Abstract
This research work focuses on the prediction of micro-hardness in Bobbin tool friction stir welding (BT-FSW) of AA6063 plates using machine learning (ML) models. The objectives are to determine the effect of process parameters on micro-hardness and to compare the performance of various ML techniques. The dataset obtained from experimental runs is subjected to classical supervised learning techniques such as LR, SVR, DTR, KNN, as well as ensemble techniques including Bagging, Random Forest, Extra Trees, AdaBoost, Gradient Boosting, and Extreme Gradient Boosting. The results show that ML models effectively predict micro-hardness, with ensemble methods outperforming individual techniques. The tool rotational speed is identified as the most influential parameter affecting micro-hardness.
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