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Using computer theory to detect PCB defects in an IoT environment

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Abstract

All kinds of electronic products, such as motherboards, digital cameras, mobile phones and even chip products in manufacturing production processes, require some form of defect detection in an IoT environment. Shortcomings and improvements need to be found before a product is shipped, and various computer applications have been proposed to facilitate this, such as facial recognition, object tracking, digital picture processing and other forms of defect detection automation in an IoT environment, to reduce costs in the industrial production field. Various manufacturers have also invested in computer research aimed at replacing some labour-intensive aspects of key processes. This paper focuses on detection of printed circuit board defects, which plays a key role in the production process of general electronic products. In this study, computer theory was used as the basic architecture. This method can convert spatial domain signals into frequency domain signals, providing users with a specific frequency range. An algorithm is described here, capable of detecting defects in printed circuit boards, such as short circuits, protruding solder, solder dents and open circuits. In this study, the algorithm was used with wavelet transform technology to achieve the purpose of detecting PCB defects in an IoT environment. The results indicated that the technology in question is simple to operate and performs efficiently. It only requires an accurate picture to be inputted (after processing), containing the defect, in order to determine the location of the defect.

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Acknowledgements

The research was support by Dongguan Polytechnic Project: FPC Surface Defect Intelligent Detection System Based on Machine Vision (ZXF021) and the project of Guangdong 2021 Engineering Technology Research Center (2021GCZX016).

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Authors and Affiliations

  1. School of Electronic Information, Dongguan polytechnic, Dongguan, 523808, China

    Long Gao & Jian Yong Bian

  2. Network Center, Dongguan University of Technology, Dongguan, 523808, China

    Fen Zheng

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  1. Long Gao
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  2. Fen Zheng
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  3. Jian Yong Bian
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Correspondence to Fen Zheng.

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Gao, L., Zheng, F. & Bian, J.Y. Using computer theory to detect PCB defects in an IoT environment. J Supercomput 78, 18887–18914 (2022). https://doi.org/10.1007/s11227-022-04610-4

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