Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machinability. The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading. However, this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy. Conventional predictive methods such as the Coffin-Manson equation require manual parameter adjustment for different conditions, thus limiting their applicability. Accordingly, a novel predictive model for low-cycle fatigue (LCF) life that combines machine learning (ML) with an energy-based physical model, referred to as the hybrid ML/E model, is proposed herein. The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life. The proposed approach addresses the inherent challenges of small fatigue datasets, hysteresis-loop perception, and algorithm selection. The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions, based on validation against conventional methods. The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.

中文翻译：

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基于机器学习和基于能量的建模的低周疲劳寿命替代预测方法


镁合金因其轻质特性和出色的机械加工性而在汽车和航空航天工业中极具价值。这些行业的应用需要准确预测循环载荷下的疲劳寿命。然而，由于许多锻造镁合金具有明显的塑性各向异性，这对许多锻造镁合金来说是一个挑战。传统的预测方法（如 Coffin-Manson 方程）需要针对不同的条件手动调整参数，从而限制了它们的适用性。因此，本文提出了一种新的低周疲劳 （LCF） 寿命预测模型，该模型将机器学习 （ML） 与基于能量的物理模型相结合，称为混合 ML/E 模型。混合 ML/E 模型利用对轧制 AZ31 Mg 合金的 LCF 测试生成的大量磁滞回数据集来有效预测疲劳寿命。所提出的方法解决了小型疲劳数据集、磁滞回环感知和算法选择的固有挑战。基于对传统方法的验证，混合 ML/E 模型在各种加载方向上表现出卓越的预测准确性和稳健性。ML 和物理原理的集成为各向异性材料的 LCF 寿命预测提供了一个统一的框架，代表了工业应用的重大进步。