Generating significant subassemblies from 3D assembly models for design reuse

Chao Zhang, Guanghui Zhou, Qi Lu and Fengtian Chang

International Journal of Production Research, 2018, vol. 56, issue 14, 4744-4761

Abstract: Significant subassemblies are defined as the reusable regions of pre-existing 3D assembly models. A significant subassembly has great significances for design reuse as it aggregates abundant knowledge in a vivid 3D CAD model and enables designers to reuse existing mature designs from a high-level perspective. Consequently, this paper contributes to significant subassembly generation from pre-existing 3D assembly models for design reuse. The paper first gives an explicit definition of significant subassemblies and further explores the multilevel knowledge embedded in these significant subassemblies. Based on the above definition and multilevel knowledge, a knowledge-based approach is then proposed for significant subassembly generation, which includes three phases: (1) identifying candidate subassemblies with high cohesion inside and low coupling outside using the Markov clustering process; (2) removing normal candidate subassemblies with low reusability and less information, and generating filtered subassemblies using the proposed assembly frequency – inverse mean subassembly frequency based scheme; and (3) determining significant subassemblies by measuring the complexity of the filtered subassemblies. Finally, a computer numerical control honing machine model is taken as an application example to demonstrate the effectiveness of the proposed approach.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1465608 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:56:y:2018:i:14:p:4744-4761

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2018.1465608

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().