The Improved K-nearest Neighbor Solder Joints Defect Detection

DOI

10.2991/emim-16.2016.136How to use a DOI?

Keywords

AOI; Feature extraction; Improved K-nearest neighbor algorithm; Solder joint defect detection

Abstract

Aiming at the problems such as defect misstatements, omissions are prone to happen when automatic optical inspection (AOI) system detects Printed Circuit Board (PCB) solder joints. The article puts forward a kind of method based on improved K-nearest neighbor to test and classify the quality of solder joints.Firstly, the original images collected by industrial camera should be pretreated, and solder joints should be positioned by using the method of template matching.Secondly, the features of solder joints should be extracted and selected usefully through the experiments. Finally, the improved K-nearest neighbor algorithm based on effective feature is used to test and classify solder joints. Experiments show that the improved K-nearest neighbor algorithm has higher accuracy and stronger adaptability thanneural network algorithm used for classification. What’s more, the cost of testing is also reduced effectively. So we can conclude thatthe improved K-nearest neighbor algorithm is useful for solder joints testing.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Meiju Liu
AU  - Lingyan Li
AU  - Wenbo Guo
PY  - 2016/04
DA  - 2016/04
TI  - The Improved K-nearest Neighbor Solder Joints Defect Detection
BT  - Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society
PB  - Atlantis Press
SP  - 651
EP  - 657
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.136
DO  - 10.2991/emim-16.2016.136
ID  - Liu2016/04
ER  -