Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning
This work addresses the problem of unreliable confidence intervals in material property prediction for material scientists, representing an incremental improvement through hybrid modeling.
The study tackled the challenge of reliable uncertainty quantification in fatigue life prediction for metal alloys by integrating physics-based models with machine learning, resulting in enhanced consistency and improved uncertainty interval estimates.
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.