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Nowadays, industry 4.0 plays a tremendous role in the manufacturingindustries for increasing the amount of data and accuracy in modernmanufacturing systems. Thanks to artificial intelligence, particularly machinelearning, big data analytics have dramatically amended, and manufacturerseasily exploit organized and unorganized data. This study utilized hybridoptimization algorithms to find friction stir welding and optimal hardnessvalue at the nugget zone. A similar AA 6262 material was used and welded in abutt joint configuration. Tool rotational speed (RPM), tool traverse speed(mm/min), and the plane depth (mm) are used as controllable parameters andoptimized using Taguchi L9, Random Forest, and XG Boost machine learning tools.Analysis of variance was also conducted at a 95% confidence interval foridentifying the significant parameters. The result indicated that thecoefficient of determination from Taguchi L9 orthogonal array is 0.91 obtainedwhile Random Forest and XG Boost algorithm imparted 0.62 and 0.65,respectively.

Performance Evaluation of Machine Learning-based Algorithm and Taguchi  Algorithm for the Determination of the Hardness Value of the Friction Stir  Welded AA 6262 Joints at a Nugget Zone