Abstract
The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.
Pan, Fu;
Hope, A D;
Javed, M
[1]
- Systems Engineering Faculty, Southampton Institute (United Kingdom)
Citation Formats
Pan, Fu, Hope, A D, and Javed, M.
An intelligent condition monitoring system for on-line classification of machine tool wear.
Finland: N. p.,
1997.
Web.
Pan, Fu, Hope, A D, & Javed, M.
An intelligent condition monitoring system for on-line classification of machine tool wear.
Finland.
Pan, Fu, Hope, A D, and Javed, M.
1997.
"An intelligent condition monitoring system for on-line classification of machine tool wear."
Finland.
@misc{etde_626295,
title = {An intelligent condition monitoring system for on-line classification of machine tool wear}
author = {Pan, Fu, Hope, A D, and Javed, M}
abstractNote = {The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.}
place = {Finland}
year = {1997}
month = {Dec}
}
title = {An intelligent condition monitoring system for on-line classification of machine tool wear}
author = {Pan, Fu, Hope, A D, and Javed, M}
abstractNote = {The development of intelligent tool condition monitoring systems is a necessary requirement for successful automation of manufacturing processes. This presentation introduces a tool wear monitoring system for milling operations. The system utilizes power, force, acoustic emission and vibration sensors to monitor tool condition comprehensively. Features relevant to tool wear are drawn from time and frequency domain signals and a fuzzy pattern recognition technique is applied to combine the multisensor information and provide reliable classification results of tool wear states. (orig.) 10 refs.}
place = {Finland}
year = {1997}
month = {Dec}
}