Automatic shape adaptation scheme planning for CAD models in direct modeling. (December 2022)

• Record Type: Journal Article
• Authors: Pan, Wanbin; Chen, Chao; Yang, Yuncan; Gao, Shuming; Wang, Yigang; Wang, Shufang
• Is Part Of: Computer aided design. Volume 153(2022)
• Publication Date: 2022-12
• Subjects: Attributed face-layout graph -- Topology-consistent shape adaptation -- Graph-incremental shape adaptation -- Direct modeling planning -- CAD; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2022.103405
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: Designing a new CAD model by adapting a retrieved candidate model offers several advantages; however, shape adaptation is often a tough job. To facilitate shape adaptation, direct modeling (technology) provides a very flexible and intuitive approach. However, based on new design requirements, the research on automatic and effective methods to determine the geometries on the candidate model that need adapting and how to adapt these geometries has rarely been reported till date. The shape adaptation based on direct modeling is often dependent on manual processing and thus it is usually a tedious process. To account for this, a novel automatic shape adaptation scheme planning approach is proposed in this study. The candidate model is usually retrieved using a more shape-simple query model that indicates the overall shape meeting the main functional requirements of a final product (i.e., new primary shape requirements); therefore, this study focuses on the automatic adaptation of the shape of the candidate model to that of the query model to accelerate its entire adaptation progress. First, to describe the shape requirements indicated by a CAD model, an attributed face-layout graph (AFLG) is presented. Further, by comparing the candidate model's AFLG to that of the query model, the geometries (i.e., faces) on the candidate model requiring adaptation can be determined. Next, integrating with a geometric detail reuse strategy and a novel incremental adapting method on the presented graph, an improved particle swarm optimization method is developed to determine an optimized shape adaptation scheme (i.e., an optimized direct modeling operation sequence) based on the determined geometries. Finally, by applying the above-mentioned scheme, the face layout and shape parameters are smoothly transferred from the query model to the candidate model as shape requirements, and the shape of the candidate model gets automatically adapted to the query model while its geometric details are reused adaptively. Besides, a prototype system is also implemented, and the experimental results are analyzed to verify the effectiveness and characteristics of the proposed approach. Highlights: Determining geometrics with different shape requirements by comparing new descriptors. Generating optimized operation sequence based on a novel graph incremental adapting. Reusing geometric details adaptively based on topology change prediction.