Learning input feature selection for sensor fusion in tool wear monitoring

Choon Seong Leem, Stuart E. Dreyfus

Research output: Contribution to conferencePaperpeer-review

8 Citations (Scopus)

Abstract

We develop a neural network for on-line tool wear monitoring in metal cutting environments. After levels of tool wear are topologically ordered by the unsupervised Kohonen's Feature Map, input features from Acoustic Emission and force sensor signals are scaled by an additional supervised learning stage using Input Feature Scaling(IFS) algorithm developed in this work. In a machining experiment, without any off-line feature selection procedure, this neural network with the ability to learn feature selection achieves 94% and 92% accuracy for classification into two and three levels of tool wear, respectively. In conjunction with Kohonen's Feature Map, IFS is a practical and reliable pattern classifier for sensor fusion.

Original languageEnglish
Pages815-820
Number of pages6
Publication statusPublished - 1992
EventProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
Duration: 1992 Nov 151992 Nov 18

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
CitySt.Louis, MO, USA
Period92/11/1592/11/18

All Science Journal Classification (ASJC) codes

  • Software

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