A Fuzzy Recognition of early Crack of Metal Deep Drawing Stamping Based on the Fuzzy Clustering

Ya Chun Dai; Jian Wei Xu; Dian Kai Huang; Jing Jing Zhang

doi:10.4028/www.scientific.net/AMR.926-930.26

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Optimal Number of Contact Angle Measurements on Pyrite Surface
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Characterization of Mg_xZn_1-xO Films Grown by Electrochemical Deposition
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A Fuzzy Recognition of early Crack of Metal Deep Drawing Stamping Based on the Fuzzy Clustering
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930A Fuzzy Recognition of early Crack of Metal Deep...

A Fuzzy Recognition of early Crack of Metal Deep Drawing Stamping Based on the Fuzzy Clustering

Abstract:

This paper was according to the early crack identification on metal deep drawing, combined with the characteristics of the AE singal of deep drawing forming, determined to analyze it by db4 wavelet basis function for 3 layers, extracted the energy value at every point of the eight AE signal samples from deep drawing forming which crack state was known to constituted characteristic matrix, fuzzy equivalence matrix was obtained by a series of matrix transformation,which was regard as pattern library of crack that used in cluster analysis. Then extracted energy value at every point of AE signal from deep drawing experiments with there states unknown, calculate similarity between samples and the model we established by using euclidean distance formula after normalization processing, deep drawing state was determined by the principle of selecting near, finally compared the recognition result with crack situation deviced by naked eye and microscope, results show that the accuracy of fuzzy identification of early crackn based on fuzzy clustering is up to 90%.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

26-31

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.26

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Online since:

May 2014

Authors:

Ya Chun Dai*, Jian Wei Xu, Dian Kai Huang, Jing Jing Zhang

Keywords:

Acoustic Emission (AE), Deep Drawing Forming, Fuzzy Clustering, Model Identification, Wavelet Transform (WT)

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