Material characterization based on simulated spherical-Berkovich indentation tests

被引：7

作者：

Harsono, E. ^{[1
]}

Swaddiwudhipong, S. ^{[1
]}

Liu, Z. S. ^{[2
]}

机构：

[1] Natl Univ Singapore, Dept Civil Engn, Singapore 119260, Singapore

[2] Inst High Performance Comp, Singapore 138632, Singapore

来源：

SCRIPTA MATERIALIA | 2009年 / 60卷 / 11期

关键词：

Indentation; Finite element analysis; Artificial neural network; Material characterization; NANOINDENTATION TESTS; CONSTITUTIVE MODELS; SHARP INDENTATION; NEURAL-NETWORKS; INDENTERS;

D O I：

10.1016/j.scriptamat.2009.02.025

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

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. (C) 2009 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

引用

页码：972 / 975

页数：4

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