Comparative analysis of ensemble learning techniques for enhanced fatigue life prediction

Efficient prediction of fatigue life in structural components is crucial for ensuring their integrity and reliability, especially considering the dominant occurrence of fatigue failure in metallic structures within the industrial sectors. Conventional fatigue assessment methods, although theoretically established, are often time-consuming and exhibit limitations due to the intricate nature of the fatigue mechanism. Machine learning models have demonstrated significant potential for enhancing the efficiency of predictions in fatigue life. This research explores the effectiveness of ensemble learning models-boosting, stacking, and bagging-compared to linear regression and K-Nearest Neighbors as benchmarks. Fatigue life prediction is conducted across different notched scenarios using Incremental Energy Release Rate (IERR) measures in addition to the more standard stress/strain field measures. To assess the performance of the proposed models, a comprehensive set of evaluation metrics was performed, including mean square error (MSE), mean squared logarithmic error (MSLE), symmetric mean absolute percentage (SMAPE), and Tweedie score. The findings reveal that ensemble learning models, particularly the ensemble neural networks, stands out as a superior approach for fatigue life cycle assessment compared to other methods. Moreover, the integration of IERR in predicting fatigue life for notched-shape components indicates a promising approach for enhancing the reliability and efficiency of fatigue life predictions in real-world industrial applications.