Title :
Artificial neural networks approach to tool condition monitoring in a metal turning operation
Author_Institution :
Sch. of Mech. & Offshore Eng., Robert Gordon´´s Inst. of Technol., Aberdeen, UK
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;
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
DOI :
10.1109/ETFA.1999.815371
