A New Nonlinear Fatigue Cumulative Damage Model Based on Enhanced Whale Optimization Algorithm and Manson–Halford Model

ABSTRACT In the field of modern mechanical engineering, structures often endure multi‐level variable stress loading. The nonlinear fatigue cumulative damage process of these structures is highly complex due to the significant influence of loading sequences and interactions, which makes fatigue life...

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Veröffentlicht in:Fatigue & fracture of engineering materials & structures Jg. 48; H. 8; S. 3528 - 3544
Hauptverfasser: Tang, Yuhan, Wang, Yuedong, Dong, Qi, Li, Yonghua, Guo, Tao, Zhang, Zhiyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Wiley Subscription Services, Inc 01.08.2025
Schlagworte:
Algorithms
Cumulative damage
Damage assessment
enhanced whale optimization algorithm
Fatigue failure
Fatigue life
fatigue life prediction
Life prediction
Manson–Halford model
Mechanical engineering
multi‐level variable stress loading
nonlinear fatigue cumulative damage
Optimization
Optimization algorithms
Undercarriages
ISSN:8756-758X, 1460-2695
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Zusammenfassung:ABSTRACT In the field of modern mechanical engineering, structures often endure multi‐level variable stress loading. The nonlinear fatigue cumulative damage process of these structures is highly complex due to the significant influence of loading sequences and interactions, which makes fatigue life prediction difficult. To accurately describe the impacts of these factors on fatigue damage, this paper proposes a nonlinear fatigue cumulative damage model (EWOA‐MH) based on the enhanced whale optimization algorithm (EWOA) and the Manson–Halford (M‐H) model. This model obtains weight factors through EWOA and incorporates them into the M‐H model. Verified by experimental data of multi‐level variable stress loading and calculated with a weighted method considering different materials' sample numbers, the prediction accuracy is increased by approximately 43%. Its application to the analysis of high‐speed train bogie frames effectively demonstrates the model's effectiveness. The research shows that the EWOA‐MH model performs outstandingly in fatigue life prediction and can effectively solve fatigue damage problems under multi‐level variable stress loading conditions.
Bibliographie:Funding
The authors are grateful for the financial supports by the Science and Technology Research and Development Programme Project of China National Railway Administration Group (Grant No: 2022YJ322).
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ISSN:8756-758X
1460-2695
DOI:10.1111/ffe.14689