Detecting System of Ink Cells in Gravure Cylinder via Neural Network
We apply neural network to build up a detecting system of ink cells in gravure cylinder. First, ink cells images are gained in the images capturing device and histogram equalization. The edge of cells is extracted by use of Canny operator. We use different thresholds and experimental sigma values that compare to experimental results. Canny edge extraction operator is best when the value of sigma is 16. According to the image used in this research to determine the standard ink cells carving, the value of gaps d 0 equals 125, the value of dark tone s 0 equals 394, and so its standard value of gaps and dark tone are d 0 ± 10 and s 0 ± 10. The values of gravure outlets gaps and dark tone are measured, while d and s are in the scope of standard range, of which output 1 of the ink cells determined to pass and output 0 deemed to fail. Binarization images are obtained through adaptive threshold segmentation, which regards the value of gaps and dark tone as the characteristic value when they start to detect. Finally, we extract size and surface defects of ink cells for grading. Segmentation pictures are extracted by K-means clustering. The areas of ink cells are deemed to size characteristics. Then we classify the ink cells into two classes using neural network. The experimental results consider a neural network model that produces consequences.
KeywordsInk cells in gravure cylinder Detecting systems Neural network Value of gaps and dark tone
This work was supported by Project 60962007 of the National Science Foundation of China and Foundation of Kunming University of Science and Technology under Grant 2011-02.
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