Title :
CNC tool wear detection using neuro-fuzzy classification system
Author :
Moavenian, M. ; Moghaddam, E.T.
Author_Institution :
Ferdowsi University of Mashad, Iran
fDate :
June 28 2004-July 1 2004
Abstract :
Wide spread implementation of milling process in automated manufacturing has made an inevitable need for on-line knowledge of tool condition. The major problems facing are the complexity of the methods available for monitoring the tool wear and means of reliable sensing and data processing. The approach proposed in this paper has considered these while a fuzzy-neuro classification (ANFIS) for detection of tool wear employs simulated online data captured from X-axis drive of a CNC milling machine. Simulated results demonstrate successful detection of changes in tool wear state.
Keywords :
Adaptive control; Clustering algorithms; Computer numerical control; Data processing; Fuzzy sets; Fuzzy systems; Mathematical model; Mechanical engineering; Metalworking machines; Milling; Fuzzy Classification; Milling operation; Neuro-Fuzzy system; Tool Wear;
Conference_Titel :
Automation Congress, 2004. Proceedings. World
Conference_Location :
Seville
Print_ISBN :
1-889335-21-5
