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Research Papers

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

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

Wei Tang

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

Artie G. Peterson

Consultant
Charlotte, NC 28262

Zhili Feng

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

Gregory J. Frederick

Weld Repair Technology Center,
Electric Power Research Institute,
1300 W W.T. Harris Boulevard,
Charlotte, NC 28262
e-mail: gfrederi@epri.com

1Corresponding author.

Manuscript received April 24, 2017; final manuscript received June 28, 2017; published online December 18, 2017. Assoc. Editor: Jaydeep Karandikar.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Manuf. Sci. Eng 140(2), 021012 (Dec 18, 2017) (11 pages) Paper No: MANU-17-1279; doi: 10.1115/1.4037242 History: Received April 24, 2017; Revised June 28, 2017

A wear characterization study was performed to determine the useful lifetime of polycrystalline cubic boron nitride (PCBN) 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 nondestructive 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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Figures

Grahic Jump Location
Fig. 1

PCBN tool in new condition (left) and after weld 12 (right)

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Fig. 3

Radiographic NDE and destructive weld sectioning

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Fig. 4

Tool retraction points of weld 1 (left) and weld 12 with defect indicated (right)

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Fig. 5

FSW tool midplane profilometry

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Fig. 6

Tool wear at points of interest

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Fig. 7

Analysis of midplane cross-sectional area of PCBN insert

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Fig. 8

Three-dimensional-reconstruction-based tool models: (a) top view of scan strategy with nominal tool shoulder diameter shown, (b) assembly of profilometry traces for new tool, (c) reconstruction of new tool, (d) reconstruction of tool after 12 welds, and (e) wear in z-axis from new tool through 12 welds

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Fig. 9

Tool mass reduction with model-based estimations

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Fig. 10

As-collected torque signals

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Fig. 11

Variation of torque and plunge depth

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Fig. 12

Power spectral density of weld forces

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Fig. 13

MLP training and validation

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