The Improved K-nearest Neighbor Solder Joints Defect Detection
Meiju Liu, Lingyan Li, Wenbo Guo
Available Online April 2016.
- https://doi.org/10.2991/emim-16.2016.136How to use a DOI?
- AOI; Feature extraction; Improved K-nearest neighbor algorithm; Solder joint defect detection
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/emim-16.2016.136 ID - Liu2016/04 ER -