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

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

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