Journal Published Online: 14 April 2016
Volume 45, Issue 3

X-Ray Imaging Inspection System for Blind Holes in the Intermediate Layer of Printed Circuit Boards with Neural Network Identification

CODEN: JTEVAB

Abstract

This study presented an X-ray imaging inspection system with a backpropagation neural network that could increase the accuracy of defect detection and classification of blind holes in the intermediate layer of printed circuit boards (PCBs). In this system, a multilayer PCB image was obtained from an X-ray camera. The original image was then converted into a binary image with a noise-suppression filter, and the edge-detection method was used to compare the image with a standard sample. Drilling was based on the hole-position's accuracy measurement to obtain the hole flak figure, which was useful for calculating the drilling coordinate error with a backpropagation neural network. The proposed method could determine the information of the PCB edge test holes automatically. The accuracy of the feature extraction was increased by using the proposed module-detection method, together with image processing and the backpropagation networks process.

Author Information

Lin, C.-S.
Dept. of Automatic Control Engineering, Feng Chia Univ. Taichung, TW
Chan, B.-E.
Extended Education on Information and Electrical Engineering, Feng Chia Univ. Taichung, TW
Huang, Y.-C.
Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia Univ., Taichung, TW
Chen, H.-T.
Altek Corp., Taipei, TW
Lin, Y.-C.
Dept. of Automatic Control Engineering, Feng Chia Univ. Taichung, TW
Pages: 11
Price: $25.00
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Details
Stock #: JTE20150015
ISSN: 0090-3973
DOI: 10.1520/JTE20150015