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Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method

Authors :
Donato Perfetto
Alessandro De Luca
Marco Perfetto
Giuseppe Lamanna
Francesco Caputo
Source :
Materials, Vol 14, Iss 24, p 7602 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

Details

Language :
English
ISSN :
19961944
Volume :
14
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Materials
Publication Type :
Academic Journal
Accession number :
edsdoj.b817e7cc63c94f47b7abf1dc3a076156
Document Type :
article
Full Text :
https://doi.org/10.3390/ma14247602