Probabilistic models employed for life prediction in the bearing industry (Lundberg‐Palmgren [LP], Ioannides‐Harris [IH], and Zatersky models) utilize stresses calculated based on Hertzian assumptions that ignore plastic deformation occurring under high radial loads. However, it is known that subsurface plasticity produces significant deviations from elastic calculations in contact dimensions, pressure, and stresses. In this work, we show that accounting for plastic deformation leads to significantly more accurate life predictions than the general practice of utilizing elastic stress fields. Conventional probabilistic models are also restricted to utilizing stress measures at the macroscopic scale, while the onset of damage under rolling contact fatigue (RCF) manifests on a much smaller scale. By utilizing Dang Van criterion (DVC)—a multiscale high cycle fatigue criterion—in a probabilistic framework to account for stresses at multiple length scales, we propose a formulation that is second in accuracy only to the Zaretsky model, thus outperforming LP and IH models. Thus, we improve the accuracy of conventional probabilistic models by accounting for plastic deformation, while also presenting a new microstructure‐sensitive probabilistic formulation that betters their predictions.

中文翻译：

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在滚动接触疲劳下的重载接触寿命预测中考虑微结构敏感性和可塑性

轴承行业中用于寿命预测的概率模型（Lundberg-Palmgren [LP]，Ioannides-Harris [IH]和Zatersky模型）利用基于Hertzian假设计算的应力，该应力忽略了在高径向载荷下发生的塑性变形。但是，众所周知，地下可塑性会在接触尺寸，压力和应力方面与弹性计算产生显着偏差。在这项工作中，我们表明，考虑塑性变形会比使用弹性应力场的一般做法导致更准确的寿命预测。常规的概率模型也仅限于在宏观尺度上使用应力测量，而滚动接触疲劳（RCF）下的损伤发作则要小得多。通过在概率框架中利用多尺度高周疲劳准则Dang Van准则（DVC）来解决多个长度尺度上的应力，我们提出了精度仅次于Zaretsky模型的公式，因此优于LP和IH模型。因此，我们通过考虑塑性变形来提高传统概率模型的准确性，同时还提出了一种新的对微观结构敏感的概率公式，该公式可以更好地预测它们。