Notice of Retraction On-line model-based prognosis for crack growth under variable amplitude loading

Abstract

A probabilistic method is presented for model-based prognosis of crack growth under variable amplitude loading. Experiment is conducted to simulate a through-thickness crack growth of a panel that undergoes near constant as well as large variable amplitude loading during the cycles. Visual inspection is taken periodically to measure the crack length. In the experiment, several uncertainties are encountered due to the material and geometric variances, infrequent inspection and measurement noises. Bayesian approach is employed to account for this, which is to estimate the crack growth model parameters conditional on the provided measurements, and to predict the future growth in the probabilistic way. For numerical implementation, Particle Filter method is used to characterize the distribution in the form of random samples. The advantages of the method are: it favorably predicts the crack retardation and acceleration under the variable amplitude loading, and it captures the individual difference of each crack growth due to the variability even with the identical specimen and conditions. Several specimens are tested to demonstrate this. Two models with one being the classical Paris and the other the Huang model are employed to examine the importance of choosing proper physics model in the prognosis.