Modeling the Hysteresis Behavior of SMA by an Ensemble Voting Machine Learning Model

Shape memory alloys (SMAs) are characterized by complex hysteresis behavior resulting from phase transformations between martensite and austenite, which is significantly affected by the frequency of cyclic loading. Accurate prediction of these processes is essential to ensure the reliability of structural components in various engineering applications. This paper presents an approach for predicting the hysteresis behavior of SMAs based on an ensemble Voting machine learning model. The ensemble included Random Forest, Gradient Boosting, Extra Trees, Support Vector Regressor, K-Nearest Neighbors, and a Multilayer Perceptron. The model weights were determined as the inverse of the mean squared error, which ensured a balanced contribution of each algorithm to the final prediction. The model performance was evaluated using the MAE, MSE, R², and MAPE metrics. The results demonstrated high prediction accuracy (R 2 > 0.998, MAE < 0.022, MSE < 0.0008, and MAPE < 0.008) and confirmed the ability of the model to generalize across independent cycles, including extrapolated ones (251 and 300). The predicted hysteresis loops showed good agreement with the experimental curves. The obtained results confirm the effectiveness of the ensemble approach for modeling the behavior of SMAs and predicting their functional properties.