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http://dx.doi.org/10.5302/J.ICROS.2015.15.0019

Defect Classification of Components for SMT Inspection Machines  

Lee, Jae-Seol (Research Center, Mirtec Co.)
Park, Tae-Hyoung (Department of Electronics Engineering, Chungbuk National University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.21, no.10, 2015 , pp. 982-987 More about this Journal
Abstract
The inspection machine in SMT (Surface Mount Technology) line detects the assembly defects such as missing, misalignment, loosing, or tombstone. We propose a new method to classify the defect types of chip components by processing the image of PCB. Two original images are obtained from horizontal lighting and vertical lighting. The image of the component is divided into two soldering regions and one packaging region. The features are extracted by appling the PCA (Principle Component Analysis) to each region. The MLP (Multilayer Perceptron) and SVM (Support Vector Machine) are then used to classify the defect types by learning. The experimental results are presented to show the usefulness of the proposed method.
Keywords
SMT (Surface Mount Technology); AOI (Automated Optical Inspection machine); defect classification; PCA; SVM;
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Times Cited By KSCI : 4  (Citation Analysis)
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