Defect Detection in Friction Stir Welding Process Using Signal Information and Fractal Theory

Detection of defects in welding is always a challenging task. In friction stir welding process, most of the defects are subsurface which needs special techniques for its detection. In the present research work, an attempt has been made to identify the defects in friction stir welded samples through the information contained in signals acquired during the process. For this analysis, force signals, tool rotational speed signal and main spindle motor current signatures are analyzed using well known fractal theory. Higuchi's fractal dimension algorithm is implemented to extract signal based information in terms of single valued indicator known as fractal dimension. The accountable variation in the computed fractal dimensions against the signals for defective and defect free cases demonstrates the applicability of fractal theory in detection of sub surface defects in friction stir welding process.