Multi sensor based drill wear monitoring using artificial neural network
| dc.contributor.author | Panda, Sudhansu Sekhar | |
| dc.date.accessioned | 2015-09-16T06:42:03Z | |
| dc.date.accessioned | 2023-10-26T09:44:59Z | |
| dc.date.available | 2015-09-16T06:42:03Z | |
| dc.date.available | 2023-10-26T09:44:59Z | |
| dc.date.issued | 2007 | |
| 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.identifier.other | ROLL NO.03610305 | |
| dc.identifier.uri | https://gyan.iitg.ac.in/handle/123456789/126 | |
| 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 |