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A path planning algorithm for PCB surface quality automatic inspection

Author

Listed:
  • Zheng Xiao

    (Wuhan University of Technology)

  • Zhenan Wang

    (Wuhan University of Technology)

  • Deng Liu

    (Wuhan University of Technology)

  • Hui Wang

    (Wuhan University of Technology)

Abstract

The surface quality inspection of industrial printed circuit board (PCB) is a vitally important link in its manufacturing process. To inspect surface defects of PCBs effectively, the automatic optical inspection (AOI) technology, in which the PCB image acquisition depends on the path planning method, is widely adopted by industry. It is regarded as a characteristic travelling salesman problem (TSP), which includes component clustering, location adjustment and algorithm adaptation optimization. In this paper, by improving the ant colony algorithm (ACA) algorithm, we devise a PCB image acquisition path planning model and the corresponding solving algorithms. Because the ACA encounters difficulty escaping from the local optimal solution, an improved ACA with a negative feedback mechanism is proposed that is able to obtain a better tour path with a higher probability. Aiming at the uncertainty of the local location of image acquisition windows, location adjustment methods are introduced to further shorten the path length and improve the image acquisition efficiency. Finally, via simulation experiments, the proposed global negative feedback ACA (GNF-ACA) can shorten the average length of the tour path by 1.7% without changing the time complexity. The three methods of location adjustment can further shorten the length of the tour path by 5.6%, 13.1% and 13.7%.

Suggested Citation

  • Zheng Xiao & Zhenan Wang & Deng Liu & Hui Wang, 2022. "A path planning algorithm for PCB surface quality automatic inspection," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1829-1841, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01766-3
    DOI: 10.1007/s10845-021-01766-3
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    References listed on IDEAS

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    1. Chettha Chamnanlor & Kanchana Sethanan & Mitsuo Gen & Chen-Fu Chien, 2017. "Embedding ant system in genetic algorithm for re-entrant hybrid flow shop scheduling problems with time window constraints," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1915-1931, December.
    2. Yabo Luo, 2017. "Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1801-1815, December.
    3. Chung-Feng Jeffrey Kuo & Tz-ying Fang & Chi-Lung Lee & Han-Cheng Wu, 2019. "Automated optical inspection system for surface mount device light emitting diodes," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 641-655, February.
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