Process Parameter Optimization of 6061AA Friction Stir Welded Joints Using Supervised Machine Learning Regression-Based Algorithms

Document Type : Regular Article


1 Graduated M.Sc., Faculty of Mechanical and Industrial Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia

2 Assistant Professor, Faculty of Mechanical and Industrial Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia

3 Graduate M.Sc., Department of Chemistry, Materials, and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy


In this contemporary technology epoch, material utilization is crucial concerning saving energy demand. One of the thinking points of the interest domain is weight reduction. The highest strength to weight ratio criterion of the welded joint has enthralled keenness in virtually all areas where heft reduction is indispensable. Lightweight materials and their joining processes are also a recent point of research demands in the manufacturing industries. Friction Stir Welding (FSW) is one of the recent advancements for joining materials without adding any third material (filler rod) and joining below the melting point of the parent material. The process is widely used for joining similar and dissimilar metals, especially lightweight non-ferrous materials like aluminum, copper, and magnesium alloys. This paper presents verdicts of optimum process parameters on attaining enhanced mechanical properties of the weld joint. The experiment was conducted on a 5 mm 6061 aluminum alloy sheet. Process parameters; tool material, rotational speed, traverse speed, and axial forces were utilized. Mechanical properties of the weld joint are examined employing a tensile test, and the maximum joint strength efficiency was reached 94.2%. Supervised Machine Learning based Regression algorithms such as Decision Trees, Random Forest, and Gradient Boosting algorithms were used. The results showed that the Random Forest algorithm yielded the highest coefficient of determination value of 0.926, giving the best fit compared to other algorithms. Furthermore, this method can be extended in large-scale and thick aluminum base materials.


Main Subjects

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