Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine Learning

TL;DR

This paper presents a novel machine learning approach that combines physics-based models and data-driven techniques to improve fatigue life prediction and uncertainty quantification in metal alloys.

Contribution

It introduces a physics-informed neural network framework that integrates fatigue models and experimental data for more reliable predictions.

Findings

Enhanced prediction accuracy for fatigue life of metal alloys.

Improved uncertainty interval estimation through physics-informed loss functions.

Validation on Titanium and Carbon steel alloys datasets confirms effectiveness.

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.