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Digital twin-driven fatigue life prediction framework of mechanical structures using a power density theory: Application to off-road vehicle front axle housing

文献类型： 外文期刊

作者： Wen, Chang-kai 1 ; Liu, Zhi-yong 3 ; Wu, Guang-wei 1 ; Zhao, Chun-jiang 1 ; Chen, Li-ping 1 ; Yin, Yan-xin 1 ; Meng, Zhi-jun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.State Key Lab Intelligent Agr Power Equipment, Beijing 100097, Peoples R China

3.Jiangsu Univ, Coll Agr Engn, Zhenjiang 212013, Peoples R China

关键词： Digital twin; Fatigue prediction; Failure location; Power density; Stress correction; Off-road vehicle components

期刊名称：MEASUREMENT （ 影响因子：5.6； 五年影响因子：5.0 ）

ISSN： 0263-2241

年卷期： 2023 年 220 卷

页码：

收录情况： SCI

摘要： Accurate fatigue life prediction is beneficial for fatigue maintenance of complex mechanical structures of off-road vehicles. For fatigue life prediction of mechanical structures subjected to non-stationary random loads during operation, this paper proposes a digital twin-driven fatigue life prediction method for mechanical structures. The core consists of improved fatigue theory, stress online measurement, and fatigue damage verification. In this study, a digital twin (DT) prediction framework containing multiple correction factors is established, in which the actual stress values in the physical world and the stress predictions of the estimated twin model can be obtained in real time. The remaining strength degradation of the structure and material can be compared to correct the prediction accuracy of fatigue analysis. In addition, based on the power density theory, combined with the short-time Fourier transform, a fatigue life analysis method that can comprehensively calculate the effect of load amplitude and frequency on fatigue is proposed. A test case has been demonstrated using a front axle housing of the off-road vehicle. The difference between the results predicted by the method in this paper and the actual fatigue results in failure time is only 2.65 h, with a relative error of only 3.95 %. In terms of failure location, the relative error is only 3.15 %.

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