A Low-Cycle Fatigue Life Prediction Method for a Drive Shaft Considering the Effects of Loading and Strength Degradation

The low-cycle fatigue failure of drive shafts under complex service conditions constitutes a critical issue that undermines the structural integrity and service safety of the transmission system in special vehicles. To improve the prediction accuracy of the low-cycle fatigue life of drive shafts, a low-cycle fatigue life prediction method for the drive shaft that accounts for load effects and strength degradation is proposed. A fatigue life prediction model that accounts for the mean stress effect and fatigue strength degradation is proposed by introducing dynamically degrading fatigue strength into the mean stress-refined SWT (Smith–Watson–Topper) model. A fatigue cumulative damage model that considers load interactions and fatigue strength degradation is also proposed, in which the load ratio is introduced to quantitatively describe the extent of the influence of load interactions on the damage process. Furthermore, the dynamically degrading fatigue strength is incorporated into the M-H (Manson–Halford) model. Finally, the stress–strain responses at the critical locations of the drive shaft are analyzed using the finite element model, and the fatigue life of the drive shaft under the load spectrum is calculated using the improved fatigue life prediction model and the improved fatigue cumulative damage model. The results indicate that the improved life prediction method, which considers load effects and strength degradation, can effectively enhance the accuracy of fatigue life prediction for the drive shaft.

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Publication Details

Journal
Materials
Published
2026-05-21
DOI
https://doi.org/10.3390/ma19102164
Primary Topic
Mechanical Failure Analysis and Simulation
Type
article
Field-Weighted Citation Impact
0.00

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article

A Low-Cycle Fatigue Life Prediction Method for a Drive Shaft Considering the Effects of Loading and Strength Degradation

L Wang, Jinle Zhang, Feng Liu, Ximing Zhang et al.
Materials
Mechanical Failure Analysis and Simulation
article

A Low-Cycle Fatigue Life Prediction Method for a Drive Shaft Considering the Effects of Loading and Strength Degradation

L Wang, Jinle Zhang, Feng Liu, Ximing Zhang, Jing Zhang, Li Yang, Xingsheng Yu
article en

Abstract

The low-cycle fatigue failure of drive shafts under complex service conditions constitutes a critical issue that undermines the structural integrity and service safety of the transmission system in special vehicles. To improve the prediction accuracy of the low-cycle fatigue life of drive shafts, a low-cycle fatigue life prediction method for the drive shaft that accounts for load effects and strength degradation is proposed. A fatigue life prediction model that accounts for the mean stress effect and fatigue strength degradation is proposed by introducing dynamically degrading fatigue strength into the mean stress-refined SWT (Smith–Watson–Topper) model. A fatigue cumulative damage model that considers load interactions and fatigue strength degradation is also proposed, in which the load ratio is introduced to quantitatively describe the extent of the influence of load interactions on the damage process. Furthermore, the dynamically degrading fatigue strength is incorporated into the M-H (Manson–Halford) model. Finally, the stress–strain responses at the critical locations of the drive shaft are analyzed using the finite element model, and the fatigue life of the drive shaft under the load spectrum is calculated using the improved fatigue life prediction model and the improved fatigue cumulative damage model. The results indicate that the improved life prediction method, which considers load effects and strength degradation, can effectively enhance the accuracy of fatigue life prediction for the drive shaft.

MaterialsVol. 19(10)
Beijing Agricultural Machinery Research Institute (CN), Beijing Information Science & Technology University (CN)
China National Petroleum Corporation
Responsible consumption and production
Openalex Percentile: Top 10%
Mechanical Failure Analysis and Simulation
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