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

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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 languageEnglish
Pages (from-to)151-159
Number of pages9
JournalInternational Journal of Solids and Structures
Publication statusPublished - Mar 1 2016

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • General Materials Science
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Applied Mathematics


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