TY - JOUR
T1 - High temperature and mesostructure effect on aluminum foam compression responses
AU - Xiao, Sihang
AU - Zhao, Zeang
AU - Duan, Shengyu
AU - Chen, Yanfei
AU - Wang, Yaoqi
AU - Wang, Panding
AU - Lei, Hongshuai
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/8/1
Y1 - 2024/8/1
N2 - Closed-cell Al foam is widely utilized in high-temperature environments due to its high strength-to-weight ratio and considerable energy absorption capability. However, the mechanical properties depend on temperature and mesostructure. In this paper, the compressive responses of Al foams at temperatures ranging from 25 ℃ to 600 ℃ are investigated by experimental tests and two types of high-fidelity finite element simulations, including the X-ray computed tomography (CT) reconstructed model and Voronoi model. A deep learning approach is employed to remove tiny pores of CT slices, simplifying geometry features and improving the computational efficiency of the CT reconstructed model. A novel improved Voronoi model with high accuracy is proposed, considering the nonuniform distribution of cell wall thickness in actual closed-cell Al foams. A temperature-depend macro constitutive model is established and validated through the experimental and numerical results. This work reveals the influence mechanism of temperature and mesostructure on the mechanical responses of Al foams, and exhibits significant potential for the application of Al foams in high temperature environment.
AB - Closed-cell Al foam is widely utilized in high-temperature environments due to its high strength-to-weight ratio and considerable energy absorption capability. However, the mechanical properties depend on temperature and mesostructure. In this paper, the compressive responses of Al foams at temperatures ranging from 25 ℃ to 600 ℃ are investigated by experimental tests and two types of high-fidelity finite element simulations, including the X-ray computed tomography (CT) reconstructed model and Voronoi model. A deep learning approach is employed to remove tiny pores of CT slices, simplifying geometry features and improving the computational efficiency of the CT reconstructed model. A novel improved Voronoi model with high accuracy is proposed, considering the nonuniform distribution of cell wall thickness in actual closed-cell Al foams. A temperature-depend macro constitutive model is established and validated through the experimental and numerical results. This work reveals the influence mechanism of temperature and mesostructure on the mechanical responses of Al foams, and exhibits significant potential for the application of Al foams in high temperature environment.
KW - Closed-cell aluminum foam
KW - Deep learning approach
KW - Improved Voronoi model
KW - Mechanical performance
KW - Temperature effect
UR - http://www.scopus.com/inward/record.url?scp=85192263718&partnerID=8YFLogxK
U2 - 10.1016/j.ijmecsci.2024.109344
DO - 10.1016/j.ijmecsci.2024.109344
M3 - Article
AN - SCOPUS:85192263718
SN - 0020-7403
VL - 275
JO - International Journal of Mechanical Sciences
JF - International Journal of Mechanical Sciences
M1 - 109344
ER -