journal article·2025-09

Quantifying the Relationship between Structure and Yield Stress in the Evolution of Aluminum Foam

Wei Zhao, Siyuan He, Mingqiang Wei, Yi Zhang, Wenhao Chen, Ruicheng Wang, Luo Han, Yangjiangshan Xu

Advanced Engineering Materials· Volume 27· Issue 18
Publisher page

Abstract

This study explores an extension of the scaling law to enhance the prediction accuracy of yield stresses in aluminum foam. To complete that, the series of aluminum foam samples are prepared via the melt foaming method with different holding periods. After that, the porous structure information is obtained in terms of X‐ray computed tomography (CT) scanning. During the sample preparation process, the pore structure undergoes a “merging point,” where the numerous small pores transition to large pores with reduced sphericity. The incorporation of additional five parameters (relative density, diameter, solid material thickness, anisotropy, and sphericity) into the expanded law of proportions significantly improves the residual value R2 of the predicted results, increasing from 0.7251 in the Gibson–Ashby equation to 0.8926. Additionally, after eliminating redundant parameters through stepwise linear regression, the simplified equation still achieves a residual value R2 of 0.8699. The remaining structural parameters, including porosity, sphericity, solid material thickness, and anisotropy, independently are found to have an influence on the yield stress through covariance analysis. By correlating these processes, it is expected that the mechanical properties of aluminum foam can be precisely regulated by adjusting the parameters.
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Research context

OpenAlex topics: Cellular and Composite Structures · Tribology and Lubrication Engineering · Aluminum Alloy Microstructure Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China

Key Laboratory of Brain‐Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China

State Key Laboratory of Bioelectronics School of Biological Science & Medical Engineering Southeast University Nanjing 210096 P. R. China

School of Material Science and Engineering Jiangsu University of Science and technology Zhenjiang 212100 P. R. China

Beijing Blue Sky Innovation Center for Frontier Science Innovation Center for Science and Technology Beijing 100085 P. R. China

Funding

National Natural Science Foundation of China

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