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http://bura.brunel.ac.uk/handle/2438/24381
Title: | Research on Printing Defects Inspection of Solder Paste Images |
Authors: | Qi, M Yin, T Cheng, G Xu, Y Meng, H Wang, Y Cui, S |
Keywords: | solder paste inspection;image interpolation;solder paste area detection;connected domain labeling |
Issue Date: | 25-Mar-2022 |
Publisher: | Hindawi Limited |
Citation: | Qi, M. et al. (2022) 'Research on Printing Defects Inspection of Solder Paste Images", Wireless Communications and Mobile Computing', 2022, Article ID 8651956, pp. 1 - 9. doi: 10.1155/2022/8651956. |
Abstract: | Copyright © 2022 Min Qi et al. Solder paste printing is the first part of the surface mount process flow; its postprinting defect inspection is particularly important. In this paper, we focus on studying the printing defects inspection algorithm for solder paste on PCB (Printed Circuit Board) images. +e work proposes a number of methods to enhance the defects inspection performance of solder paste printing: a regional multidirectional data fusion image interpolation method, which can achieve fast and high precision image interpolation; a method for detecting solder paste areas with better accuracy, efficiency, and robustness; an improved connected domain labeling method to reduce time complexity; and defects detection and types classification method, which extracts features and centroid of every solder paste region and completes the inspection by comparing with a standard image. +e experiments show that the defects inspection algorithm can detect the most common types of defects with low time consumption, high inspection accuracy, and classification accuracy. |
Description: | Data Availability: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. |
URI: | https://bura.brunel.ac.uk/handle/2438/24381 |
DOI: | https://doi.org/10.1155/2022/8651956 |
ISSN: | 1530-8669 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Copyright © 2022 Min Qi et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | 3.33 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License