The Manson-Halford (M-H) nonlinear cumulative damage model is widely applied for fatigue life analysis problems under multi-level loading. In this model, the influence of loading sequence on the fatigue life can be better considerer, but the loading interaction effect is ignored. An improved whale optimization algorithm (IWOA) by integrating multiple strategies is proposed. The ability of global search and local exploitation is balanced and improved through nonlinear convergence factor, adaptive weighting factors and the Cauchy reverse learning strategies. In order to fully account for loading interaction effect, loading weighting factors are introduced to modify the M-H model, and the parameters are optimized through the global search properties of IWOA. The model is evaluated on multi-level loading fatigue experimental data from five metal materials and two aluminum alloy welded joints. The results suggest that the proposed IWOA has better optimization accuracy compared to the standard whale optimization algorithm (WOA). The proposed modified M-H model has better prediction performance compared to the four traditional cumulative damage models, which can be effectively applied to multi-level loading fatigue life analysis problems under actual working conditions. The proposed model is useful for the study of fatigue life evaluation methods.

中文翻译：

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基于改进WOA的修正Manson-Halford模型用于多级载荷下疲劳寿命预测


Manson-Halford (M-H) 非线性累积损伤模型广泛应用于多级载荷下的疲劳寿命分析问题。该模型可以更好地考虑加载顺序对疲劳寿命的影响，但忽略了加载的交互作用。提出了一种融合多种策略的改进鲸鱼优化算法（IWOA）。通过非线性收敛因子、自适应权重因子和柯西逆向学习策略来平衡和提高全局搜索和局部开发的能力。为了充分考虑载荷交互效应，引入载荷权重因子对M-H模型进行修正，并通过IWOA的全局搜索特性对参数进行优化。该模型根据五种金属材料和两种铝合金焊接接头的多级加载疲劳实验数据进行评估。结果表明，与标准鲸鱼优化算法（WOA）相比，所提出的 IWOA 具有更好的优化精度。与四种传统累积损伤模型相比，所提出的修正M-H模型具有更好的预测性能，可有效应用于实际工况下的多级加载疲劳寿命分析问题。该模型对于疲劳寿命评估方法的研究很有用。