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

Katedra Cybernetyki, Nanotechnologii i Przetwarzania Danych (RAU/RAU10)

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

Wyniki badań: Wkład do czasopisma › Artykuł › recenzja

139 Cytowania z bazy Scopus

Abstrakt

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.

Język oryginału	angielski
Strony (od–do)	73-80
Liczba stron	8
Czasopismo	Engineering Applications of Artificial Intelligence
Tom	15
Numer wydania	1
Identyfikatory DOI	https://doi.org/10.1016/S0952-1976(02)00004-0
Status publikacji	Opublikowano - lut 2002

Obszary tematyczne ASJC Scopus

Inżynieria sterowania i systemów
Sztuczna inteligencja
Inżynieria elektryczna i elektroniczna

Uzyskaj dostęp do dokumentu

10.1016/S0952-1976(02)00004-0

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

@article{badfbc1b562048e5a3549049e231ccf5,

title = "Tool condition monitoring using artificial intelligence methods",

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.",

keywords = "Artificial intelligence, Cutting force, Tool monitoring",

author = "Marek Balazinski and Ernest Czogala and Krzysztof Jemielniak and Jacek Leski",

year = "2002",

month = feb,

doi = "10.1016/S0952-1976(02)00004-0",

language = "English",

volume = "15",

pages = "73--80",

journal = "Engineering Applications of Artificial Intelligence",

issn = "0952-1976",

publisher = "Elsevier Ltd.",

number = "1",

}

TY - JOUR

T1 - Tool condition monitoring using artificial intelligence methods

AU - Balazinski, Marek

AU - Czogala, Ernest

AU - Jemielniak, Krzysztof

AU - Leski, Jacek

PY - 2002/2

Y1 - 2002/2

N2 - 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.

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

U2 - 10.1016/S0952-1976(02)00004-0

DO - 10.1016/S0952-1976(02)00004-0

M3 - Article

AN - SCOPUS:0036464293

SN - 0952-1976

VL - 15

SP - 73

EP - 80

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

IS - 1

ER -

Tool condition monitoring using artificial intelligence methods

Abstrakt

Obszary tematyczne ASJC Scopus

Uzyskaj dostęp do dokumentu

Inne pliki i linki

Fingerprint

Cytowanie