Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach

The purpose of this study is to evaluate the effectiveness of various machine learning algorithms in predicting the ultimate tensile strength (UTS) of friction stir welded joints. This prediction is crucial for assessing weld quality and integrity. Several investigations are carried out in linear and non-linear regression models, including Poisson Regressor, Gradient Boosting Regressor, Bayesian Ridge, k-Nearest Neighbours, Lasso, Random Forest, Elastic-Net, and Support Vector Regression, using datasets of welding parameters. The models were evaluated for their prediction accuracy and reliability using mean absolute error (MAE), mean square error (MSE) and R2 score metrics. The findings revealed significant variations in model performance. Non-linear models, especially Decision Trees and k-Nearest Neighbours, have exhibited the highest efficiency. The Decision Tree model achieved a remarkable R2 score of 0.97. Meanwhile, the k-Nearest Neighbours model noted the lowest MSE value of 408.62, whereas the MAE value was 14.75. In comparison, the Support Vector Regression model lagged significantly, registering the best MSE and MAE of 14824.74 and 107.73, along the side of a negative R2 score of −0.22, indicating deficient performance. This study highlights the potential of machine learning to forecast the quality of the weld joints. It proposes that the utilisation of these models can significantly enhance production processes and optimise material performance in industrial applications. The study demonstrates the integration of machine learning into manufacturing workflows to enable predictive maintenance and enhance quality control.