A path planning algorithm for PCB surface quality automatic inspection

Zheng Xiao (), Zhenan Wang (), Deng Liu and Hui Wang
Additional contact information
Zheng Xiao: Wuhan University of Technology
Zhenan Wang: Wuhan University of Technology
Deng Liu: Wuhan University of Technology
Hui Wang: Wuhan University of Technology

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 6, No 16, 1829-1841

Abstract: 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%.

Keywords: PCB image acquisition; ACA; Location adjustment; AOI path planning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-021-01766-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01766-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-021-01766-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().