Identification of material properties using nanoindentation and surrogate modeling

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Identification of material properties using nanoindentation and surrogate modeling

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Article
Kyushu Univ. Production Kyushu Univ. Production
Responsibility:
Li, Han(Department of Mechanical Engineering, Kyushu University)
Gutierrez, Leonardo(Plasess (Rumania) S.R.L.)
Toda, Hiroyuki(Faculty of Engineering, Kyushu University)

戸田, 裕之(九州大学大学院工学研究院)
Kuwazuru, Osamu(Department of Nuclear Power and Energy Safety Engineering, University of Fukui)
桑水流, 理(福井大学)
Liu, Wenli(Department of Nuclear Power and Energy Safety Engineering, University of Fukui)
Hangai, Yoshihiko(Department of Mechanical Science and Technology, Gunma University)
半谷, 禎彦(群馬大学理工学部)
Kobayashi, Masakazu(Department of Mechanical Engineering, Toyohashi University of Technology)
小林, 正和(豊橋技術科学大学)
Batres, Rafael(Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey)

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Language:
English
Publication info:
International Journal of Solids and Structures. 81, pp. 151-159, 2016-05-01. Pergamon Press
Version:
Author
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. Read more
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