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X射線影像檢測系統於內部層組成元件之品質分析

建立日期：2018/03/09
  • 作者： 逢甲大學自動控制工程學系-林宸生、詹炳恩；逢甲大學電機工程學系-陳柏志、田春林
  • 出處： 2016 AOI論壇與展覽
  • 內容： In this study, an X-ray imaging inspection system with back-propagation neural network (BPN) is presented. The proposed system can increase the accuracy in the defect detection and classification for the blind hole in interlayer of printed circuit board (PCB). In the proposed system, the multilayer PCB image is obtained from X-ray camera. Then the original image is converted into a binary image with a noise suppression filter. Next, edge detection method is used to be compared with a standard sample. The drilling is based on hole position accuracy measurement to obtain hole flak figure which helps to calculate the drilling coordinate error. It can find out the PCB edge test holes information automatically. Finally, the accuracy of the results in the feature extraction is increased by using proposed module detection method with integration of image processing and the process of BPN.
  • 檔案下載：全文下載
  • 下載次數：37
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