Lightweight framework of shell models utilizing local heat sensors

Shengfa Wang, Longfei Zhang, Baojun Li and Zhongxuan Luo

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 11, 1550147718811303

Abstract: Lightweight is one of the most important research subjects in modern manufacturing. However, the research on lightweight of shell models is rare, and most limited in topological changes. This article proposes a local heat sensor–based lightweight framework of shell models that consists of model analysis, lightweight modeling and analysis, and three-dimensional printing and practical validation in an optimum iterative procedure. Specifically, first, both geometric features and empirical features are introduced to construct a frame structure. Second, a local diffusion–based heat sensor network is exploited to simulate the stress distribution due to two reasons: one is that they have the similar physical transmissibility and the other is that the heat diffusion is smooth, and it guarantees that the thickness variation is smooth and natural without restriction on the degrees of freedom. Then, a local iteration consists of heat simulation and stress analysis is utilized to further improve the efficiency. Finally, we use three-dimensional printer to manufacture testing models and apply them to practical verification and feedback. Our extensive experiments have exhibited many attractive properties, including the flexibility and freedom of the thickness variation, the effectiveness, and credibility of the lightweight.

Keywords: Lightweight; shell model; heat sensor; diffusion; three-dimensional printing (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1550147718811303 (text/html)

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:sae:intdis:v:14:y:2018:i:11:p:1550147718811303

DOI: 10.1177/1550147718811303

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().