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Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks
Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is use...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698861/ https://www.ncbi.nlm.nih.gov/pubmed/33218153 http://dx.doi.org/10.3390/ma13225216 |
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author | Hijazi, Ala Al-Dahidi, Sameer Altarazi, Safwan |
author_facet | Hijazi, Ala Al-Dahidi, Sameer Altarazi, Safwan |
author_sort | Hijazi, Ala |
collection | PubMed |
description | Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations. |
format | Online Article Text |
id | pubmed-7698861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76988612020-11-29 Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks Hijazi, Ala Al-Dahidi, Sameer Altarazi, Safwan Materials (Basel) Article Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations. MDPI 2020-11-18 /pmc/articles/PMC7698861/ /pubmed/33218153 http://dx.doi.org/10.3390/ma13225216 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hijazi, Ala Al-Dahidi, Sameer Altarazi, Safwan Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks |
title | Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks |
title_full | Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks |
title_fullStr | Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks |
title_full_unstemmed | Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks |
title_short | Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks |
title_sort | residual strength prediction of aluminum panels with multiple site damage using artificial neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698861/ https://www.ncbi.nlm.nih.gov/pubmed/33218153 http://dx.doi.org/10.3390/ma13225216 |
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