Abstract

A variety of fatigue crack growth models have been developed during the last three decades aiming at capturing the effect of variable amplitude loading. However, all of them require using as the base a set of experimental fatigue crack growth data obtained under constant amplitude loading which can be highly inaccurate especially in the ‘near threshold’ region. The main purpose of the paper is to illustrate how the scatter of the input data (material constants) can affect the prediction of fatigue lives. Constant amplitude fatigue crack growth parameters were considered to be random variables with statistical distributions determined from the experimental data. The UniGrow fatigue crack growth model based on the local crack tip stress/strain material behaviour was combined with the Monte- Carlo method in order to obtain the distribution of the final fatigue life based on the probability distributions of the input material parameters. A large set of experimental fatigue crack growth data for an aluminum alloy (7075-T6) was used for the verification of this methodology. The fatigue crack growth analysis was carried out for central through crack specimens. The simulated fatigue life distributions enable to determine the fatigue life corresponding to a given probability with a given confidence level. Comparison between theoretical and experimental results confirms the ability of the UniGrow fatigue crack growth model to simulate the load-interaction effect and shows the advantages of probabilistic approach for the fatigue crack growth analysis.

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