Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164916
Title: Deep learning based solder joint defect detection on industrial printed circuit board X-ray images
Authors: Zhang, Qianru
Zhang, Meng
Gamanayake, Chinthaka
Yuen, Chau
Geng, · Zehao
Jayasekara, Hirunima
Woo, Chia-wei
Low, Jenny
Liu, Xiang
Guan, Yong Liang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Zhang, Q., Zhang, M., Gamanayake, C., Yuen, C., Geng, ·. Z., Jayasekara, H., Woo, C., Low, J., Liu, X. & Guan, Y. L. (2022). Deep learning based solder joint defect detection on industrial printed circuit board X-ray images. Complex and Intelligent Systems, 8(2), 1525-1537. https://dx.doi.org/10.1007/s40747-021-00600-w
Journal: Complex and Intelligent Systems
Abstract: With the improvement of electronic circuit production methods, such as reduction of component size and the increase of component density, the risk of defects is increasing in the production line. Many techniques have been incorporated to check for failed solder joints, such as X-ray imaging, optical imaging and thermal imaging, among which X-ray imaging can inspect external and internal defects. However, some advanced algorithms are not accurate enough to meet the requirements of quality control. A lot of manual inspection is required that increases the specialist workload. In addition, automatic X-ray inspection could produce incorrect region of interests that deteriorates the defect detection. The high-dimensionality of X-ray images and changes in image size also pose challenges to detection algorithms. Recently, the latest advances in deep learning provide inspiration for image-based tasks and are competitive with human level. In this work, deep learning is introduced in the inspection for quality control. Four joint defect detection models based on artificial intelligence are proposed and compared. The noisy ROI and the change of image dimension problems are addressed. The effectiveness of the proposed models is verified by experiments on real-world 3D X-ray dataset, which saves the specialist inspection workload greatly.
URI: https://hdl.handle.net/10356/164916
ISSN: 2199-4536
DOI: 10.1007/s40747-021-00600-w
Schools: School of Electrical and Electronic Engineering 
Rights: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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