NDLI: Uncertainty prediction for tool wear condition using type-2 tsk fuzzy approach

Content Provider	IEEE Xplore Digital Library
Author	Qun Ren Balazinski, M. Baron, L.
Copyright Year	2009
Description	Author affiliation: Mechanical Engineering Department École Polytechnique de Montréal C.P. 6079, succ. Centre-Ville, Montreal, Quebec, Canada, H3C 3A7 (Qun Ren; Balazinski, M.; Baron, L.)
Abstract	Because of the difficulty in understanding the physics of the machining process, several different intelligence methods, which employ cutting forces for estimation tool wear, have been developed in the past few years. Unfortunately, none of them can overcome the difficulty to estimate the errors of approximation during tool wear monitoring. This paper aimed at presenting a tool wear monitoring method using type-2 Takagi-Sugeno-Kang (TSK) fuzzy approach. This innovative method not only provides high reliability of the tool wear prediction over a wide range of cutting conditions, but also assesses the uncertainties associated with the modeling process and with the outcome of the model itself. The magnitude and direction of uncertainties in the machining process are described explicitly to increase the credibility of assessments.
Starting Page	660
Ending Page	665
File Size	351915
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Machining Fuzzy logic Condition monitoring Artificial intelligence Neural networks Physics Predictive models Machine tools Manufacturing processes approximation uncertainty estimation type-2 TSK fuzzy logic tool wear condition machining
Content Type	Text
Resource Type	Article

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