Prediction of Weld Defects in Underwater Friction Stir Welding of Dissimilar Materials

Friction Stir Welding (FSW) of aluminum and steel is often encountered with the formation of weld defects due to the improper material flow in the process. This also leads to the formation of inhomogeneous microstructures and non-uniform thickness of inter-metallic layers at the weld interface. The defects, heterogeneous size and orientations of grains, and thickness of intermetallics can be reduced in underwater friction stir welding but cannot be avoided. The destructive tests involved for the identification of weld defects is expensive and time consuming. The prediction of weld defects can also be carried out by the application of signal processing approach on the welding signals such as axial force and spindle torque. In the present work, the discrete wavelet transformation, a signal processing approach has been applied on the axial force and torque which decompose signals into detail and approximate coefficients through filter banks in time-frequency domain. Later different frequency components have been calculated to predict the weld defects. The results have been verified with optical micrographs and X-ray tomography results. Tensile shear strength and hardness of FSWed have been investigated. In addition, microstructures of the welded samples have been studied to understand the variations in the hardness of weld regions.