DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction

Fatigue life characterizes the duration a material can function before failure under specific environmental conditions, and is traditionally assessed using stress-life (SN) curves. While machine learning and deep learning offer promising results for fatigue life prediction, they face the overfitting challenge because of the small size of fatigue experimental data in specific materials. To address this challenge, we propose, DeepOFormer, by formulating S-N curve prediction as an operator learning problem. DeepOFormer improves the deep operator learning framework with a Transformer-based encoder and a mean L2 relative error loss function. We also consider Stüssi, Weibull, and Pascual and Meeker (PM) features as domain-informed features. These features are motivated by empirical fatigue models. To evaluate the performance of our DeepOFormer, we compare it with different deep learning models and XGBoost on a dataset with 54 SN curves of aluminum alloys. With seven different aluminum alloys selected for testing, our DeepOFormer achieves an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9515, a mean absolute error of 0.2080, and a mean relative error of 0.5077, significantly outperforming state-of-the-art deep/machine learning methods, including DeepONet, TabTransformer, and XGBoost, etc. The results highlight that our DeepOFormer, integrating with domain-informed features, substantially improves prediction accuracy and generalization capabilities for fatigue life prediction in aluminum alloys.