Predicting Axial Force in Friction Stir Welded Joints of AA7075-T6 and AA2024-T3 Using Machine Learning Techniques

Abstract

The friction stir welding (FSW) process has gained popularity in joining various types of aluminum alloys. The heat generated during the process is caused by the friction between the shoulder of the welding tool and the workpiece. The axial force (Fz) of the weld plays a crucial role in the welding process. If the axial force is insufficient to generate heat will result in defective workpieces. Therefore, the aim of this study was to predict the axial force of the FSW process of dissimilar aluminum alloys (AA7075-T6 and AA2024-T3) using machine learning techniques. The data used to create the predictive model was obtained from experiments involving 5 factors, each with 2 levels: 1) welding speed (mm/min), 2) rotational speed (RPM), 3) type of tools, 4) plunge depth (mm), and 5) dwell time (sec). The axial force was measured using a dynamometer with a sampling frequency of 10 Hz. The predictive model was created using all 4 algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. The performance of the four predictive models was evaluated using four metrics: mean absolute error (MAE), mean absolute percent error (MAPE), mean square error (MSE), and root mean square error (RMSE). The results showed that the AdaBoost algorithm had the best performance, with MAE, MAPE, MSE, and RMSE values of 509.08, 0.24, 452591.73, and 672.75, respectively. The AdaBoost algorithm was then used to predict the axial force using a dataset of 29,282 data, with predicted minimum average axial force of 1871.6 N. When compared to the axial force measured in the experiment, which was 1265 N, the results showed that the AdaBoost algorithm was capable of predicting the axial force with acceptable accuracy.