Strength of 7075-T6 and 2024-T3 Aluminum Panels with Multiple-Site Damage

Smith, Bert L.; Hijazi, Ala; Myose, Roy Y.

doi:10.2514/2.2933

Cited by 4 publications

(18 citation statements)

References 2 publications

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“…When A-Basis yields strengths are used in Eqs. (5) and 7, the ratio is denoted as R a , and when B-Basis yield strengths are used the ratio is denoted by R b . These ratios are given in Tables 6 and 7 for each panel and shown graphically in Figs.…”

Section: Resultsmentioning

confidence: 99%

“…The same empirical approach was used to develop the modified link-up model for 2524-T3 as was used by Smith et al 2 to develop the modified linkup model for 2024-T3 given in Eq. (5). Because the ligament length L strongly influences the link-up strength σ LU , the data were displayed as shown in Fig.…”

Section: Modified Link-up Model For 2524-t3mentioning

confidence: 99%

“…4 These models were developed for use with either A-Basis or B-Basis yield strengths. In addition, Smith et al 5 developed a modified link-up model as well as a correction to a brittle fracture model for 7075-T6 aluminum.…”

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confidence: 99%

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Link-Up Strength of 2524-T3 and 2024-T3 Aluminum Panels with Multiple Site Damage

Smith

Hijazi

2005

Journal of Aircraft

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The aluminum alloy 2524-T3 is replacing 2024-T3 for many applications because 2524-T3 has a greater fracture toughness while retaining the same strength. Much attention has been given to the effect of multiple site damage on 2024-T3 aluminum; however, very little has been reported about 2524-T3. Twenty-two panels of 2524-T3, each with a different crack configuration, were tested for critical (linkup) strength, and the results were compared with an identical set of previously tested 2024-T3 panels with MSD. The panels were 24 in. wide with a midspan row of 0.25-in.-diam holes at 1-in. pitch. Each panel had a central lead crack with collinear MSD cracks emerging from both sides of the adjacent holes. A comparison of the results showed the 2524-T3 panels to average approximately 27% greater strength than the 2024-T3 panels. The linkup or plastic-zone-touch model used to predict the critical (link-up) strength of the panels was found to be highly conservative. Consequently the test data were used for a semi-empirical analysis to develop a modified link-up model for 2524-T3, similar to the one previously developed for 2024-T3. The average error between the critical strengths from testing and those predicted by the link-up model was approximately 20%, whereas that for the modified link-up model was approximately 3%.

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Section: Resultsmentioning

confidence: 99%

Section: Modified Link-up Model For 2524-t3mentioning

confidence: 99%

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Link-Up Strength of 2524-T3 and 2024-T3 Aluminum Panels with Multiple Site Damage

Smith

Hijazi

2005

Journal of Aircraft

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“…Another theory that can be used for predicting the residual strength is the Net Section Yielding (NSY), and it is more applicable to ductile materials. However, experiments have shown that neither of the LEFM nor NSY theories is able to accurately predict the residual strength of panels with MSD for neither ductile nor brittle materials [13]. An analytical model that is especially formulated for the prediction of residual strength of panels with MSD was introduced by Swift [1].…”

Section: Residual Strength Of Panels With Msdmentioning

confidence: 99%

“…Experimental data for the unstiffened (Clad) 2524-T3 panel configurations[14]. Experimental data for the unstiffened (Bare) 7075-T6 panel configurations[13].…”

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confidence: 99%

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

2020

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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.

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A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

2020

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An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (<3% average error). The relative accuracy improvement is higher (up to 46%) for ANN learning algorithms that give lower prediction accuracy. Also, the relative accuracy improvement becomes more significant (up to 38%) for smaller dataset sizes.

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Strength of 7075-T6 and 2024-T3 Aluminum Panels with Multiple-Site Damage

Cited by 4 publications

References 2 publications

Link-Up Strength of 2524-T3 and 2024-T3 Aluminum Panels with Multiple Site Damage

Link-Up Strength of 2524-T3 and 2024-T3 Aluminum Panels with Multiple Site Damage

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

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

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