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A neural networks approach to characterize material properties using the spherical indentation test

Mahmoudi, A. H.; Nourbakhsh, S. H.

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Authors

Amir Mahmoudi Amir.Mahmoudi@uwe.ac.uk
Senior Lecturer in Engineering Principles

S. H. Nourbakhsh



Abstract

Determination of material characteristics using the instrumented indentation test has gained interests among many researchers. The output of a spherical indentation test is usually the load-penetration (P-h) curve. To achieve this goal, the elastic deformation of sphere must be eliminated from the penetration. To determine three parameters of the LUDWIG's equation which are σy, K and m, choice of a prompt numerical procedure is of essences.
The purpose of the present work is to determination three parameters of the LUDWIG's equation using the spherical indentation test and Neural Networks. Therefore, a Neural Networks is trained following the spherical indentation test using two parameters that are obtained from the P-h curve. The output of the networks is the three parameters of the LUDWIG's equation. The results were then compared with the finite element predictions and verified using the experimental data. A good agreement was observed. Finally, the weights of Neural Networks layer were extracted for easy use of the above procedure.

Journal Article Type Article
Online Publication Date Jun 10, 2011
Publication Date 2011
Deposit Date Oct 21, 2024
Publicly Available Date Oct 21, 2024
Journal Procedia Engineering
Print ISSN 1877-7058
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 10
Pages 3062-3067
DOI https://doi.org/10.1016/j.proeng.2011.04.507
Public URL https://uwe-repository.worktribe.com/output/13305587

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