Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network-Reference-Cited by-同舟云学术

Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network

Published:2000-06 Issue:3 Volume:13 Page:249-261
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Kuo R.J.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference43 articles.

1. Sensor integration for tool wear estimation in machining;Chryssolouris,1988

2. An experimental study of strategies for integrating sensor information in machining;Chryssolouris;Annals of the CIRP,1989

3. Chryssolouris, G., Domroese, M., Beaulieu, P., 1991. A statistical approach to sensor synthesis. Transactions of the North American Manufacturing Research Institution of SME, 333–337

4. Sensor synthesis for control of manufacturing processes;Chryssolouris;ASME Journal of Engineering for Industry,1992

5. Force parameters for on-line tool wear estimation: a neural network approach;Dan;Neural Networks,1996

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