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Research on Printing Defects Inspection of Solder Paste Images
Author(s) -
Min Qi,
Ting Yin,
Gong Cheng,
Yuelei Xu,
Hongying Meng,
Yi Wang,
Shanshan Cui
Publication year - 2022
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2022/8651956
Subject(s) - printed circuit board , solder paste , computer science , soldering , surface mount technology , automated optical inspection , robustness (evolution) , artificial intelligence , computer vision , process (computing) , automated x ray inspection , materials science , image processing , image (mathematics) , composite material , gene , chemistry , operating system , biochemistry
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. The 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. The 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.

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