DocumentCode :
617944
Title :
Detecting PCB component placement defects by genetic programming
Author :
Feng Xie ; Uitdenbogerd, Alexandra ; Song, Andrew
Author_Institution :
Sch. of Comput. Sci. & IT, RMIT Univ., Melbourne, VIC, Australia
fYear :
2013
fDate :
20-23 June 2013
Firstpage :
1138
Lastpage :
1145
Abstract :
A novel approach is proposed in this study, which is to evolve visual inspection programs for automatic defect detection on populated printed circuit boards. This GP-based method does not require knowledge of the layout design of a board, nor relevant domain knowledge such as lighting conditions and visual characteristics of the components. Furthermore, conventional image operators are not required to perform the detection. The experiments show that these evolved GP programs can identify all the faults while some suspicious areas are also highlighted. By this GP approach, manual inspection effort can be dramatically reduced. In addition, an evolved GP detection program can readily work on different types of boards without re-training.
Keywords :
automatic optical inspection; genetic algorithms; image processing; printed circuit layout; printed circuit manufacture; printed circuit testing; GP approach; GP programs; GP-based method; PCB component placement defect detection; automatic defect detection; genetic programming; image operators; layout design; lighting conditions; manual inspection effort; printed circuit boards; visual characteristics; visual inspection programs; Circuit faults; Educational institutions; Feature extraction; Inspection; Lighting; Printed circuits; Training; automatic optical inspection; defects detection; genetic programming; machine vision; printed circuit board;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
Type :
conf
DOI :
10.1109/CEC.2013.6557694
Filename :
6557694
Link To Document :
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