Design of artificial neural networks for tool wear monitoring

Kurapati Venkatesh, Mengchu Zhou, Reggie J. Caudill

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing.

Original languageEnglish (US)
Pages (from-to)215-226
Number of pages12
JournalJournal of Intelligent Manufacturing
Volume8
Issue number3
DOIs
StatePublished - Jan 1 1997

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

Keywords

  • Flexible manufacturing systems
  • Neural networks
  • Tool wear monitoring

Fingerprint Dive into the research topics of 'Design of artificial neural networks for tool wear monitoring'. Together they form a unique fingerprint.

Cite this