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Proceedings Paper

Methods of recognizing chip shape based on neural net
Author(s): Xianli Liu; Qiaoling Yuan; Liguo Zhang; Fugang Yan
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Paper Abstract

Aiming at the problem of process monitoring on chip generating in automatic machining, methods of recognizing chips' shape based on neural net are researched in this paper. The conception of area ratio of the chip image to the located window is defined, the area ratio feature has been proposed because the size of all windows and the direction of chips are respectively same. At the same time, the Euler number characteristic and disperse degree characteristic of the chip image have been worked out. The above geometry characteristics of the chip image are chosen as input vectors of neural network, and the 50 various images of each type such as C shape, spiral shape and disorderly shape are chosen as training sample, the recursion least square law is used to train network. The recognition rate and training time of the BP network are compared with those of the RBF network, so the conclusion that the RBF network is superior to the BP network at the aspect of chip shape recognition has got, and the relevant computer program has been developed, which possess good real-time application and adaptability by way of the experiment certification. The recognition rate achieves more than 90%.

Paper Details

Date Published: 8 February 2005
PDF: 5 pages
Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005); doi: 10.1117/12.577000
Show Author Affiliations
Xianli Liu, Harbin Univ. of Science and Technology (China)
Qiaoling Yuan, Zhejiang Univ. of Technology (China)
Liguo Zhang, Zhejiang Univ. of Technology (China)
Fugang Yan, Harbin Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 5637:
Electronic Imaging and Multimedia Technology IV
Chung-Sheng Li; Minerva M. Yeung, Editor(s)

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