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Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 16 | Page No.: 2814-2824
DOI: 10.3923/jas.2008.2814.2824
 
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The Deficiency Recognition in PCBA`s Automatic Optical Inspection System by Using Back-Propagation Network Method

Min-Chie Chiu, Long-Jyi Yeh and Che-Jung Hsu

Abstract:
To improve the precision in the recognition process, a new algorithm (the image division method-IDM) is proposed. Recently, to meet various client requisitions, a flexible manufacturing process applied to various products of smaller quantity has become the trend. Even though the above new method can improve deficiency recognition in PCBAs, a huge quantity of samples used in off-line training is still obligatory. Unfortunately, the method is not suitable for a process that includes various products of smaller quantity. Moreover, not all deficiencies can be fully recognized by a single algorithm. To overcome the above drawbacks and increase the recognition rate, a combination of these algorithms in conjunction with a neural network system, which will increase the recognition rate with fewer samples, is proposed. Consequently, results reveal that deficiency recognition can be improved when the IDM in conjunction with other AOI algorithms are linked with a neural network.
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How to cite this article:

Min-Chie Chiu, Long-Jyi Yeh and Che-Jung Hsu, 2008. The Deficiency Recognition in PCBA`s Automatic Optical Inspection System by Using Back-Propagation Network Method. Journal of Applied Sciences, 8: 2814-2824.

DOI: 10.3923/jas.2008.2814.2824

URL: https://scialert.net/abstract/?doi=jas.2008.2814.2824

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