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

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

Content Provider	MDPI
Author	Hijazi, Ala Al-Dahidi, Sameer Altarazi, Safwan
Copyright Year	2020
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.
Starting Page	5216
e-ISSN	19961944
DOI	10.3390/ma13225216
Journal	Materials
Issue Number	22
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2020-11-18
Access Restriction	Open
Subject Keyword	Materials Aerospace Engineering Fracture Multiple Site Damage Cracks Residual Strength Aircraft Fuselage Panels Stiffened Panels Lap-joint Panels Artificial Neural Networks Ann Optimization
Content Type	Text
Resource Type	Article

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