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Proceedings Paper

Sensor fusion using neural network for cutting chip form monitoring
Author(s): Jihong Chen; Hanming Shi; Tiexia Huang; Ri-Yao Chen
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Paper Abstract

Detecting and monitoring techniques are the essential condition for continuous operation of unmanned manufacturing systems. Because of the complexity, randomness and fuzziness of cutting processes, traditional monitoring methods are unreliable, incapable of being repeated and narrow in applicability. The new strategy of integrating information from a variety of sensors called sensor fusion, is described. The neural networks are suitable for solving problems of integrating information for sensor fusion. In this paper, an intelligent monitoring scheme based on neural networks for recognizing chip types is established. Neural networks are used to integrate information from the cutting condition and multiple sensors. The correct recognizing rates are as high as 84 percent when different cutting regions are used for evaluation. It is shown that the advantage of sensor fusion is its ability to recognize and control the complex processes over a wide range of conditions.

Paper Details

Date Published: 22 September 1993
PDF: 5 pages
Proc. SPIE 2101, Measurement Technology and Intelligent Instruments, (22 September 1993); doi: 10.1117/12.156382
Show Author Affiliations
Jihong Chen, Huazhong Univ. of Science and Technology (China)
Hanming Shi, Huazhong Univ. of Science and Technology (China)
Tiexia Huang, Huazhong Univ. of Science and Technology (China)
Ri-Yao Chen, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 2101:
Measurement Technology and Intelligent Instruments
Li Zhu, Editor(s)

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