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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\n            <jats:italic>mates<\/jats:italic>\n            , 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.\n          <\/jats:p>","DOI":"10.1145\/3478513.3480562","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:28:45Z","timestamp":1639160925000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["AutoMate"],"prefix":"10.1145","volume":"40","author":[{"given":"Benjamin","family":"Jones","sequence":"first","affiliation":[{"name":"University of Washington"}]},{"given":"Dalton","family":"Hildreth","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Duowen","family":"Chen","sequence":"additional","affiliation":[{"name":"Columbia University"}]},{"given":"Ilya","family":"Baran","sequence":"additional","affiliation":[{"name":"PTC Inc."}]},{"given":"Vladimir G.","family":"Kim","sequence":"additional","affiliation":[{"name":"Abobe Inc."}]},{"given":"Adriana","family":"Schulz","sequence":"additional","affiliation":[{"name":"University of Washington"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_2_1_2_1","unstructured":"Weijuan Cao Trevor Robinson Yang Hua Flavien Boussuge Andrew R. Colligan and Wanbin Pan. 2020. Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning (International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Vol. Volume 11A: 46th Design Automation Conference (DAC)).  Weijuan Cao Trevor Robinson Yang Hua Flavien Boussuge Andrew R. Colligan and Wanbin Pan. 2020. Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning (International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Vol. Volume 11A: 46th Design Automation Conference (DAC) )."},{"key":"e_1_2_1_3_1","unstructured":"Siddhartha Chaudhuri Evangelos Kalogerakis Leonidas Guibas and Vladlen Koltun. 2011. ACM Transactions on Graphics (SIGGRAPH).  Siddhartha Chaudhuri Evangelos Kalogerakis Leonidas Guibas and Vladlen Koltun. 2011. 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