Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF01179276
Title: On-line cutting state recognition in turning Using a neural network
Authors: Rahman, M. 
Zhou, Q.
Hong, G.S. 
Keywords: Chatter vibration
Chip breaking
Monitoring
Neural network
Tool wear
Issue Date: Mar-1995
Citation: Rahman, M., Zhou, Q., Hong, G.S. (1995-03). On-line cutting state recognition in turning Using a neural network. The International Journal of Advanced Manufacturing Technology 10 (2) : 87-92. ScholarBank@NUS Repository. https://doi.org/10.1007/BF01179276
Abstract: Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate. © 1995 Springer-Verlag London Limited.
Source Title: The International Journal of Advanced Manufacturing Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/58579
ISSN: 02683768
DOI: 10.1007/BF01179276
Appears in Collections:Staff Publications

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