Tool condition monitoring using artificial intelligence methods

Marek Balazinski; Ernest Czogala; Krzysztof Jemielniak; Jacek Leski

doi:10.1016/S0952-1976(02)00004-0

Tool condition monitoring using artificial intelligence methods

Marek Balazinski
, Ernest Czogala
, Krzysztof Jemielniak
, Jacek Leski

Department of Cybernetics, Nanotechnology and Data Processing (RAU/RAU10)

École Polytechnique de Montréal
Silesian University of Technology
Warsaw University of Technology

Research output: Contribution to journal › Article › peer-review

139 Citations (Scopus)

Abstract

This paper describes an application of three artificial intelligence (AI) methods to estimate tool wear in lathe turning. The first two are "conventional" AI methods - the feed forward back propagation neural network and the fuzzy decision support system. The third is a new artificial neural network based-fuzzy inference system with moving consequents in if-then rules. Tool wear estimation is based on the measurement of cutting force components. This paper discusses a comparison of usability of these methods in practice.

Original language	English
Pages (from-to)	73-80
Number of pages	8
Journal	Engineering Applications of Artificial Intelligence
Volume	15
Issue number	1
DOIs	https://doi.org/10.1016/S0952-1976(02)00004-0
Publication status	Published - Feb 2002

Keywords

Artificial intelligence
Cutting force
Tool monitoring

ASJC Scopus subject areas

Control and Systems Engineering
Artificial Intelligence
Electrical and Electronic Engineering

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10.1016/S0952-1976(02)00004-0

Cite this

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AU - Leski, Jacek

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AB - This paper describes an application of three artificial intelligence (AI) methods to estimate tool wear in lathe turning. The first two are "conventional" AI methods - the feed forward back propagation neural network and the fuzzy decision support system. The third is a new artificial neural network based-fuzzy inference system with moving consequents in if-then rules. Tool wear estimation is based on the measurement of cutting force components. This paper discusses a comparison of usability of these methods in practice.

KW - Artificial intelligence

KW - Cutting force

KW - Tool monitoring

UR - https://www.scopus.com/pages/publications/0036464293

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Tool condition monitoring using artificial intelligence methods

Abstract

Keywords

ASJC Scopus subject areas

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