Application of Artificial Neural Net in Defect Image Recognizing of Cutting Chip

Abstract:

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Aiming at the problem of image recognition in the process of defected chip generation of automatic machining, fuzzy category methods of RBF net and eight neighborhood Euler numbers are researched in this paper. They are based on fuzzy theory and neural net. The gradient steepest descent of optimization theory is used and aberration is minimized by step between required output and actual output. By modifying studying algorithm, recognized capacity is increased. This method is tested in Matlab platform. It can be concluded that fraction of chip image under sophisticated surrounding may be recognized accurately through this net.

Info:

Periodical:

Key Engineering Materials (Volumes 315-316)

Edited by:

Zhejun Yuan, Xipeng Xu, Dunwen Zuo, Julong Yuan and Yingxue Yao

Pages:

496-500

DOI:

10.4028/www.scientific.net/KEM.315-316.496

Citation:

Y. L. Zhao et al., "Application of Artificial Neural Net in Defect Image Recognizing of Cutting Chip", Key Engineering Materials, Vols. 315-316, pp. 496-500, 2006

Online since:

July 2006

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Price:

$35.00

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