Generic face adjacency graph for automatic common design structure discovery in assembly models

TLDR: A generic face adjacency graph (GFAG) is proposed which is extended from the face adjACency graph for B-Rep part model description and can take more geometric information, together with topological information, into account quantitatively, and thus can be mapped into a 2D coordinate system for easy validation.

Abstract: Common Design Structure Discovery (CDSD) is to identify local structures shared by multiple models. Nowadays it is mainly restricted to part models. Extending it to assembly models can produce a significant value for assembly design reuse. However, current descriptions of assembly models usually capture topological information qualitatively, considering little geometric information, and thus are not suitable for CDSD in assembly models (CDSDA). To counter this problem, this paper proposes a generic face adjacency graph (GFAG) which is extended from the face adjacency graph for B-Rep part model description. GFAG can transform abstract relationships in assembly models into measurable entities by introducing a concept of mating face pair (MFP), thus facilitating a more quantitative and consistent description of parts and relationships in assembly models. Corresponding to geometric faces and edges in a part model, GFAG treats parts and relationships in an assembly model as generic faces and generic edges respectively. To make GFAG have a higher discrimination capability, a node in GFAG captures the geometric information of a part together with its mating parts by shape parameters and also quantitatively incorporates a relationship between parts by shape parameters of an MFP. By doing so, GFAG can take more geometric information, together with topological information, into account quantitatively, and thus can be mapped into a 2D coordinate system for easy validation. We also extend a discovery algorithm to validate the feasibility of GFAG for CDSDA, and the results demonstrate the expected effectiveness.