Clustering and retrieval of mechanical CAD assembly models based on multi-source attributes information. (August 2019)

• Record Type: Journal Article
• Authors: Han, Zhoupeng; Mo, Rong; Hao, Li
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 58(2019)
• Page Start: 220
• Page End: 229
• Publication Date: 2019-08
• Subjects: Multi-source attributes information -- Spectral clustering -- Indexing model -- Assembly clustering -- Assembly retrieval; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2019.01.003
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: An approach for clustering and retrieval of CAD assembly models is proposed by using attributes information rather than geometric and topological information. CAD assembly model is described from the point of view of multi-source attributes information. Spectral clustering is employed to cluster assembly models into different clusters in model repository. The assembly retrieval based on clustering is given through an indexing model mechanism. Abstract: Content-based CAD assembly model retrieval focuses more on the similarity measure of geometry and topology information, which can hardly meet designers' design requirements in the product conceptual design process. To search quickly and effectively the CAD assembly models related with product design requirements information for assembly model reuse, inspire designers' design thought and product design innovation in the product conceptual design phase, a novel approach for CAD assembly model clustering and retrieval based on multi-source attributes information is proposed. First, the CAD assembly model is represented by attribute adjacency graph (AAG) with multiple attributes information, and the similarity between assembly models is evaluated comprehensively by considering part information and assembly relationship information. Then, a weighted graph is constructed for assembly clustering in model repository. Meanwhile, an improved spectral clustering algorithm is given to divide CAD assembly models into different assembly clusters. Subsequently, indexing model is established for each assembly cluster. After that, the process of assembly retrieval based on indexing model mechanism is given in detail. Finally, experiments on the prototype system verify effectiveness and feasibility of the proposed approach for assembly clustering and retrieval.