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Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method
- 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.
- Subjects :
- Artificial Neural Network (ANN)
guided waves
Structural Health Monitoring (SHM)
Finite Element Analysis (FEA)
damage detection
metals
Technology
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Engineering (General). Civil engineering (General)
TA1-2040
Microscopy
QH201-278.5
Descriptive and experimental mechanics
QC120-168.85
Subjects
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