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

Xu Du, Yu-ting He, Chao Gao, Kai Liu, Teng Zhang and Sheng Zhang

Mathematical Problems in Engineering, 2017, vol. 2017, 1-12

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9409101

DOI: 10.1155/2017/9409101

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