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- W755872904 abstract "Drill wear is an important issue in manufacturing industries, which not only affects the surface roughness of the hole but also influences drill life. Therefore, replacement of a drill at an appropriate time is of significant importance. Flank wear in a drill depends upon the input parameters like speed, feed rate, drill diameter, thrust force, torque and chip thickness. Therefore, it sometimes becomes difficult to have a quantitative measurement of all the parameters and a qualitative description becomes easier. In those cases, a fuzzy neural network based prediction model becomes more useful in tool condition monitoring (TCM). This paper describes..." @default.
- W755872904 created "2016-06-24" @default.
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- W755872904 date "2011-01-01" @default.
- W755872904 modified "2023-09-27" @default.
- W755872904 title "Drill Wear Prediction Using Fuzzy Neural Network" @default.
- W755872904 doi "https://doi.org/10.1115/1.859926.paper21" @default.
- W755872904 hasPublicationYear "2011" @default.
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