Machine Learning Assisted Prediction of Recovered Angle of Carbon Fiber Reinforced Shape Memory Epoxy Carbon Composites

ABSTRACT Shape memory polymers and their composites are promising smart materials for a wide range of applications associated with smart material‐based actuators. Carbon fiber‐reinforced shape memory epoxy composites can enable electroactive shape recovery via Joule heating, offering precise, remote‐controlled actuation. However, predicting time‐dependent recovery behavior across varying current levels remains challenging due to limited experimental data. This study presents an overfitting‐aware machine learning framework for predicting shape recovery angles of the shape memory epoxy composites activated by Joule heating as a function of time and applied current. Four regression algorithms: Random Forest (RF), Support Vector Regression (SVR), Regularized Polynomial, and Gaussian Process Regression (GPR) are considered for constructing the shape memory recovery angle prediction algorithm. In the models, overfitting constraints are integrated into Bayesian hyperparameter optimization using a hybrid objective function that penalizes excessive generalization gaps. Stratified fivefold cross‐validation ensures balanced current distribution across folds. Results demonstrate that kernel‐based methods outperform tree ensembles: SVR achieves the highest accuracy with controlled generalization ( R 2 = 0.944) while RF exhibits limited effectiveness ( R 2 = 0.842), confirming tree‐based methods are less suitable for small, smooth datasets. SVR provides optimal accuracy‐generalization balance for reliable engineering predictions.