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    Volume 45, Issue 3 (May 2017)

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

    (Received 16 January 2015; accepted 29 February 2016)

    Published Online: 24 May 2017

    CODEN: JTEVAB

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    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,

    Chan, B.-E.
    Extended Education on Information and Electrical Engineering, Feng Chia Univ. Taichung,

    Huang, Y.-C.
    Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia Univ., Taichung,

    Chen, H.-T.
    Altek Corp., Taipei,

    Lin, Y.-C.
    Dept. of Automatic Control Engineering, Feng Chia Univ. Taichung,


    Stock #: JTE20150015

    ISSN:0090-3973

    DOI: 10.1520/JTE20150015

    Author
    Title X-Ray Imaging Inspection System for Blind Holes in the Intermediate Layer of Printed Circuit Boards with Neural Network Identification
    Symposium ,
    Committee E28