Multi sensor based drill wear monitoring using artificial neural network

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dc.contributor.author Panda, Sudhansu Sekhar
dc.date.accessioned 2015-09-16T06:42:03Z
dc.date.available 2015-09-16T06:42:03Z
dc.date.issued 2007
dc.identifier.other ROLL NO.03610305
dc.identifier.uri http://gyan.iitg.ernet.in/handle/123456789/126
dc.description Supervisor: Debabrata Chakroborty en_US
dc.description.abstract Tool condition monitoring(TCM) is one of the most important activities in modern manufacturing activities. proper implementation of TCM system not only prevents catastrophic failure of tool but also increases the productivity of the industries. Drilling is one of the most common machining operations used in industries and hence monitoring of the drilling condition is of significationt importance in industries. Among different causes of drilling failure gradual wear of the drilling is unavoidable and needs to be monitored to avoid sudden failure of the drill. en_US
dc.language.iso en en_US
dc.relation.ispartofseries TH-0490;
dc.subject MECHANICAL ENGINEERING en_US
dc.title Multi sensor based drill wear monitoring using artificial neural network en_US
dc.type Thesis en_US


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