Force data-driven machine learning for defects in friction stir welding

This study proposes a strategy for developing force-data-driven machine learning models to precisely predict defects and their types in friction stir welding (FSW). The characteristics of the three component forces in FSW, including traverse force (F-x), lateral force (F-y), and plunge force (F-Z) are studied. The change in the force wave corresponded well with the variation in the defect. F-yavg had the best correlation with the characteristics of tunnel defects, whereas some other time-frequency features had negligible effects on the defect variation. The machine learning models built with the input of 15 force features could detect defects with an accuracy of 95.8% and classify them into tunnels and porosities with an accuracy of 98.0%. The abnormal increase in F-yavg, caused by the buildup of redundant material transported to the retreating side, was the main characteristic of force change when a defect was formed.