Please use this identifier to cite or link to this item:
|Title:||On-line cutting state recognition in turning Using a neural network|
|Authors:||Rahman, M. |
|Citation:||Rahman, M., Zhou, Q., Hong, G.S. (1995-03). On-line cutting state recognition in turning Using a neural network. The International Journal of Advanced Manufacturing Technology 10 (2) : 87-92. ScholarBank@NUS Repository. https://doi.org/10.1007/BF01179276|
|Abstract:||Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate. © 1995 Springer-Verlag London Limited.|
|Source Title:||The International Journal of Advanced Manufacturing Technology|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 13, 2018
WEB OF SCIENCETM
checked on Jul 4, 2018
checked on Jul 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.