Identification of material properties using nanoindentation and surrogate modeling

Han Li, Leonardo Gutierrez, Hiroyuki Toda, Osamu Kuwazuru, Wenli Liu, Yoshihiko Hangai, Masakazu Kobayashi, Rafael Batres

研究成果: Contribution to journalArticle査読

12 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)151-159
ページ数9
ジャーナルInternational Journal of Solids and Structures
81
DOI
出版ステータス出版済み - 3 1 2016

All Science Journal Classification (ASJC) codes

  • モデリングとシミュレーション
  • 材料科学(全般)
  • 凝縮系物理学
  • 材料力学
  • 機械工学
  • 応用数学

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