CAD Parts-Based Assembly Modeling by Probabilistic Reasoning

Kai-Ke Zhang, Kai-Mo Hu, Li-Cheng Yin, Dongming Yan, Bin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system. © 2015 IEEE.
Original languageEnglish (US)
Title of host publication2015 14th International Conference on Computer-Aided Design and Computer Graphics (CAD/Graphics)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages89-96
Number of pages8
ISBN (Print)9781467380201
DOIs
StatePublished - Apr 11 2016

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