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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.

The purpose of this paper is to evaluate the tool experiences using torque during welding as a means of in-process sensing for tool wear. Metal matrix composites (MMCs) are materials with immense potential for aerospace structural applications. The major barrier to implementation of these materials is manufacturability, specifically joining MMCs to themselves or other materials using fusion welding. Friction stir welding (FSW) is an excellent candidate process for joining MMCs, as it occurs below the melting point of the material, thus precluding the formation of degradative intermetallics’ phases present in fusion welded joints. The limiting factor for use of FSW in this application is wear of the tool. The abrasive particles which give MMCs their enhanced properties progressively erode the tool features that facilitate vertical mixing and consolidation of material during welding, resulting in joints with porosity. While wear can be mitigated by careful selection of process parameters and/or the use of harder tool materials, these approaches have significant complexities and limitations.

This study evaluates using the torque the tool experiences during welding as a means of in-process sensing for tool wear. Process signals were collected during linear FSW of Al 359/SiC/20p and correlated with wear of the tool probe. The results of these experiments demonstrate that there is a correlation between torque and wear, and the torque process signal can potentially be exploited to monitor and control tool wear during welding.

Radial deterioration of the probe during joining of MMCs by FSW corresponds to a decrease in the torque experienced by the tool. Experimentally observed relationship between torque and wear opens the door to the development of in-process sensing, as the decay in the torque signal can be correlated to the amount of volume lost by the probe. The decay function for tool wear in FSW of a particular MMC can be determined experimentally using the methodology presented here. The decay of the torque signal as the tool loses volume presents a potential method for control of the wear process.

This work has near-term commercial applications, as a means of monitoring and controlling wear in process could serve to grow commercial use of MMCs and expand the design space for these materials beyond net or near-net-shape parts.