Material characterization based on simulated spherical-Berkovich indentation tests

E. Harsono; S. Swaddiwudhipong; Z. S. Liu

doi:10.1016/j.scriptamat.2009.02.025

Material characterization based on simulated spherical-Berkovich indentation tests

E. Harsono
, S. Swaddiwudhipong^*
, Z. S. Liu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Material properties can be extracted from load-displacement indentation curves via appropriate reverse data analysis. This reverse analysis can, however, be conveniently carried out using neural networks. We propose an artificial neural network model to extract material properties based on a simulated spherical and Berkovich indentation database. The proposed model can predict accurately the elastoplastic properties of a new set of materials.

Original language	English
Pages (from-to)	972-975
Number of pages	4
Journal	Scripta Materialia
Volume	60
Issue number	11
DOIs	https://doi.org/10.1016/j.scriptamat.2009.02.025
State	Published - Jun 2009
Externally published	Yes

Keywords

Artificial neural network
Finite element analysis
Indentation
Material characterization

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10.1016/j.scriptamat.2009.02.025

Cite this

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title = "Material characterization based on simulated spherical-Berkovich indentation tests",

abstract = "Material properties can be extracted from load-displacement indentation curves via appropriate reverse data analysis. This reverse analysis can, however, be conveniently carried out using neural networks. We propose an artificial neural network model to extract material properties based on a simulated spherical and Berkovich indentation database. The proposed model can predict accurately the elastoplastic properties of a new set of materials.",

keywords = "Artificial neural network, Finite element analysis, Indentation, Material characterization",

author = "E. Harsono and S. Swaddiwudhipong and Liu, \{Z. S.\}",

year = "2009",

month = jun,

doi = "10.1016/j.scriptamat.2009.02.025",

language = "英语",

volume = "60",

pages = "972--975",

journal = "Scripta Materialia",

issn = "1359-6462",

publisher = "Acta Materialia Inc",

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TY - JOUR

T1 - Material characterization based on simulated spherical-Berkovich indentation tests

AU - Harsono, E.

AU - Swaddiwudhipong, S.

AU - Liu, Z. S.

PY - 2009/6

Y1 - 2009/6

N2 - Material properties can be extracted from load-displacement indentation curves via appropriate reverse data analysis. This reverse analysis can, however, be conveniently carried out using neural networks. We propose an artificial neural network model to extract material properties based on a simulated spherical and Berkovich indentation database. The proposed model can predict accurately the elastoplastic properties of a new set of materials.

AB - Material properties can be extracted from load-displacement indentation curves via appropriate reverse data analysis. This reverse analysis can, however, be conveniently carried out using neural networks. We propose an artificial neural network model to extract material properties based on a simulated spherical and Berkovich indentation database. The proposed model can predict accurately the elastoplastic properties of a new set of materials.

KW - Artificial neural network

KW - Finite element analysis

KW - Indentation

KW - Material characterization

UR - https://www.scopus.com/pages/publications/63149176701

U2 - 10.1016/j.scriptamat.2009.02.025

DO - 10.1016/j.scriptamat.2009.02.025

M3 - 文章

AN - SCOPUS:63149176701

SN - 1359-6462

VL - 60

SP - 972

EP - 975

JO - Scripta Materialia

JF - Scripta Materialia

IS - 11

ER -

Material characterization based on simulated spherical-Berkovich indentation tests

Abstract

Keywords

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