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. Firstly, the ink cells images are gained in the images capturing device and histogram equalization. The edge of cells is extracted by using 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 d₀ equals 125, the value of dark tone s₀ equals 394, so its standard value of gaps and dark tone are d₀ ± 10 and s₀ ± 10. The value of gravure outlets gaps and dark tone are measured, while d and s is in the scope of standard range, which the output 1 of the ink cells determined to pass and the 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 detecting. 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 by using of neural network. The experimental results consider neural network model that produce consequences.