Monitoring of tool wear using artificial neural networks

Kurapati Venkatesh, Meng Chu Zhou, Reggie Caudill

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at previous instance of time, memory was included in ANN. With this aim, an ANN without memory, an ANN with one phase memory, and an ANN with two are investigated in this study. The advantages and unique characteristics of the proposed tool wear modeling scheme with earlier methods of tool wear estimation are discussed.

Original languageEnglish (US)
Pages (from-to)2565-2569
Number of pages5
JournalProceedings of the American Control Conference
StatePublished - 1994
EventProceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA
Duration: Jun 29 1994Jul 1 1994

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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