JOURNAL ARTICLE
Effects of Folding Degree and Mass Fraction on the Static and Natural Frequency Characteristics of Functionally Graded Graphene Origami–Enabled Auxetic Metamaterials Annular Plates.
Published In: International Journal of Structural Stability & Dynamics, 2026, v. 26, n. 15. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yao, Yujuan; Arshid, Ehsan 3 of 3
Abstract
This study addresses a critical gap in the literature by investigating the static and natural frequency characteristics of functionally graded (FG) auxetic metamaterial annular plates reinforced with graphene origami (GOri), a novel area previously unexplored in the context of composite constructions, particularly for circular plates. The governing equations are derived utilizing higher-order shear deformation theory along with Hamilton's principle, and solved using the finite element approach. For the first time, a comprehensive parametric study including the folding degree and mass fraction, and distribution pattern of GOri, is investigated on the static and natural frequency properties of annular plates. It is found that the natural frequency generally increased with higher mass fractions and decreased with greater folding degrees, though the X and V patterns at a 3% mass fraction showed an atypical increase in frequency with higher folding degrees. The impact of distribution patterns varied with weight fraction: the X-pattern caused the highest deflection at 1% weight fraction but the lowest at 3%, while the O-pattern caused the least deflection overall. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Structural Stability & Dynamics. 2026/07, Vol. 26, Issue 15, p1
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2026
- ISSN:0219-4554
- DOI:10.1142/S0219455426501282
- Accession Number:192787899
- Copyright Statement:Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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