Contact stiffness attenuation model of bolted joint based on time-varying uncertainty

Niu, Nana; Zhao, Yaohua; Yang, Congbin; Wu, Hongchao; Li, Ying; Zhang, Tao

doi:10.1007/s00170-021-07957-9

Cited by 4 publications

(1 citation statement)

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“…The random process of the bolt clamping force changing in continuous time can be considered as the standard Wiener process. 27 The drift coefficient and fluctuation coefficient are used to describe the system changes over time and the uncertainty of the system, respectively. This not only ensures that the overall change of the bolt preload is monotonous, but also reflects the uncertainty of bolt clamping force decrease.…”

Section: Uncertainty Analysis Of Bolt Looseningmentioning

confidence: 99%

A prediction model of bolted joint loosening based on deep learning network

Liu,

Yan,

Chen

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The Bolted joint loosening is challenging to describe with an accurate mathematical model. Therefore, a prediction model of bolted joint loosening was proposed based on a deep learning network. Firstly, the loosening experiments of the bolted joint were carried out with an orthogonal test. And the nonlinear and uncertain characteristics of bolted joint loosening process were further analyzed. Furthermore, a prediction model of bolted joint loosening based on a deep learning network was established. The proposed model was trained and verified using the experimental data. The results showed that compared with the traditional mathematical and regression models, the model could not only obtain the change of the mean value of preload but also describe the confidence interval of the change of preload in the sense of probability synchronously. The experiment data and prediction data are in good agreement, which can be used to validate the rationality of the model.

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Section: Uncertainty Analysis Of Bolt Looseningmentioning

confidence: 99%