DocumentCode
349845
Title
Artificial neural networks approach to tool condition monitoring in a metal turning operation
Author
Dimla, D.E.
Author_Institution
Sch. of Mech. & Offshore Eng., Robert Gordon´´s Inst. of Technol., Aberdeen, UK
Volume
1
fYear
1999
fDate
1999
Firstpage
313
Abstract
Presents a neural networks based cutting tool wear monitoring system for metal turning operations. Multilayer perceptron neural networks were used to distinguish and classify worn/sharp tool-states from online data acquired during turning test cuts. The networks classified the tool-states with an accuracy of just over 90% success
Keywords
computerised monitoring; condition monitoring; cutting; machine tools; multilayer perceptrons; artificial neural networks approach; metal turning operation; multilayer perceptron neural networks; tool condition monitoring; tool-states classification; Acoustic sensors; Artificial neural networks; Condition monitoring; Cutting tools; Electrical resistance measurement; Intelligent networks; Neural networks; Optical sensors; Sensor phenomena and characterization; Turning;
fLanguage
English
Publisher
ieee
Conference_Titel
Emerging Technologies and Factory Automation, 1999. Proceedings. ETFA '99. 1999 7th IEEE International Conference on
Conference_Location
Barcelona
Print_ISBN
0-7803-5670-5
Type
conf
DOI
10.1109/ETFA.1999.815371
Filename
815371
Link To Document