AutoML with Explainable AI Analysis: Optimization and Interpretation of Machine Learning Models for the Prediction of Hysteresis Behavior in Shape Memory Alloys

This study presents an approach for predicting the hysteresis behavior of shape memory alloys (SMAs) based on automated machine learning (AutoML) integrated with explainable artificial intelligence (XAI). Experimental data from cyclic tests of NiTi wire under loading frequencies of 0.3, 0.5, 1, and 5 Hz were used for model development. The AutoML framework PyCaret enabled automated model selection, hyperparameter optimization, and performance comparison of regression algorithms. The highest prediction accuracy was achieved by the LightGBM model (for 0.3 Hz and 1 Hz) and the CatBoost model (for 0.5 Hz and 5 Hz), both demonstrating a coefficient of determination R2 > 0.997 and low MSE, MAE, and MAPE values. Integration of XAI through the SHAP method provided both global and local interpretability of the model’s behavior. The analysis revealed the dominant influence of the Stress parameter, the physically meaningful role of the loading or unloading stage (UpDown), and a gradual increase in the contribution of the Cycle parameter in later cycles, reflecting fatigue accumulation effects. The obtained results confirm the high accuracy, interpretability, and physical consistency of the developed models.