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Open AccessArticle

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

1
Department of Mechanical Engineering, German Jordanian University, Amman 11180, Jordan
2
Department of Industrial Engineering, German Jordanian University, Amman 11180, Jordan
*
Author to whom correspondence should be addressed.
Materials 2020, 13(22), 5216; https://doi.org/10.3390/ma13225216
Received: 20 October 2020 / Revised: 12 November 2020 / Accepted: 16 November 2020 / Published: 18 November 2020
(This article belongs to the Section Materials Simulation and Design)
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. View Full-Text
Keywords: fracture; multiple site damage cracks; residual strength; aircraft fuselage panels; stiffened panels; lap-joint panels; artificial neural networks; ANN optimization fracture; multiple site damage cracks; residual strength; aircraft fuselage panels; stiffened panels; lap-joint panels; artificial neural networks; ANN optimization
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MDPI and ACS Style

Hijazi, A.; Al-Dahidi, S.; Altarazi, S. Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks. Materials 2020, 13, 5216. https://doi.org/10.3390/ma13225216

AMA Style

Hijazi A, Al-Dahidi S, Altarazi S. Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks. Materials. 2020; 13(22):5216. https://doi.org/10.3390/ma13225216

Chicago/Turabian Style

Hijazi, Ala; Al-Dahidi, Sameer; Altarazi, Safwan. 2020. "Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks" Materials 13, no. 22: 5216. https://doi.org/10.3390/ma13225216

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