期刊
OPTIK
卷 124, 期 20, 页码 4110-4116出版社
ELSEVIER GMBH
DOI: 10.1016/j.ijleo.2012.12.030
关键词
Automatic optical inspection; Feature selection; Genetic algorithm; Neural network; Solder joint; Printed circuit board (PCB)
类别
资金
- National Science Foundation for distinguished young scholars of China [50825504]
- United Fund of Natural Science Foundation of China and Guangdong province [U0934004]
- Fundamental Research Funds for the Central Universities [2012ZP0004]
- Project GDUPS
To improve the performance of automatic optical inspection (AOI), a neural network combined with genetic algorithm for the diagnosis of solder joint defects on printed circuit boards (PCBs) assembled in surface mounting technology (SMT) is presented. Six types of solder joint have been classified in respect to the reality in the manufacture. The images of solder joint under test are acquired and 14 features are extracted as input features for the classification. The neural network is easily become over-fitting because these input features are not independent of each other, so the genetic algorithm is introduced to select and remove redundant input features. The experimental results have proved that the neural network combined with genetic algorithm reduced the number of input feature and had a satisfying recognition rate. (c) 2013 Elsevier GmbH. All rights reserved.
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