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Abstract <p>To mitigate the limitations associated with strength degradation in fatigue life prediction methodologies, a refined nonlinear cumulative damage model is proposed. This model constitutes an enhancement of the foundational Manson-Halford (M-H) theory by incorporating load interaction coefficients. These coefficients explicitly account for the complex interactions between successive loading cycles, a critical factor influencing damage evolution under variable amplitude loading. Validated under two-level loading conditions, the proposed model achieves superior predictive accuracy compared to the original M-H formulation: 80% of its predictions exhibit relative errors below 30%, significantly improving upon the M-H model’s 68% accuracy. The proposed model demonstrates greater conservatism, with 90% of predictions falling within a 1.5x lifetime factor and 98% within a 2x lifetime factor. This conservatism, arising from explicit consideration of load interactions and strength degradation, enhances design safety by mitigating premature failure risk while maintaining balanced error distributions to avoid excessive overdesign. Under multi-level loading spectra, the proposed model consistently yields lower relative prediction errors than its M-H model. Critically, the model maintains practical utility, requiring only standard fatigue test data for parameter determination and introducing no additional fitting parameters. Consequently, this enhanced nonlinear cumulative damage model offers a viable and improved engineering tool for predicting the fatigue life of metallic (steel and aluminum alloys) components under variable loading histories.</p>

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An Enhanced Manson-Halford Model Incorporating Load Interaction and Strength Degradation

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Zexiao Han,
  • Junting Zhang,
  • Yuanji Xu,
  • Dongxia Kou,
  • Chongbo Li,
  • Kaihui Ma

摘要

Abstract

To mitigate the limitations associated with strength degradation in fatigue life prediction methodologies, a refined nonlinear cumulative damage model is proposed. This model constitutes an enhancement of the foundational Manson-Halford (M-H) theory by incorporating load interaction coefficients. These coefficients explicitly account for the complex interactions between successive loading cycles, a critical factor influencing damage evolution under variable amplitude loading. Validated under two-level loading conditions, the proposed model achieves superior predictive accuracy compared to the original M-H formulation: 80% of its predictions exhibit relative errors below 30%, significantly improving upon the M-H model’s 68% accuracy. The proposed model demonstrates greater conservatism, with 90% of predictions falling within a 1.5x lifetime factor and 98% within a 2x lifetime factor. This conservatism, arising from explicit consideration of load interactions and strength degradation, enhances design safety by mitigating premature failure risk while maintaining balanced error distributions to avoid excessive overdesign. Under multi-level loading spectra, the proposed model consistently yields lower relative prediction errors than its M-H model. Critically, the model maintains practical utility, requiring only standard fatigue test data for parameter determination and introducing no additional fitting parameters. Consequently, this enhanced nonlinear cumulative damage model offers a viable and improved engineering tool for predicting the fatigue life of metallic (steel and aluminum alloys) components under variable loading histories.