The effect of tightening again on bolt loosening under transverse load: Experimental and finite element analysis

Liu, Zhifeng; Yan, Xiaohong; Zheng, Mingpo; Wang, Yuezhen; Chen, Wentao; Li, Ying

doi:10.1016/j.istruc.2022.08.049

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…where, D is standard deviation. Equations ( 12) and ( 13) can be substituted into equation (8) to obtain the standard deviation of ln F t ð Þ:…”

Section: ) Constructing a Time-varying Uncertainty Analysis Modelmentioning

confidence: 99%

“…Liu et al 6,7 pointed out that the early loosening under axial load was caused by plastic deformation of the thread root, and the main reason for the later loosening was fretting wear between the thread surfaces through experiments and numerical studies. An experimental investigation on bolt self-loosening under axial and transverse loads was performed by Liu et al 8 and Li et al 9 The findings demonstrated that the relaxation development of bolted joints subjected to different vibration conditions was identical and could be separated into two stages. Cheng et al 10 and Cao and Zhang 11 studied the energy dissipation and stress distribution of the contact interface of the bolted joint structure through theoretical and numerical simulation methods, which provided a theoretical basis for reducing bolt loosening.…”

Section: Introductionmentioning

confidence: 99%

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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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“…where, D is standard deviation. Equations ( 12) and ( 13) can be substituted into equation (8) to obtain the standard deviation of ln F t ð Þ:…”

Section: ) Constructing a Time-varying Uncertainty Analysis Modelmentioning

confidence: 99%

Section: Introductionmentioning

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:

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to use hexahedral finite elements for the threads, in order to avoid potential convergence problems. The commonly used construction method for threaded hexahedrons is mainly based on the slicing method [10][11][12][13] , but manually meshing is extremely tedious.…”

Section: Introductionmentioning

confidence: 99%

High-fidelity Modelling Approach for Investigating the Tightening Torque Effect in Thread Connection

Zhang¹,

Yu²,

Wang³

2023

J. Phys.: Conf. Ser.

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Accurate replication of the torque-tightening effect is important when simulating the mechanical behavior of composite joints using the finite element (FE) approach. In this study, a parametric mesh construction strategy was developed to automatically build a high-fidelity 3D FE model with lift angle to simulate bolt thread interactions. An experimental approach was proposed to calibrate the FE model. The relation between tightening torque and the axial force was studied by the FE method, experiment, and engineering algorithm. Results showed that traditional empirical formula overestimate the clamping force. By using the proposed model to calculate the axial force of the bolt, the accuracy of the axial force can be significantly improved when analyzing the composite bolted joints. Finally, parametric simulations were performed to investigate the influence of coefficients of friction at thread interfaces and the nut/washer interfaces. The pre-tightening force was significantly influenced by the frictional coefficients at these interfaces. Results indicated that increasing the coefficient of friction (CoF) at the nut/washer interface from 0.1 to 0.2 can lead up to 28% reduction in the clamping force, while this increase was 23% for the threads interfaces.

show abstract

Dynamic characteristics of a rod-fastening rotor system with tie rod bolt loosening

Yang,

Zhou,

et al. 2023

Nonlinear Dyn

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The effect of tightening again on bolt loosening under transverse load: Experimental and finite element analysis

Cited by 10 publications

References 21 publications

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

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

High-fidelity Modelling Approach for Investigating the Tightening Torque Effect in Thread Connection

Dynamic characteristics of a rod-fastening rotor system with tie rod bolt loosening

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