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- W2017351364 abstract "An artificial neural network (NN) method is developed to represent the fatigue-crack-growth and cycle relationships under spectrum loadings of the Mirage aircraft operated by the Royal Australian Air Force. This method utilizes load cycle spectrum using available flight and experimental data for crack growth vs cycles as input. The trained network is able to predict the relationship between the crack-growth and the loading cycles. The neural network is able to predict the crack-growth cycle behavior for different variations in the original loading spectrums. The results predicted by the NN model seem reasonable and the model is capable of representing crack-growth behavior for various arbitrary aircraft spectrum loadings with certain limitations. In addition, an attempt is made to predict the material parameters for Walker's fatigue-crack-growth relationship using a different neural network. Because of the demonstrated performance, it is possible that the proposed NN approach can be extended with more research effort to estimate the fatigue life of arbitrary cracked structural components under complex loadings in real time." @default.
- W2017351364 created "2016-06-24" @default.
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- W2017351364 date "1995-07-01" @default.
- W2017351364 modified "2023-09-26" @default.
- W2017351364 title "Neural network approach to fatigue-crack-growth predictions under aircraft spectrum loadings" @default.
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- W2017351364 doi "https://doi.org/10.2514/3.46797" @default.
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