MCMC‐Based Fatigue Crack Growth Prediction on 2024‐T6 Aluminum Alloy

Abstract

This work aims to make the crack growth prediction on 2024-T6 aluminum alloy by using Markov chain Monte Carlo (MCMC). The fatigue crack growth test is conducted on the 2024-T62 aluminum alloy standard specimens, and the scatter of fatigue crack growth behavior was analyzed by using experimental data based on mathematical statistics. An empirical analytical solution of Paris' crack growth model was introduced to describe the crack growth behavior of 2024-T62 aluminum alloy. The crack growth test results were set as prior information, and prior distributions of model parameters were obtained by MCMC using OpenBUGS package. In the additional crack growth test, the first test point data was regarded as experimental data and the posterior distribution of model parameters was obtained based on prior distributions combined with experimental data by using the Bayesian updating. At last, the veracity and superiority of the proposed method were verified by additional crack growth test.

An Effective Bayesian Method for Probability Fatigue Crack Propagation Modeling through Test Data

“…The feasibility of using (2) to describe the fatigue crack growth behavior about the metal material has been validated in Michale S. Hamada's work [13,16], which will be validated and analyzed in the following work of this study. This equation about a( ) is a semiempirical crack growth fitting model, which has no physical meaning.…”