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Erschienen in: Journal of Intelligent Manufacturing 2/2015

01.04.2015

Health assessment and life prediction of cutting tools based on support vector regression

verfasst von: T. Benkedjouh, K. Medjaher, N. Zerhouni, S. Rechak

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 2/2015

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Abstract

The integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool’s one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life (RUL) of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their RUL. This information can then be used by the operators to take appropriate maintenance actions.

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Metadaten
Titel
Health assessment and life prediction of cutting tools based on support vector regression
verfasst von
T. Benkedjouh
K. Medjaher
N. Zerhouni
S. Rechak
Publikationsdatum
01.04.2015
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 2/2015
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-013-0774-6

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