Probabilistic analysis of bolted joint anti-loosening under cyclic transverse load

Abstract

The loosening of bolted joints under cyclic transverse load is a crucial failure mode, and the uncertainty of the relevant parameters of the bolted joint will affect the loosening state. However, there are few bolt-loosening probability analysis and evaluation methods based on parameter uncertainty in previous studies. In this paper, different loosening states are analyzed based on the relationship between pitch torque, thread friction torque, and bearing friction torque. Considering the uncertainty of process parameters, a probabilistic analysis method of the bolted joint anti-loosening under cyclic transverse load is proposed. In the proposed method, the finite element model verified by the fastening experiment is used to verify the accuracy of the bolted joints’ loosening criterion. Then, the loosening criterion is used as the Limit State Function (LSF). According to the LSF, an Adaptive Kriging-Kernel Density Importance Sampling (AK-KDIS) method is proposed for solving the anti-loosening reliability. This method combines adaptive Kriging model with the importance sampling method, effectively reducing the number of sampling samples and the number of LSF calls. The classical and robust Quasi-Monte Carlo (QMC) method is also used to evaluate the reliability of anti-loosening to verify the accuracy of the proposed AK-KDIS method. Finally, the practical application of probabilistic analysis of anti-loosening is presented, and the effectiveness of the proposed method is analyzed in detail. The results indicate that the AK-KDIS method has higher efficiency while ensuring calculation accuracy compared with the QMC method.