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- W2808322913 abstract "Fatigue crack propagation in aircraft structures is a critical problem as life risk is involved besides financial loss. The relationship between stress intensity factor, ΔΚ and fatigue crack growth rate, da/dN is non-linear even in Paris region (region II). Analytical techniques are not much flexible to handle non-linearity. Accurate prediction of crack growth rate developed as a result of fatigue is important to evaluate fatigue life of engineering structures. Machine learning algorithms cater for non-linarites satisfactorily because of their excellent learning capability and flexible nature. This paper presents MLA based technique for prediction of crack growth rate using Radial Basis Function Neural Network (RBF-NN). The proposed technique is tested on different aluminum alloys used for aircraft structures. The minimum predicted MSE was achieved as 1.1315 × 10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>−9</sup> for 7055-T7511 Aluminum Alloy. The results show an excellent agreement to experimental data." @default.
- W2808322913 created "2018-06-21" @default.
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- W2808322913 date "2018-03-01" @default.
- W2808322913 modified "2023-10-06" @default.
- W2808322913 title "Prediction of fatigue crack growth rate in aircraft aluminum alloys using radial basis function neural network" @default.
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- W2808322913 doi "https://doi.org/10.1109/icaci.2018.8377568" @default.
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