Machine learning-based fatigue life prediction of lamellar titanium alloys: A microstructural perspective

Abstract

Fatigue life prediction based on the traditional empirical formulation in large fatigue regimes is limited and prone to over-fitting. In this regard, we propose an integrated approach for fatigue life prediction of lamellar titanium alloys incorporating crystal plasticity simulations and a variety of machine learning methods from a microstructural perspective. Firstly, various defect shapes and distinct lamellar distributions are employed to characterize fatigue performance under different microstructural features. By utilizing fatigue indicator parameters (FIPs) to get an expanded dataset, this work employs algorithms including artificial neural networks (ANN), random forests regression (RFR), support vector regression (SVR), and particularly convolutional neural networks (CNN) to predict fatigue life. The results indicate that CNN outperforms the previously mentioned algorithms in prediction accuracy, due to its ability to directly extract microstructural features from images. Our proposed approach provides a microstructural perspective for fatigue life prediction of lamellar titanium alloys, broadening the way for life assessment of materials with diverse microstructures.