Fatigue lifetime predictions for variable amplitude loading are primarily based on the linear damage accumulation rule of Palmgren–Miner, which does not account for load sequence effects. Various nonlinear models have been developed, but their generalization capability is limited. Although neural network models have been used for prediction of the lifetime of metals subjected to constant amplitude loading, random loading and two-level block loading sequences before, they have never been used for multi-level block loading to the authors knowledge. The primary goal of this study is the development of neural network models for fatigue life estimation of metals subjected to block loading. To achieve this, a sufficient amount of qualitative data is required. Therefore a large number of rotating bending fatigue experiments with constant amplitude and block loading sequences are carried out. This new data is combined with data gathered from literature, leading to the most extensive open-access collection of variable amplitude fatigue data published to date. Neural network models are trained with the developed dataset and compared to four cumulative damage models including the linear Palmgren–Miner rule and three non-linear models. It is concluded that the neural network model for multi-level block loading outperforms all of the considered analytical models.

中文翻译：

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基于神经网络的块体载荷金属疲劳寿命预测

变幅载荷的疲劳寿命预测主要基于 Palmgren-Miner 的线性损伤累积规则，该规则不考虑载荷序列效应。人们已经开发了各种非线性模型，但它们的泛化能力有限。尽管神经网络模型之前已用于预测恒幅加载、随机加载和两级块加载序列下的金属寿命，但据作者所知，它们从未用于多级块加载。本研究的主要目标是开发神经网络模型，用于估计承受块载荷的金属的疲劳寿命。为了实现这一目标，需要足够数量的定性数据。因此，进行了大量等幅、分块加载序列的旋转弯曲疲劳实验。这些新数据与从文献中收集的数据相结合，形成了迄今为止发布的最广泛的开放获取的变幅疲劳数据集。神经网络模型使用开发的数据集进行训练，并与包括线性 Palmgren-Miner 规则和三个非线性模型在内的四种累积损伤模型进行比较。结论是，用于多级块加载的神经网络模型优于所有考虑的分析模型。