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A Machine Vision Based Automatic Optical Inspection System for Detecting Defects of PCBA

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

The process of manufacturing electronic printed circuit board assembly (PCBA) is a common process in technology companies. In particular, error checking of a PCBA with full components is a difficult job for human inspectors due to the high concentration and persistence. To solve this problem, many solutions have been studied, in which image processing technology has emerged as the best solution with advantages such as fast processing speed and high accuracy. However, the cost of these systems is often quite high for small and medium size companies. This paper presents a solution using image processing technology to check for missing components on complete PCBAs. Accordingly, a suitable algorithm is proposed for low-cost cameras, which allows building a simpler and more economical inspection process for both types of solder joints: through hole and surface mounting device (SMD). Consuming time to test a PCBA with nearly 50 components is less than 1 s. A cheap and easy-to-use Automatic Optical Inspection (AOI) system had been developed and used to test the actual PCBAs at the company.

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Acknowledgments

PCBAs used in this research were supported by Panasonic Viet Nam in Ho Chi Minh City, Viet Nam. The company is also using our developed AOI system for testing the real products.

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Authors and Affiliations

  1. Mechanical and Mechatronics Department, Eastern International University, Thu Dau Mot, Binh Duong Province, Vietnam

    Huan Ngoc Le & Thang Viet Nguyen

  2. School of Computing and Information Technology, Eastern International University, Thu Dau Mot, Binh Duong Province, Vietnam

    Narayan C. Debnath

Authors
  1. Huan Ngoc Le
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  2. Thang Viet Nguyen
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  3. Narayan C. Debnath
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Corresponding author

Correspondence to Huan Ngoc Le .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Le, H.N., Nguyen, T.V., Debnath, N.C. (2020). A Machine Vision Based Automatic Optical Inspection System for Detecting Defects of PCBA. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_45

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