Abstract
Defect identification in friction stir welding process is a challenging task as most of the defects formed at subsurface level. The available non destructive approaches are limited only to offline assessment of the defects. In automated environment defect identification methodologies with online monitoring possibilities are gaining significant attention. In this research work sensor fusion based model has been developed for identification of tunnel defect in friction stir welding samples. Real time signal information acquired from tool main spindle motor current signal, rotational speed and vertical force signal have been fused to estimate a quantitative indicator for defect detection within the welded samples. Real time signals are acquired using a force measurement system and a speed sensor with a sampling frequency of 10 kHz. Feature level fusion modeling is incorporated to develop a methodology for defect detection in friction stir welding process. The estimate can be used for identifying defective samples from defect free samples which is advantageous in automated production environment.
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