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螺栓松动寿命预测:一种考虑变幅载荷和多螺栓结构的实用方法

Prediction of Bolt Loosening Life: A Practical Approach Considering Variable Amplitude Loading and Multi-Bolted Structures.

作者信息

Yang Min, Jeong Seong-Mo, Hong Seong-Gu, Lim Jae-Yong

机构信息

Department of Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Convergence Research Center for Meta-Touch, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea.

出版信息

Materials (Basel). 2025 Feb 27;18(5):1069. doi: 10.3390/ma18051069.

Abstract

Bolted connections are crucial in joining mechanical assemblies and ensuring the integrity and reliability of structural components. This study proposes a method for estimating bolt loosening life by practically applying material fatigue life prediction methods. First, the study employed the linear cumulative damage rule to predict the loosening life of single bolts under two-block loading conditions. Second, a test device with two bolt attachment points on a single structure was fabricated to model the multi-bolted structure, and tests were conducted. Finite element analysis (FEA) analysis was employed to identify vulnerable points. The loosening life of single bolts predicted using the linear cumulative damage rule exhibited enhanced accuracy within a ±1.2× error band compared with the experimental data despite variations in bolt types and test conditions. The FEA results for the multi-bolt structure demonstrated that the loosening life could be predicted by identifying vulnerable points and estimating the displacements. This study effectively predicts the bolt loosening life, offering valuable data for the reliability assessment of bolted structures.

摘要

螺栓连接对于机械组件的连接以及确保结构部件的完整性和可靠性至关重要。本研究提出了一种通过实际应用材料疲劳寿命预测方法来估算螺栓松动寿命的方法。首先,该研究采用线性累积损伤法则来预测单螺栓在双块加载条件下的松动寿命。其次,制造了一个在单个结构上有两个螺栓连接点的测试装置来模拟多螺栓结构,并进行了测试。采用有限元分析(FEA)来识别薄弱点。尽管螺栓类型和测试条件存在差异,但使用线性累积损伤法则预测的单螺栓松动寿命与实验数据相比,在±1.2倍误差范围内具有更高的准确性。多螺栓结构的有限元分析结果表明,通过识别薄弱点并估算位移,可以预测松动寿命。本研究有效地预测了螺栓松动寿命,为螺栓连接结构的可靠性评估提供了有价值的数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c35e/11901137/153c8e4cfe28/materials-18-01069-g001.jpg

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