Generative design of conformal cubic periodic cellular structures using a surrogate model-based optimisation scheme
Jun Wang and
Rahul Rai
International Journal of Production Research, 2022, vol. 60, issue 5, 1458-1477
Abstract:
Cellular structures (CSs) exhibit unique combinations of physical properties, including low weight, high structural strength, and substantial energy absorption, which could be useful in a variety of applications. Further, with the advent of additive manufacturing (AM), CSs are now easier to fabricate. While CSs and AM open up transformative opportunities, their potential for everyday use in industrial practice still lies largely idle. One of the major reasons is the lack of computational tools that allow us to automatically explore, verify, and optimise CSs and skin elements to create an optimised component that meets the exact specification. In this paper, we outline a periodic CS-based generative design pipeline that offers automated modelling, analysis, and inverse design solving of CS through the use of an integrated optimisation and finite-element analysis (FEA) framework. Specifically, a surrogate model-based optimisation scheme is proposed to design light-weight and high-strength functional parts by taking advantage of spatially varying conformal cubic periodic cellular structures.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2020.1859637 (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:60:y:2022:i:5:p:1458-1477
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2020.1859637
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 ().