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This paper aims to investigate methods of implementing in‐process fault avoidance in robotic friction stir welding (FSW).

Investigations into the possibilities for automatically detecting gap‐faults in a friction stir lap weld were conducted. Force signals were collected from a number of lap welds containing differing degrees of gap faults. Statistical analysis was carried out to determine whether these signals could be used to develop an automatic fault detector/classifier.

The results demonstrate that the frequency spectra of collected force signals can be mapped to a lower dimension through discovered discriminant functions where the faulty welds and control welds are linearly separable. This implies that a robust and precise classifier is very plausible, given force signals.

Future research should focus on a complete controller using the information reported in this paper. This should allow for a robotic friction stir welder to detect and avoid faults in real time. This would improve manufacturing safety and yield.

This paper is applicable to the rapidly expanding robotic FSW industry. A great advantage of heavy machine tool versus robotic FSW is that the robot cannot supply the same amount of rigidity. Future work must strive to overcome this lack of mechanical rigidity with intelligent control, as has been examined in this paper.

This paper investigates fault detection in robotic FSW. Fault detection and avoidance are essential for the increased robustness of robotic FSW. The paper's results describe very promising directions for such implementation.