Abstract

The use of five machine-learning regression models, Gaussian Process Regression (GPR), Support Vector Machine (SVM), XGBoost, CatBoost, and LightGBM, was for predicting the ultimate tensile strength (UTS) of friction stir welded (FSW) AZ31B magnesium alloy joints. A controlled, single-source, experimental dataset comprising 99 observations was created on the same FSW machine under the same laboratory conditions. The dataset covered three feed rates, eleven rotational speeds and three tool tilt angles, and each parameter combination was represented by the mean UTS value from triplicate tensile tests. The input variables were the feed rate, rotational speed and tilt angle, and the prediction target was UTS measured using ASTM E8M-04. To create a more challenging and realistic assessment, we implemented blocked-holdout validation, keeping only the previously unseen rotational speed levels for the test set. Hyperparameters were selected via exhaustive grid search, with 5-fold GroupKFold cross-validation used solely on the training data. Among the models that were tested, GPR demonstrated the best overall blocked-holdout performance, with a R2 = 0.985 and RMSE = 1.798 MPa. XGBoost (R2 = 0.923) and CatBoost (R2 = 0.912) also demonstrated competitive performance. Conversely, LightGBM exhibited the poorest generalization performance (R2 = 0.817). The findings suggest that kernel and boosting-based approaches have the capacity to adequately simulate the nonlinear relationship between FSW process parameters and tensile performance, while GPR demonstrated the best generalization under the blocked-holdout evaluation strategy.

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