Please use this identifier to cite or link to this item:
|Title:||Identification of feature set for effective tool condition monitoring - a case study in titanium machining|
|Authors:||Sun, J. |
|Source:||Sun, J., San, W.Y., Soon, H.G., Rahman, M., Zhigang, W. (2008). Identification of feature set for effective tool condition monitoring - a case study in titanium machining. 4th IEEE Conference on Automation Science and Engineering, CASE 2008 : 273-278. ScholarBank@NUS Repository. https://doi.org/10.1109/COASE.2008.4626410|
|Abstract:||Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximize machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analyzed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition. ©2008 IEEE.|
|Source Title:||4th IEEE Conference on Automation Science and Engineering, CASE 2008|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 7, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.