Prediction of fatigue crack propagation based on dynamic Bayesian network

Abstract

To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper. The Paris Law of crack propagation and the extended finite element method (XFEM) are combined to establish the state equation of crack propagation. The uncertain factors of crack propagation are analyzed, and the prediction model of fatigue crack propagation based on the dynamic Bayesian network is constructed. A Bayesian inference algorithm based on the combination of Gaussian particle filter and firefly algorithm is proposed. The fatigue experiment of the specimen with the pre-cracks is carried out to test the correlation between the fatigue load cycles and the crack propagation depth. The experimental results show that the crack propagation prediction method proposed in this paper can effectively improve the prediction accuracy of crack propagation depth.

XFEM for Composites, Biological, and Bioinspired Materials: A Review

“…In this direction, Bayesian networks can be helpful by introducing probability distributions of specific variables, such as material elastic modulus and yield strength, porosity distribution, or flaw orientation, thus creating a better understanding of the material mechanical behavior. XFEM models with Bayesian networks have been recently proposed [140], but generally lead to overly simplified structures. With the growth of the XFEM's popularity, a combined dataset of published results can be used as input for data-driven models (i.e., meta-analysis).…”