Machine Learning Prediction of Aluminum Alloy Stress-Strain Curves at Variable Temperatures with Failure Analysis

Accurately predicting stress-strain curves is essential for understanding the plastic behavior of metallic materials. This study investigates the effectiveness of machine learning (ML) methods in predicting stress-strain curves for aluminum alloys at different temperature levels. Specifically, three ML techniques, Gaussian process regression (GPR), neural network (NN), and boosted trees (BST), were utilized to predict the stress-strain response of Al6061-T6 at temperatures ranging from 25 to 300 degrees C. The performance of these ML models was evaluated using actual strain-stress measurements obtained from uniaxial tensile testing on Al6061-T6. A fivefold cross-validation approach was applied to train the models under investigation. Optimal parameters for the ML techniques were obtained during the training phase using the Bayesian optimization method to minimize mean absolute error. Four statistical metrics were employed to assess the accuracy of the predictions. The results of this study demonstrate the potential of machine learning methods in accurately predicting strain-stress measurements of materials. Additionally, the NN model outperformed the other models, achieving an average mean absolute error percentage of 0.213 and a coefficient of determination R2 of 0.998. Furthermore, it was observed that crack initiation mechanisms varied with temperature; particle fracture dominated at temperatures up to 200 degrees C, while interfacial decohesion prevailed at 300 degrees C.