Enhancing Predictive Capabilities: Machine Learning Approaches for Predicting Mechanical Behavior in Friction Stir Welded Aluminum Alloys

Accurate prediction of friction stir welding (FSW) joint behavior is crucial for optimizing welding processes and ensuring structural integrity. This study exploits machine learning to predict the mechanical behavior of aluminum alloy FSW joints under varying temperatures. It involves a comparison of predictive performance across 18 models, including support vector regression (SVR), Gaussian process regression (GPR), ensemble models, and five distinct types of neural networks (NN). The assessment used Al6061-T6 aluminum alloy with the FSW joining method at temperatures of 25, 100, 200, and 300 °C. To ensure robustness, the machine learning models were developed using a fivefold cross-validation approach, with Bayesian optimization applied for fine-tuning during training. Results revealed the ability of machine learning to precisely predict the mechanical behavior of FSW joints. Specifically, GPR and the triple NN model outperformed other models, achieving average R² values of 0.9879 and 0.9703, respectively.