Abstract
In theory, identification of material properties of microscopic materials, such as thin film or single crystal, could be carried out with physical experimentation followed by simulation and optimization to fit the simulation result to the experimental data. However, the optimization with a number of finite element simulations tends to be computationally expensive. This paper proposes an identification methodology based on nanoindentation that aims at achieving a small number of finite element simulations. The methodology is based on the construction of a surrogate model using artificial neural-networks. A sampling scheme is proposed to improve the quality of the surrogate model. In addition, the differential evolution algorithm is applied to identify the material parameters that match the surrogate model with the experimental data. The proposed methodology is demonstrated with the nanoindentation of an aluminum matrix in a die cast aluminum alloy. The result indicates that the methodology has good computational efficiency and accuracy.
| Original language | English |
|---|---|
| Pages (from-to) | 151-159 |
| Number of pages | 9 |
| Journal | International Journal of Solids and Structures |
| Volume | 81 |
| DOIs | |
| Publication status | Published - Mar 1 2016 |
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All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Materials Science(all)
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering
- Applied Mathematics
Cite this
Identification of material properties using nanoindentation and surrogate modeling. / Li, Han; Gutierrez, Leonardo; Toda, Hiroyuki; Kuwazuru, Osamu; Liu, Wenli; Hangai, Yoshihiko; Kobayashi, Masakazu; Batres, Rafael.
In: International Journal of Solids and Structures, Vol. 81, 01.03.2016, p. 151-159.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Identification of material properties using nanoindentation and surrogate modeling
AU - Li, Han
AU - Gutierrez, Leonardo
AU - Toda, Hiroyuki
AU - Kuwazuru, Osamu
AU - Liu, Wenli
AU - Hangai, Yoshihiko
AU - Kobayashi, Masakazu
AU - Batres, Rafael
PY - 2016/3/1
Y1 - 2016/3/1
N2 - In theory, identification of material properties of microscopic materials, such as thin film or single crystal, could be carried out with physical experimentation followed by simulation and optimization to fit the simulation result to the experimental data. However, the optimization with a number of finite element simulations tends to be computationally expensive. This paper proposes an identification methodology based on nanoindentation that aims at achieving a small number of finite element simulations. The methodology is based on the construction of a surrogate model using artificial neural-networks. A sampling scheme is proposed to improve the quality of the surrogate model. In addition, the differential evolution algorithm is applied to identify the material parameters that match the surrogate model with the experimental data. The proposed methodology is demonstrated with the nanoindentation of an aluminum matrix in a die cast aluminum alloy. The result indicates that the methodology has good computational efficiency and accuracy.
AB - In theory, identification of material properties of microscopic materials, such as thin film or single crystal, could be carried out with physical experimentation followed by simulation and optimization to fit the simulation result to the experimental data. However, the optimization with a number of finite element simulations tends to be computationally expensive. This paper proposes an identification methodology based on nanoindentation that aims at achieving a small number of finite element simulations. The methodology is based on the construction of a surrogate model using artificial neural-networks. A sampling scheme is proposed to improve the quality of the surrogate model. In addition, the differential evolution algorithm is applied to identify the material parameters that match the surrogate model with the experimental data. The proposed methodology is demonstrated with the nanoindentation of an aluminum matrix in a die cast aluminum alloy. The result indicates that the methodology has good computational efficiency and accuracy.
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UR - http://www.scopus.com/inward/citedby.url?scp=84956689181&partnerID=8YFLogxK
U2 - 10.1016/j.ijsolstr.2015.11.022
DO - 10.1016/j.ijsolstr.2015.11.022
M3 - Article
AN - SCOPUS:84956689181
VL - 81
SP - 151
EP - 159
JO - International Journal of Solids and Structures
JF - International Journal of Solids and Structures
SN - 0020-7683
ER -