Abstract
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.