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    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Cecilia Xie
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    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
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IJCTE 2014 Vol.6(3): 234-239 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2014.V6.868

A Method for the Identification of Mechanical Properties Using Surrogate Models

Leonardo Gutierrez, Han Li, Hiroyuki Toda, Masakazu Kobayashi, Osamu Kuwazuru, and Rafael Batres

Abstract—Identification of material properties involves physical experimentation followed by modeling, simulation and manual optimization. However, the last step tends to be computational expensive. This paper investigates an artificial neural network (ANN) surrogate model for identifying material parameters. The proposed approach is illustrated with a case study based on a nano-indentation test.

Index Terms—Surrogate models, optimization, metal-mechanic properties, infill sampling, inverse analysis.

Gutierrez L., Li H. Kobayashi M., and Batres R. are with the Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi, Aichi 441-8580, Japan (e-mail: {Leonardo, lihan, m-kobayashi} @ise.me.tut.ac.jp).
Toda H. is with the Department of Mechanical Engineering, Kyushu University, Fukuoka 819-0395, Japan (e-mail: toda@mech.kyushu-u.ac.jp). Kuwazuru O. is with the Department of Nuclear Power & Energy Safety, University of Fukui, Fukui 910-8507, Japan (e-mail: kuwa@u-fukui.ac.jp).

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Cite:Leonardo Gutierrez, Han Li, Hiroyuki Toda, Masakazu Kobayashi, Osamu Kuwazuru, and Rafael Batres, "A Method for the Identification of Mechanical Properties Using Surrogate Models," International Journal of Computer Theory and Engineering vol. 6, no. 3, pp. 234-239, 2014.


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