户完成装配配合创建的工具，验证了本模型与现有商用CAD系统的兼容性：该工具可提供配合补全建议，准确率达72.2%。

搜集汇总

背景与挑战

背景概述

AutoMate是首个大规模边界表示（BREP）CAD装配数据集，旨在支持装配建模研究，专注于现代CAD系统中的参数化边界表示结构。该数据集用于训练SB-GCN模型以预测CAD装配中的配对约束（mates），并在实际商业CAD系统中实现辅助工具，配对完成建议的准确率达72.2%。