Using a data fusion neural network in the tool wear monitoring of a computer numerical control turning machine

Shang-Liang Chen, T. H. Chang

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The cutting force and the vibration signal of a computer numerical control (CNC) turning machine centre are detected for on-line tool wear monitoring. The feature elements are first extracted from the detected signals. The feature indices are obtained from the feature elements through data preprocessing. Six data fusion methods are used for integrating the feature indices to obtain the fusion indices. The obtained fusion indices are used as the input data of a neural network for online tool wear monitoring. The feasibility of coupling a neural network algorithm with different data fusion methods is investigated, based on the monitored data. The research results show that using a data fusion neural network in tool wear monitoring is feasible.

Original languageEnglish
Pages (from-to)1241-1255
Number of pages15
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume215
Issue number9
DOIs
Publication statusPublished - 2001 Jan 1

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Data fusion
Wear of materials
Neural networks
Monitoring

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

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