融合物理模型与机器学习，提升金属疲劳寿命预测的准确性与可信度。
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
- 用物理模型生成输入特征，增强实验数据
- 引入约束损失函数，提升预测一致性
- 适合材料性能预测与可靠性评估的研究者
机器学习在材料性能预测中展现出巨大潜力，但预测值的置信区间可靠估计仍具挑战。本研究提出一种结合物理模型与机器学习的疲劳寿命不确定性量化新方法。通过基斯昆（Basquin）疲劳模型生成物理特征，补充实验疲劳寿命数据；并设计物理信息损失函数，对神经网络施加疲劳寿命边界约束。在钛合金和碳钢合金的疲劳试验数据集上验证表明，该方法显著提升了预测一致性与不确定性区间的准确性。
原文摘要 · Abstract (English)
Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted values remains a challenge, due to the inherent complexities in material modeling. This study introduces a novel approach for uncertainty quantification in fatigue life prediction of metal materials based on integrating knowledge from physics-based fatigue life models and machine learning models. The proposed approach employs physics-based input features estimated using the Basquin fatigue model to augment the experimentally collected data of fatigue life. Furthermore, a physics-informed loss function that enforces boundary constraints for the estimated fatigue life of considered materials is introduced for the neural network models. Experimental validation on datasets comprising collected data from fatigue life tests for Titanium alloys and Carbon steel alloys demonstrates the effectiveness of the proposed approach. The synergy between physics-based models and data-driven models enhances the consistency in predicted values and improves uncertainty interval estimates.
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