Serveur d'exploration sur l'opéra

Attention, ce site est en cours de développement !
Attention, site généré par des moyens informatiques à partir de corpus bruts.
Les informations ne sont donc pas validées.

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

Identifieur interne : 002A27 ( Main/Exploration ); précédent : 002A26; suivant : 002A28

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 :

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.


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

<record>
<TEI>
<teiHeader>
<fileDesc>
<titleStmt>
<title xml:lang="en" level="a">A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks</title>
<author>
<name sortKey="Dimla, D E Jr" sort="Dimla, D E Jr" uniqKey="Dimla D" first="D. E. Jr" last="Dimla">D. E. Jr Dimla</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>Engineering Research Group, SEBE, University of Wolverhampton</s1>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<wicri:noRegion>Engineering Research Group, SEBE, University of Wolverhampton</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Lister, P M" sort="Lister, P M" uniqKey="Lister P" first="P. M." last="Lister">P. M. Lister</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>Engineering Research Group, SEBE, University of Wolverhampton</s1>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<wicri:noRegion>Engineering Research Group, SEBE, University of Wolverhampton</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Leighton, N J" sort="Leighton, N J" uniqKey="Leighton N" first="N. J." last="Leighton">N. J. Leighton</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>Engineering Research Group, SEBE, University of Wolverhampton</s1>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<wicri:noRegion>Engineering Research Group, SEBE, University of Wolverhampton</wicri:noRegion>
</affiliation>
</author>
</titleStmt>
<publicationStmt>
<idno type="wicri:source">INIST</idno>
<idno type="inist">98-0101386</idno>
<date when="1997">1997</date>
<idno type="stanalyst">PASCAL 98-0101386 INIST</idno>
<idno type="RBID">Pascal:98-0101386</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000870</idno>
<idno type="wicri:Area/PascalFrancis/Curation">000C00</idno>
<idno type="wicri:Area/PascalFrancis/Checkpoint">000881</idno>
<idno type="wicri:doubleKey">0537-9989:1997:Dimla D:a:multi:sensor</idno>
<idno type="wicri:Area/Main/Merge">002B49</idno>
<idno type="wicri:Area/Main/Curation">002A27</idno>
<idno type="wicri:Area/Main/Exploration">002A27</idno>
</publicationStmt>
<sourceDesc>
<biblStruct>
<analytic>
<title xml:lang="en" level="a">A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks</title>
<author>
<name sortKey="Dimla, D E Jr" sort="Dimla, D E Jr" uniqKey="Dimla D" first="D. E. Jr" last="Dimla">D. E. Jr Dimla</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>Engineering Research Group, SEBE, University of Wolverhampton</s1>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<wicri:noRegion>Engineering Research Group, SEBE, University of Wolverhampton</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Lister, P M" sort="Lister, P M" uniqKey="Lister P" first="P. M." last="Lister">P. M. Lister</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>Engineering Research Group, SEBE, University of Wolverhampton</s1>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<wicri:noRegion>Engineering Research Group, SEBE, University of Wolverhampton</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Leighton, N J" sort="Leighton, N J" uniqKey="Leighton N" first="N. J." last="Leighton">N. J. Leighton</name>
<affiliation wicri:level="1">
<inist:fA14 i1="01">
<s1>Engineering Research Group, SEBE, University of Wolverhampton</s1>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</inist:fA14>
<country>Royaume-Uni</country>
<wicri:noRegion>Engineering Research Group, SEBE, University of Wolverhampton</wicri:noRegion>
</affiliation>
</author>
</analytic>
<series>
<title level="j" type="main">IEE conference publication</title>
<idno type="ISSN">0537-9989</idno>
<imprint>
<date when="1997">1997</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt>
<title level="j" type="main">IEE conference publication</title>
<idno type="ISSN">0537-9989</idno>
</seriesStmt>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Artificial intelligence</term>
<term>Cutting force</term>
<term>Cutting tool</term>
<term>Data fusion</term>
<term>Machining</term>
<term>Metrology</term>
<term>Monitoring</term>
<term>Multilayer network</term>
<term>Neural network</term>
<term>Perceptron</term>
<term>Vibration</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Métrologie</term>
<term>Monitorage</term>
<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>
</textClass>
</profileDesc>
</teiHeader>
<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>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Royaume-Uni</li>
</country>
</list>
<tree>
<country name="Royaume-Uni">
<noRegion>
<name sortKey="Dimla, D E Jr" sort="Dimla, D E Jr" uniqKey="Dimla D" first="D. E. Jr" last="Dimla">D. E. Jr Dimla</name>
</noRegion>
<name sortKey="Leighton, N J" sort="Leighton, N J" uniqKey="Leighton N" first="N. J." last="Leighton">N. J. Leighton</name>
<name sortKey="Lister, P M" sort="Lister, P M" uniqKey="Lister P" first="P. M." last="Lister">P. M. Lister</name>
</country>
</tree>
</affiliations>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Wicri/Musique/explor/OperaV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002A27 | SxmlIndent | more

Ou

HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 002A27 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Wicri/Musique
   |area=    OperaV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     Pascal:98-0101386
   |texte=   A multi-sensor integration method of signals in a metal cutting opera tion via application of multi-layer perceptron neural networks
}}

Wicri

This area was generated with Dilib version V0.6.21.
Data generation: Thu Apr 14 14:59:05 2016. Site generation: Thu Oct 8 06:48:41 2020