Prediction Of Fatigue Crack Growth Rate In Aircraft Aluminum Alloys Using Radial Basis Function Neural Network

Fatigue crack propagation in aircraft structures is a critical problem as life risk is involved besides financial loss. The relationship between stress intensity factor, Delta K 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 x 10(-9)for 7055-T7511 Aluminum Alloy. The results show an excellent agreement to experimental data.