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research-article

Evaluating the Potential for Remote In-Process Monitoring of Tool Wear in Friction Stir Welding of Stainless Steel

[+] Author and Article Information
Brian T. Gibson

ASME Member Oak Ridge National Laboratory, Materials Science and Technology Division, One Bethel Valley Road, Oak Ridge, TN 37831 MS: 6097
gibsonbt@ornl.gov

Wei Tang

Oak Ridge National Laboratory, Materials Science and Technology Division, One Bethel Valley Road, Oak Ridge, TN 37831 MS: 6096
tangw@ornl.gov

Artie G. Peterson

ASME Member Consultant, Charlotte, NC
artiegene@yahoo.com

Zhili Feng

Oak Ridge National Laboratory, Materials Science and Technology Division, One Bethel Valley Road, Oak Ridge, TN 37831 MS: 6096
fengz@ornl.gov

Gregory Frederick

Electric Power Research Institute, Weld Repair Technology Center, 1300 W W.T. Harris Blvd, Charlotte, NC 28262
gfrederi@epri.com

1Corresponding author.

ASME doi:10.1115/1.4037242 History: Received April 24, 2017; Revised June 28, 2017

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. These combined methods of characterization allowed for the successful correlation of defect formation with tool condition. Additionally, the spectral content of weld forces was examined to search for indications of evolving material flow conditions, caused by significant tool wear, that would result in the formation of defects; this analysis established the basis for a system that would automatically detect these conditions. To demonstrate this type of system, an artificial neural network was trained and evaluated, and a 95.2% classification rate of defined defect states in validation was achieved. This 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, with implications for remote monitoring capabilities in the specific application of nuclear repair welding.

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