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Application of sensor fusion and polynomial classifiers to tool wear monitoring

  • American University of Sharjah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper presents a novel approach to model and predict cutting tool wear using statistical signal analysis, pattern recognition and sensor fusion. The data are acquired from two sources: an acoustic emission sensor (AE) and a tool post dynamometer. The pattern recognition used here is based on two methods: Artificial Neural Networks (ANN), and Polynomial Classifiers (PC). In this work we compare between cutting tool wear predicted by neural network (ANN) and polynomial classifiers (PC). For the case study presented; PC proved to significantly reduce the required training time compared to that required by an ANN without compromising the prediction accuracy. The predicted results compared well to the measured tool wear.

Original languageEnglish
Title of host publicationProceeding of the 5th International Symposium on Mechatronics and its Applications, ISMA 2008
DOIs
StatePublished - 2008
Externally publishedYes
Event5th International Symposium on Mechatronics and its Applications, ISMA 2008 - Amman, Jordan
Duration: 27 May 200829 May 2008

Publication series

NameProceeding of the 5th International Symposium on Mechatronics and its Applications, ISMA 2008

Conference

Conference5th International Symposium on Mechatronics and its Applications, ISMA 2008
Country/TerritoryJordan
CityAmman
Period27/05/0829/05/08

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