Fatigue Predictive Modeling of Composite Materials for Wind Turbine Blades Using Explainable Gradient Boosting Models

Wind turbine blades are subjected to cyclic loading conditions throughout their operational lifetime, making fatigue a critical factor in their design. Accurate prediction of the fatigue performance of wind turbine blades is important for optimizing their design and extending the operational lifespan of wind energy systems. This study aims to develop predictive models of laminated composite fatigue life based on experimental results published by Montana State University, Bozeman, Composite Material Technologies Research Group. The models have been trained on a dataset consisting of 855 data points. Each data point consists of the stacking sequence, fiber volume fraction, stress amplitude, loading frequency, laminate thickness, and the number of cycles of a fatigue test carried out on a laminated composite specimen. The output feature of the dataset is the number of cycles, which indicates the fatigue life of a specimen. Random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and extra trees regressor models have been trained to predict the fatigue life of the specimens. For optimum performance, the hyperparameters of these models were optimized using GridSearchCV optimization. The total number of cycles to failure could be predicted with a coefficient of determination greater than 0.9. A feature importance analysis was carried out using the SHapley Additive exPlanations (SHAP) approach. LightGBM showed the highest performance among the models (R2 = 0.9054, RMSE = 1.3668, and MSE = 1.8682).