Artificial intelligence

Abstract: A hybrid algorithm for the PCB topology control and classification of defects are proposed. The algorithm is based on the comparing of connection tables of etalon and tested PCBs. Connection table is produced by partitioning the image of tracks into elementary fragments with skeleton transform. A peculiar feature of the algorithm is a large number of types of defects that are detected during inspection (defects of the logical structure, defects of the shape of elements, defects of the relative position) and good paralleling possibility.

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References:

1. Lecklider T. PCB Inspection Outlook for 2005.www.evaluationengineering.com/archive/articles/1204/1204pcb_inspection.asp
2. Elias N. A survey on industrial vision systems, applications, tools. Vol 21. Issue 2. Image Vision Computing2003. P 171-188
3. K. C. Fan. Strategic planning of developing automatic optical inspection (AOI) technologies in Taiwan,J. Phys.: Conf. 2005 Ser. 13. 2005. P 394-397
4. Zipunnikov V. Komponenty i tehnologii http://www.kit-e.ru/assets/files/pdf/2004_04_208.pdf
5. Kucherjavyj A. Komponenty i tehnologii http://www.kit-e.ru/assets/files/pdf/2006_06_8.pdf
6. Galeckij F. Jelektronnye komponenty. №8. 2003. S 51-55
7. GOST 20406-75 Platy pechatnye. Terminy i opredelenija
8. GOST27716-88 Fotoshablony pechatnyh plat. Obshhie tehnicheskie uslovija
9. Moganti M. A Survey, Computer Vision and Image Understanding № 63. 1996. P 287-313
10. Pirogova E. V. Proektirovanie i tehnologija pechatnyh plat: Uchebnik. M.: FORUM: INFRA-M. 2005. 560 s.
11. Efrat T. Printed Circuit Board Inspection http://visl.technion.ac.il/projects/2002w23
12. Jae-Young Pyun. Real-Time Machine Vision System for Printed Circuit Boards. http://dali.korea.ac.kr/publication/int_pro/paper/6.pdf
13. Zuwairie I. Wavelet-Based Printed Circuit Board Inspection System, International Journal of SignalProcessing (IJSP). № 1. 2004. P 65-71
14. Fuming Wang. Proc. SPIE. Vol 6357, 635719. 2006
15. Masayasu I., Pattern Inspection of a Printed Circuit Board using Graph Information, Transaction of IEEJapan, 112-C (2). 1992. P 102-111
16. Ahmed M. A Rule Based Approach for Visual Pattern Inspection, IEEE Transactions of Pattern Analysisand Machine Intelligence, PAMI10 (1). 1988. P. 56-68
17. Yung-Nien Sun. A New ModelBased Approach for Industrial Visual Inspection, Pattern Recognition. №25 (11). 1992. P 1327-1336
18. Heriansyah R. Jurnal Teknologi. 39 (D). 2003. P 84-104
19. Tatibana M. H. Proceedings of “X Brazilian Symposium Computer Graphics and Image ProcessingSIBGRAPI'97”. Campos de Jordgo. 1997. P 187-194
20. Göktürk, S.B. Proceedings of IEEE/EURASIP Workshop “Nonlinear Signal Processing and ImageProcessing”. Antalya. 1999
21. Fabiana R. ABCM Symposium Series in Mechatronics. № 3. 2008. P 623-632
22. Szolgay P. International Journal of Circuit Theory and Applications. № 27. 1999. P 103-116
23. Hidvegi Т. Int. J. of Circuit Theory and Applications. № 30. 2002. P.231-245.
24. Feijun Song. Opt. Eng. № 41 (1). 2002.
25. F. Ercal, F. In Proc. “SPIE Conf. On Architectures, networks, and intelligent systems for manufacturingintegration”. Pittsburg: PA. Oct. 1997. P. 49-59.
26. Ercal F. Proc. “SPIE Conf. on Intelligent systems in design and manufacturing”. Boston: MA. Nov.1998. P. 286-293.
27. Abdelhameed I. Opt. Eng. № 49. 2010.
28. Mandeville J. R. IBM J. Res. Develop. № 29 (1). 1985. P. 73-86.
29. Benhabib B. An Experimental System, International Journal of Robotic and Automation. № 5 (2). 1990
30. Dougherty E.R. An introduction to morphological image processing. Washington: Bellingham. 1992. 161 p.
31. Doudkin A.A. International Journal of Computing. № 5 (3). 2006. P. 107-111.