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
This work addresses the optimization of welding parameters for manufacturers in the industry 4.0 context, but it is incremental as it applies existing methods to a specific dataset.
This study tackled the problem of optimizing friction stir welding parameters to determine hardness values in AA 6262 joints by comparing Taguchi L9, Random Forest, and XG Boost algorithms, with results showing Taguchi L9 achieved a coefficient of determination of 0.91, outperforming Random Forest (0.62) and XG Boost (0.65).
Nowadays, industry 4.0 plays a tremendous role in the manufacturing industries for increasing the amount of data and accuracy in modern manufacturing systems. Thanks to artificial intelligence, particularly machine learning, big data analytics have dramatically amended, and manufacturers easily exploit organized and unorganized data. This study utilized hybrid optimization algorithms to find friction stir welding and optimal hardness value at the nugget zone. A similar AA 6262 material was used and welded in a butt joint configuration. Tool rotational speed (RPM), tool traverse speed (mm/min), and the plane depth (mm) are used as controllable parameters and optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools. Analysis of variance was also conducted at a 95% confidence interval for identifying the significant parameters. The result indicated that the coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained while Random Forest and XG Boost algorithm imparted 0.62 and 0.65, respectively.