multi-scale modeling for predicting the stiffness and strength of hollow-structured metal foams with structural hierarchy
Clicks: 234
ID: 172107
2018
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Steady Performance
30.0
/100
233 views
14 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
This work was inspired by previous experiments which managed to establish an optimal template-dealloying route to prepare ultralow density metal foams. In this study, we propose a new analytical–numerical model of hollow-structured metal foams with structural hierarchy to predict its stiffness and strength. The two-level model comprises a main backbone and a secondary nanoporous structure. The main backbone is composed of hollow sphere-packing architecture, while the secondary one is constructed of a bicontinuous nanoporous network proposed to describe the nanoscale interactions in the shell. Firstly, two nanoporous models with different geometries are generated by Voronoi tessellation, then the scaling laws of the mechanical properties are determined as a function of relative density by finite volume simulation. Furthermore, the scaling laws are applied to identify the uniaxial compression behavior of metal foams. It is shown that the thickness and relative density highly influence the Young’s modulus and yield strength, and vacancy defect determines the foams being self-supported. The present study provides not only new insights into the mechanical behaviors of both nanoporous metals and metal foams, but also a practical guide for their fabrication and application.
Abstract Quality Issue:
This abstract appears to be incomplete or contains metadata (187 words).
Try re-searching for a better abstract.
| Reference Key |
yi2018materialsmulti-scale
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Yong Yi;Xiaoyang Zheng;Zhibing Fu;Chaoyang Wang;Xibin Xu;Xiulan Tan |
| Journal | Nature Materials |
| Year | 2018 |
| DOI |
10.3390/ma11030380
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.