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A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks

Identifieur interne : 000870 ( PascalFrancis/Corpus ); précédent : 000869; suivant : 000871

A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks

Auteurs : D. E. Jr Dimla ; P. M. Lister ; N. J. Leighton

Source :

RBID : Pascal:98-0101386

Descripteurs français

English descriptors

Abstract

The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such a manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable Tool Condition Monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realised through traditional tool changing philosophies. Unfortunately, the neural network algorithms used have been complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple Multi-layer Perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90% was attainable.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

pA  
A01 01  1    @0 0537-9989
A06       @2 440
A08 01  1  ENG  @1 A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks
A09 01  1  ENG  @1 Artificial neural networks : Cambridge, 7-9 July 1997
A11 01  1    @1 DIMLA (D. E. JR)
A11 02  1    @1 LISTER (P. M.)
A11 03  1    @1 LEIGHTON (N. J.)
A14 01      @1 Engineering Research Group, SEBE, University of Wolverhampton @3 GBR @Z 1 aut. @Z 2 aut. @Z 3 aut.
A18 01  1    @1 Institution of Electrical Engineers @2 London @3 GBR @9 patr.
A20       @1 306-311
A21       @1 1997
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A26 01      @0 0-85296-690-3
A43 01      @1 INIST @2 12497 @5 354000068075400540
A44       @0 0000 @1 © 1998 INIST-CNRS. All rights reserved.
A45       @0 18 ref.
A47 01  1    @0 98-0101386
A60       @1 P @2 C
A61       @0 A
A64   1    @0 IEE conference publication
A66 01      @0 GBR
C01 01    ENG  @0 The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such a manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable Tool Condition Monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realised through traditional tool changing philosophies. Unfortunately, the neural network algorithms used have been complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple Multi-layer Perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90% was attainable.
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C03 01  X  GER  @0 Metrologie @5 01
C03 01  X  SPA  @0 Metrología @5 01
C03 02  X  FRE  @0 Monitorage @5 02
C03 02  X  ENG  @0 Monitoring @5 02
C03 02  X  SPA  @0 Monitoreo @5 02
C03 03  X  FRE  @0 Usinage @5 03
C03 03  X  ENG  @0 Machining @5 03
C03 03  X  GER  @0 Zerspanen @5 03
C03 03  X  SPA  @0 Mecanizado @5 03
C03 04  X  FRE  @0 Fusion donnée @5 05
C03 04  X  ENG  @0 Data fusion @5 05
C03 04  X  SPA  @0 Fusión datos @5 05
C03 05  X  FRE  @0 Outil coupe @5 06
C03 05  X  ENG  @0 Cutting tool @5 06
C03 05  X  GER  @0 Zerspanungswerkzeug @5 06
C03 05  X  SPA  @0 Herramienta corte @5 06
C03 06  X  FRE  @0 Force coupe @5 07
C03 06  X  ENG  @0 Cutting force @5 07
C03 06  X  GER  @0 Schnittkraft @5 07
C03 06  X  SPA  @0 Fuerza corte @5 07
C03 07  X  FRE  @0 Vibration @5 08
C03 07  X  ENG  @0 Vibration @5 08
C03 07  X  GER  @0 Schwingung @5 08
C03 07  X  SPA  @0 Vibración @5 08
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C03 08  X  SPA  @0 Inteligencia artificial @5 09
C03 09  X  FRE  @0 Perceptron @5 10
C03 09  X  ENG  @0 Perceptron @5 10
C03 09  X  SPA  @0 Perceptron @5 10
C03 10  X  FRE  @0 Réseau neuronal @5 11
C03 10  X  ENG  @0 Neural network @5 11
C03 10  X  SPA  @0 Red neuronal @5 11
C03 11  X  FRE  @0 Réseau multicouche @5 12
C03 11  X  ENG  @0 Multilayer network @5 12
C03 11  X  SPA  @0 Red multinivel @5 12
N21       @1 061
pR  
A30 01  1  ENG  @1 International conference on artificial neural networks @2 5 @3 Cambridge GBR @4 1997-07-07

Format Inist (serveur)

NO : PASCAL 98-0101386 INIST
ET : A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks
AU : DIMLA (D. E. JR); LISTER (P. M.); LEIGHTON (N. J.)
AF : Engineering Research Group, SEBE, University of Wolverhampton/Royaume-Uni (1 aut., 2 aut., 3 aut.)
DT : Publication en série; Congrès; Niveau analytique
SO : IEE conference publication; ISSN 0537-9989; Royaume-Uni; Da. 1997; No. 440; Pp. 306-311; Bibl. 18 ref.
LA : Anglais
EA : The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such a manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable Tool Condition Monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realised through traditional tool changing philosophies. Unfortunately, the neural network algorithms used have been complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple Multi-layer Perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90% was attainable.
CC : 001D02C06; 001D12C01; 001D11C05A; 240
FD : Métrologie; Monitorage; Usinage; Fusion donnée; Outil coupe; Force coupe; Vibration; Intelligence artificielle; Perceptron; Réseau neuronal; Réseau multicouche
ED : Metrology; Monitoring; Machining; Data fusion; Cutting tool; Cutting force; Vibration; Artificial intelligence; Perceptron; Neural network; Multilayer network
GD : Metrologie; Zerspanen; Zerspanungswerkzeug; Schnittkraft; Schwingung
SD : Metrología; Monitoreo; Mecanizado; Fusión datos; Herramienta corte; Fuerza corte; Vibración; Inteligencia artificial; Perceptron; Red neuronal; Red multinivel
LO : INIST-12497.354000068075400540
ID : 98-0101386

Links to Exploration step

Pascal:98-0101386

Le document en format XML

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<ET>A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks</ET>
<AU>DIMLA (D. E. JR); LISTER (P. M.); LEIGHTON (N. J.)</AU>
<AF>Engineering Research Group, SEBE, University of Wolverhampton/Royaume-Uni (1 aut., 2 aut., 3 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>IEE conference publication; ISSN 0537-9989; Royaume-Uni; Da. 1997; No. 440; Pp. 306-311; Bibl. 18 ref.</SO>
<LA>Anglais</LA>
<EA>The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such a manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable Tool Condition Monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realised through traditional tool changing philosophies. Unfortunately, the neural network algorithms used have been complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple Multi-layer Perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90% was attainable.</EA>
<CC>001D02C06; 001D12C01; 001D11C05A; 240</CC>
<FD>Métrologie; Monitorage; Usinage; Fusion donnée; Outil coupe; Force coupe; Vibration; Intelligence artificielle; Perceptron; Réseau neuronal; Réseau multicouche</FD>
<ED>Metrology; Monitoring; Machining; Data fusion; Cutting tool; Cutting force; Vibration; Artificial intelligence; Perceptron; Neural network; Multilayer network</ED>
<GD>Metrologie; Zerspanen; Zerspanungswerkzeug; Schnittkraft; Schwingung</GD>
<SD>Metrología; Monitoreo; Mecanizado; Fusión datos; Herramienta corte; Fuerza corte; Vibración; Inteligencia artificial; Perceptron; Red neuronal; Red multinivel</SD>
<LO>INIST-12497.354000068075400540</LO>
<ID>98-0101386</ID>
</server>
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