A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks
Identifieur interne :
000C00 ( PascalFrancis/Curation );
précédent :
000B99;
suivant :
000C01
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 [
Royaume-Uni] ;
P. M. Lister [
Royaume-Uni] ;
N. J. Leighton [
Royaume-Uni]
Source :
-
IEE conference publication [ 0537-9989 ] ; 1997.
RBID : Pascal:98-0101386
Descripteurs français
- Pascal (Inist)
- Métrologie,
Monitorage,
Usinage,
Fusion donnée,
Outil coupe,
Force coupe,
Vibration,
Intelligence artificielle,
Perceptron,
Réseau neuronal,
Réseau multicouche.
- Wicri :
English descriptors
- KwdEn :
- Artificial intelligence,
Cutting force,
Cutting tool,
Data fusion,
Machining,
Metrology,
Monitoring,
Multilayer network,
Neural network,
Perceptron,
Vibration.
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.
| pA |
| A01 | 01 | 1 | | @0 0537-9989 |
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| A06 | | | | @2 440 |
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| 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 |
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| A09 | 01 | 1 | ENG | @1 Artificial neural networks : Cambridge, 7-9 July 1997 |
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| A11 | 01 | 1 | | @1 DIMLA (D. E. JR) |
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| 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 |
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| A21 | | | | @1 1997 |
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| A23 | 01 | | | @0 ENG |
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| A26 | 01 | | | @0 0-85296-690-3 |
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| A44 | | | | @0 0000 @1 © 1998 INIST-CNRS. All rights reserved. |
|---|
| A45 | | | | @0 18 ref. |
|---|
| A47 | 01 | 1 | | @0 98-0101386 |
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| A61 | | | | @0 A |
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| 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 | FRE | @0 Métrologie @5 01 |
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| C03 | 01 | X | ENG | @0 Metrology @5 01 |
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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 |
|---|
| C03 | 08 | X | FRE | @0 Intelligence artificielle @5 09 |
|---|
| C03 | 08 | X | ENG | @0 Artificial intelligence @5 09 |
|---|
| 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 |
|---|
|
|---|
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Le document en format XML
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</seriesStmt><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial intelligence</term>
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<term>Data fusion</term>
<term>Machining</term>
<term>Metrology</term>
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<term>Usinage</term>
<term>Fusion donnée</term>
<term>Outil coupe</term>
<term>Force coupe</term>
<term>Vibration</term>
<term>Intelligence artificielle</term>
<term>Perceptron</term>
<term>Réseau neuronal</term>
<term>Réseau multicouche</term>
</keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Métrologie</term>
<term>Intelligence artificielle</term>
</keywords><front><div type="abstract" xml:lang="en">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.</div>
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</fA11><fA11 i1="02" i2="1"><s1>LISTER (P. M.)</s1>
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</fA66><fC01 i1="01" l="ENG"><s0>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.</s0>
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