Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/487211
Title: | Multi sensor based drill wear monitoring using artificial neural network |
Researcher: | Panda, Sudhansu Sekhar |
Guide(s): | Chakraborty, Debabrata |
Keywords: | Engineering Engineering and Technology Engineering Mechanical |
University: | Indian Institute of Technology Guwahati |
Completed Date: | 2007 |
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 |
Pagination: | Not Available |
URI: | http://hdl.handle.net/10603/487211 |
Appears in Departments: | DEPARTMENT OF MECHANICAL ENGINEERING |
Files in This Item:
File | Description | Size | Format | |
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01_fulltext.pdf | Attached File | 7.06 MB | Adobe PDF | View/Open |
04_abstract.pdf | 94.55 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 175.95 kB | Adobe PDF | View/Open |
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