Abstract
A wear characterization study was performed to determine the useful lifetime of Polycrystalline Cubic Boron Nitride tooling for the friction stir welding (FSW) of stainless steel samples in support of a nuclear repair welding research and development program. In-situ and ex-situ laser profilometry were utilized as primary methods of monitoring tool geometry degradation, and volumetric defects were detected through both non-destructive and destructive techniques, as repeated welds of a standard sample configuration were produced. The spectral content of weld forces were examined to search for indications of evolving material flow conditions, caused by significant tool wear, that would result in the formation of defects, and an artificial neural network was trained and evaluated as a means of automatically detecting these conditions. The resulting performance constituted a successful demonstration of in-process monitoring of tool wear and weld quality in FSW of a high melting temperature, high hardness material.
