Tool wear prediction from acoustic emission and surface characteristics via an artificial neural network

P. Wilkinson, R. L. Reuben, J. D C Jones, J. S. Barton, D. P. Hand, Tom Carolan, Steve Kidd

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

We examine the application of an artificial neural network to classification of tool wear states in face milling. The input features were derived from measurements of acoustic emission during machining and topography of the machined surfaces. Five input features were applied to the back-propagating neural network to predict a wear state of light, medium or heavy wear. We present results from milling experiments with multi- and single-point cutting and compare the neural network predictions with observed cutting insert wear states.

Original languageEnglish
Pages (from-to)955-966
Number of pages12
JournalMechanical Systems and Signal Processing
Volume13
Issue number6
DOIs
Publication statusPublished - Nov 1999

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