Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning

In situ fatigue crack propagation experiment was conducted on laser cladding with coaxial powder feeding (LCPF) K477 under various stress ratios and temperatures. Multiple crack initiation sites were observed by using in situ scanning electron microscopy (SEM). The fatigue short crack growth rate was measured, and the impacts of temperature and stress ratio on this growth rate were analyzed. Based on these experiments, the experimental data were expanded, and three ensemble learning algorithms, that is, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were employed to establish a fatigue short crack growth rate model controlled by multiple parameters. It is indicated that the RF model performs the best, achieving a coefficient of determination (R²) of up to 0.88. The fatigue life predicted by the machine learning (ML) method agrees well with the experimental one.