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A prior knowledge-guided predictive framework for LCF life and its implementation in shaft-like components under multiaxial loading

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  • Li, Butong
  • Zhu, Junjie
  • Zhao, Xufeng

Abstract

Predicting low-cycle fatigue (LCF) life under complex loading conditions has long been a challenge. Reliable fatigue life prediction is crucial for the fatigue reliability assessment of industrial components. This paper proposes a prior knowledge-guided framework for predicting LCF life under multiaxial loading conditions. The framework integrates physical knowledge and partial known relationships. Inspired by concepts from reliability-based design optimization (RBDO), the framework is capable of predicting deterministic and probabilistic LCF life with greater efficiency and accuracy. Building on this, we discuss the LCF life degradation of components under survival rates by introducing the multiaxial fatigue parameter. The parameter can effectively describe the trends of LCF life under elliptical multiaxial loading paths. Furthermore, the hazard rate and fatigue reliability of structural components is investigated. The research presented in this paper can offer valuable guidance for applications in practical industrial contexts.

Suggested Citation

  • Li, Butong & Zhu, Junjie & Zhao, Xufeng, 2025. "A prior knowledge-guided predictive framework for LCF life and its implementation in shaft-like components under multiaxial loading," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002455
    DOI: 10.1016/j.ress.2025.111044
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    References listed on IDEAS

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