Prediction of fatigue crack propagation based on dynamic Bayesian network

Advances in Mechanical Engineering

Research Article

Mengzhen Li¹  Weikai Liu¹  Wei Wang¹  Zhiping Liu²  Yanfang Yang² 

[1] School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China;School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China;Engineering Research Center of Port Logistic Technology and Equipment, Ministry of Education, Wuhan, China;

关键词: Fatigue crack propagation;    dynamic Bayesian network;    Gaussian particle filter;    firefly algorithm;    the extended finite element method;   

DOI  :  10.1177/16878132221136413

 received in 2022-04-20, accepted in 2022-10-14,  发布年份 2022

来源: Sage Journals

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2022

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