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

Shape memory alloys (SMA) belong to the class of smart materials characterized by unique properties – shape memory effect and superelasticity. Due to its superelasticity, the material can withstand significant strains (up to 8-10%) and completely recover its original shape after the load is removed. Under cyclic loading and unloading, a characteristic hysteresis loop forms due to reversible phase transformations between martensite and austenite, reflecting the nonlinear behavior of the material. Reproducing and predicting this behavior is crucial for assessing the durability of structures, but traditional analytical models often fail to provide adequate accuracy. This research employs an automated approach (AutoML) to build machine learning models for predicting the hysteretic behavior of SMA. AutoML offers a systematic and reproducible approach for selecting optimal algorithms and hyperparameters, eliminating the need for manual intervention. Training and testing were performed based on experimental data from 150 cycles of NiTi alloy loading and unloading. For a frequency of 1 Hz, the model showed high prediction accuracy with an MSE equal to 0.0012, an MAE equal to 0.0282, an R² equal to 0.9975, and an MAPE equal to 0.0124, confirming its consistency with experimental data. Machine learning models were also built for other load frequencies. The interpretation of the models’ results was facilitated by Explainable AI tools, specifically the SHAP method, which enabled us to evaluate feature contributions on both a global and local scale. The results confirm the effectiveness of combining AutoML and Explainable AI for accurate and explainable prediction of SMA hysteresis behavior.