Amir Mahmoudi Amir.Mahmoudi@uwe.ac.uk
Senior Lecturer in Engineering Principles
A neural networks approach to characterize material properties using the spherical indentation test
Mahmoudi, A. H.; Nourbakhsh, S. H.
Authors
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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A neural networks approach to characterize material properties using the spherical indentation test
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http://creativecommons.org/licenses/by-nc-nd/3.0/
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http://creativecommons.org/licenses/by-nc-nd/3.0/
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