Identification of material properties using nanoindentation and surrogate modeling

Hdl Handle:
http://hdl.handle.net/11285/621389
Title:
Identification of material properties using nanoindentation and surrogate modeling
Authors:
Li, Han; Gutierrez, Leonardo; Toda, Hiroyuki; Kuwazuru, Osamu; Liu, WenliHangai, Yoshihiko; Kobayashi, Masakazu; Batres Prieto, Rafael ( 0000-0002-8934-5632 )
Journal:
International Journal of Solids and Structures
Volume:
81
Issue:
1
Start Page:
151
End Page:
159
Issue Date:
2016
Publisher:
Elsevier
Department:
Tecnologico de Monterrey
Discipline:
Ingeniería y Ciencias Aplicadas / Engineering & Applied Sciences
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.

Keywords:
Nanoindentation; Material properties; Die cast aluminum alloy; Surrogate model; Finite-element analysis; Optimization; Neural networks; Infill sampling criteria; Differential evolution
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0020768315004849
Type:
Artículo / Article
Appears in Collections:
Artículos de Revistas

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Hanen
dc.contributor.authorGutierrez, Leonardoen
dc.contributor.authorToda, Hiroyukien
dc.contributor.authorKuwazuru, Osamuen
dc.contributor.authorLiu, WenliHangai, Yoshihikoen
dc.contributor.authorKobayashi, Masakazuen
dc.contributor.authorBatres Prieto, Rafaelen
dc.date.accessioned2017-03-21T17:43:46Z-
dc.date.available2017-03-21T17:43:46Z-
dc.date.issued2016-
dc.identifier.issn0020-7683-
dc.identifier.doihttp://dx.doi.org/10.1016/j.ijsolstr.2015.11.022-
dc.identifier.urihttp://hdl.handle.net/11285/621389-
dc.description.abstract<p style="text-align: justify;">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.</p>en
dc.language.isoengen
dc.publisherElsevieren
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0020768315004849en
dc.rightsRestricted Access-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleIdentification of material properties using nanoindentation and surrogate modelingen
dc.typeArtículo / Articleen
dc.identifier.journalInternational Journal of Solids and Structuresen
dc.subject.keywordNanoindentationen
dc.subject.keywordMaterial propertiesen
dc.subject.keywordDie cast aluminum alloyen
dc.subject.keywordSurrogate modelen
dc.subject.keywordFinite-element analysisen
dc.subject.keywordOptimizationen
dc.subject.keywordNeural networksen
dc.subject.keywordInfill sampling criteriaen
dc.subject.keywordDifferential evolutionen
dc.identifier.volume81en
dc.identifier.issue1en
dc.identifier.startpage151en
dc.identifier.endpage159en
dc.contributor.affiliationTecnologico de Monterreyen
dc.subject.disciplineIngeniería y Ciencias Aplicadas / Engineering & Applied Sciencesen
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