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contributor authorGibson, Brian T.
contributor authorTang, Wei
contributor authorPeterson, Artie G.
contributor authorFeng, Zhili
contributor authorFrederick, Gregory J.
date accessioned2019-02-28T11:02:10Z
date available2019-02-28T11:02:10Z
date copyright12/18/2017 12:00:00 AM
date issued2018
identifier issn1087-1357
identifier othermanu_140_02_021012.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4251956
description abstractA 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleEvaluating the Potential for Remote In-Process Monitoring of Tool Wear in Friction Stir Welding of Stainless Steel
typeJournal Paper
journal volume140
journal issue2
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4037242
journal fristpage21012
journal lastpage021012-11
treeJournal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 002
contenttypeFulltext


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