A Neural Networks approach to characterize material properties using the spherical indentation test

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 p...

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Bibliographic Details
Main Authors: Mahmoudi, A.H., Nourbakhsh, S.H.
Format: Conference Proceeding
Language:English
Subjects:
Material characteristics
Neural Networks
Sherical indentation
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Summary: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.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2011.04.507