2025/01/25 by Jiang Chang, Deekshith Basvoju, Chang, Jiang +7
Engineering · #FOS: Computer and information sciences #FOS: Physical sciences #Fatigue and fracture mechanics #I.2.8 #Machine Learning (cs.LG) #Materials Science (cond-mat.mtrl-sci) #Metallurgy and Material Forming #Non-Destructive Testing Techniques
paper · pdf · doi:10.48550/arxiv.2501.15057
openalex publication_date 2025/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.