Abstract
In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.
Highlights
As the damage tolerance concept is widely accepted and applied in the aerospace and aviation industries, it has become increasingly important to analyze how a fatigue crack grows
The artificial neural network (ANN) can offer a continuous predicting surface in predicting surface in the domain of definition based on the limited and discrete training data. This the domain of definition based on the limited and discrete training data. This example shows ANN’s example shows ANN’s advantage in fitting and extrapolating the crack growth rate under constant advantage in fitting and extrapolating the crack growth rate under constant amplitude loading with amplitude loading with different stress ratios
An applied overload can lead to fatigue crack growth retardation or even crack loadingAsinteraction effect
Summary
As the damage tolerance concept is widely accepted and applied in the aerospace and aviation industries, it has become increasingly important to analyze how a fatigue crack grows. The series of models discussed above are put forward to illustrate the nonlinear relationship between the crack growth rate and the SIF range for constant amplitude loadings. Most studies focus on accurately quantifying the nonlinear relationship between the crack growth rate and the driving parameters by using an explicit and simple function. To achieve these goals, many studies have been undertaken to introduce more parameters to construct a formula which can fit the experimental data better. The artificial neural network (ANN) has an excellent ability to fit the nonlinear multivariable relationship, which makes it a sophisticated and promising approach to the fatigue crack growth problem.
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