Please use this identifier to cite or link to this item:
Title: Improved algorithm for material characterization by simulated indentation tests
Authors: Swaddiwudhipong, S. 
Hua, J. 
Harsono, E.
Liu, Z.S.
Ooi, N.S.B.
Issue Date: 1-Dec-2006
Citation: Swaddiwudhipong, S., Hua, J., Harsono, E., Liu, Z.S., Ooi, N.S.B. (2006-12-01). Improved algorithm for material characterization by simulated indentation tests. Modelling and Simulation in Materials Science and Engineering 14 (8) : 1347-1362. ScholarBank@NUS Repository.
Abstract: The paper involves the establishment of a neural network model with improved algorithm for reverse analysis of simulated indentation tests considering the effects of friction on the contact surfaces. Extensive finite element analyses covering a wide practical range of materials obeying power law strain-hardening have been carried out to simulate the indentation tests. The results obtained from the simulated dual indentations using conical indenters with different geometries considering effects of friction are adopted in the training and verification of the least squares support vector machines involving structural risk optimization. The characteristics and performances of the neural network model for this class of problems are given and deliberated. The tuned networks are able to predict accurately the mechanical properties of a new set of materials. The approach has great potential for the applications on the characterization of a small volume of materials in micro-and nano-electromechanical systems (MEMS & NEMS). © 2006 IOP Publishing Ltd.
Source Title: Modelling and Simulation in Materials Science and Engineering
ISSN: 09650393
DOI: 10.1088/0965-0393/14/8/005
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Apr 22, 2021


checked on Apr 22, 2021

Page view(s)

checked on Apr 13, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.