Belalia, Sif Eddine, Serier, Mohamed, Al-Sabur, Raheem. (1402). Parametric Analysis for Torque Prediction in Friction Stir Welding Using Machine Learning and Shapley Additive Explanations. , 55(1), 113-124. doi: 10.22059/jcamech.2024.370055.924
Sif Eddine Belalia; Mohamed Serier; Raheem Al-Sabur. "Parametric Analysis for Torque Prediction in Friction Stir Welding Using Machine Learning and Shapley Additive Explanations". , 55, 1, 1402, 113-124. doi: 10.22059/jcamech.2024.370055.924
Belalia, Sif Eddine, Serier, Mohamed, Al-Sabur, Raheem. (1402). 'Parametric Analysis for Torque Prediction in Friction Stir Welding Using Machine Learning and Shapley Additive Explanations', , 55(1), pp. 113-124. doi: 10.22059/jcamech.2024.370055.924
Belalia, Sif Eddine, Serier, Mohamed, Al-Sabur, Raheem. Parametric Analysis for Torque Prediction in Friction Stir Welding Using Machine Learning and Shapley Additive Explanations. , 1402; 55(1): 113-124. doi: 10.22059/jcamech.2024.370055.924

Parametric Analysis for Torque Prediction in Friction Stir Welding Using Machine Learning and Shapley Additive Explanations

Journal of Computational Applied Mechanics
دوره 55، شماره 1، بهار 2024، صفحه 113-124    اصل مقاله (670 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jcamech.2024.370055.924
نویسندگان
Sif Eddine Belalia* 1، 2؛ Mohamed Serier3؛ Raheem Al-Sabur4
1Departement of Mechanical Engineering, University of Relizane, Relizane, 48000, Algeria.
2Industrial Engineering and Sustainable Development Laboratory, Faculty of Science and Technology, University of Relizane, Relizane, Algeria.
3Departement of Mechanical Engineering, University of Ain Témouchent Belhadj Bouchaib, Ain Témouchent, Algeria.
4Mechanical Department, Engineering College, University of Basrah, Basrah, Iraq.
چکیده
Friction Stir Welding (FSW) has revolutionized modern manufacturing with its advantages, such as minimal heat-affected zones and improved material properties. Accurate torque prediction in FSW is crucial for weld quality, process efficiency, and energy conservation. Many researchers achieved models for torque based on experimental research, yet the models were limited to a specific type of material. In recent years, the use of machine learning techniques has increased in industry in general and in welding in particular. In this study, a machine learning model was prepared based on artificial neural networks, and Shapley-Additive Explanations were used to predict the rotational torque from 287 experiments that had been conducted in several previous studies. The achieved model has remarkable predictive performance, with an R-squared of 99.53% and low errors (MAE, MAPE, and RMSE). Moreover, a machine learning polynomial regression was examined for comparisons. A parametric importance analysis revealed that rotational speed, plate thickness, and tilt angle significantly affect torque predictions, while the rest of the variables had minimal importance.
کلیدواژه‌ها
Friction stir welding؛ torque prediction؛ Artificial neural network؛ Parametric study
مراجع
آمار
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