Data-Enhanced Low-Cycle Fatigue Life Prediction Model Based on Nickel-Based Superalloys

To overcome the challenges of limited experimental data and improve the accuracy of empirical formulas, we propose a low-cycle fatigue (LCF) life prediction model for nickel-based superalloys using a data augmentation method. This method utilizes a variational autoencoder (VAE) to generate low-cycle fatigue data and form an augmented dataset. The Pearson correlation coefficient (PCC) is employed to verify the similarity of feature distributions between the original and augmented datasets. Six machine learning models, namely random forest (RF), artificial neural network (ANN), support vector machine (SVM), gradient-boosted decision tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost), are utilized to predict the LCF life of nickel-based superalloys. Results indicate that the proposed data augmentation method based on VAE can effectively expand the dataset, and the mean absolute error (MAE), root mean square error (RMSE), and R-squared (R²) values achieved using the CatBoost model, with respective values of 0.0242, 0.0391, and 0.9538, are superior to those of the other models. The proposed method reduces the cost and time associated with LCF experiments and accurately establishes the relationship between fatigue characteristics and LCF life of nickel-based superalloys.