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contributor authorChristopher A. Suprock
contributor authorLarry M. Downey
contributor authorJohn T. Roth
date accessioned2017-05-09T00:34:09Z
date available2017-05-09T00:34:09Z
date copyrightApril, 2009
date issued2009
identifier issn1087-1357
identifier otherJMSEFK-28113#021003_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/141250
description abstractIn this paper, an endmill condition monitoring technique is presented for curvilinear cutting. This algorithm operates without the need for prior knowledge of cutting conditions, tool type, cut curvature, cut direction, or directional rate of change. The goal of this method is an indirect measurement of the tool wear able to indicate when wear is accelerating without direct measurement of the tool. This technique is based on an autoregressive-type monitoring algorithm, which is used to track the tool’s condition using a tri-axial accelerometer. Accelerometer signals are monitored due to the sensor’s relatively low cost and since use of the sensor does not limit the machining envelope. To demonstrate repeatability, eight life tests were conducted. The technique discussed herein successfully delivers prognosis of impending fracture or meltdown due to wear in all cases, providing sufficient time to remove the tools before failure is realized. Furthermore, the algorithm produces similar trends capable of forecasting failure, regardless of tool type and cut geometry. Success is seen in all cases without requiring algorithm modifications or any prior information regarding the tool or cutting conditions.
publisherThe American Society of Mechanical Engineers (ASME)
titleEndmill Condition Monitoring and Failure Forecasting Method for Curvilinear Cuts of Nonconstant Radii
typeJournal Paper
journal volume131
journal issue2
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.3075895
journal fristpage21003
identifier eissn1528-8935
keywordsCutting
keywordsFailure
keywordsLife testing
keywordsWear testing
keywordsWear
keywordsAlgorithms AND Condition monitoring
treeJournal of Manufacturing Science and Engineering:;2009:;volume( 131 ):;issue: 002
contenttypeFulltext


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