Frequency response similarity-based bolt clamping force prediction method using convolutional neural networks

Author:

Kim Do Hyeon,Han Jeong Sam

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Reference35 articles.

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2. H. Gong, J. Liu and H. Feng, Review on anti-loosening methods for threaded fasteners, Chinese Journal of Aeronautics, 35(2) (2022) 47–61.

3. J. H. Yoo, Technology trend of bolt locking methods, Current Industrial and Technological Trends in Aerospace, 16(1) (2018) 111–117.

4. H. M. Shin, D. H. Noh, S. W. Lee and K. J. Shin, Introduction of small load cell manufacturing method for measurement of bolt tension, Magazine and Journal of the Korean Society of Steel Construction, 32 (2020) 38–41.

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1. Deep learning approach for predicting crack initiation position and size in a steam turbine blade using frequency response and model order reduction;Journal of Mechanical Science and Technology;2024-04

2. A prediction model of bolted joint loosening based on deep learning network;Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science;2023-11-14

3. Convolutional Neural Network-based Prediction of Bolt Clamping Force in Initial Bolt Loosening State Using Frequency Response Similarity;Journal of the Computational Structural Engineering Institute of Korea;2023-08-31

4. A Study on the Generation and Utilization of the MS Similarity Map to Detect the Bolt Clamping Force in Bolted Structures;Transactions of the Korean Society of Mechanical Engineers - A;2023-07-31

5. Blade Edge Crack Prediction Using Model Order Reduction-based Frequency Response Analysis and Deep Learning;Transactions of the Korean Society of Mechanical Engineers - A;2022-11-30

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