Abstract
The importance of the inspection has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, especially in the printed circuit board (PCB) industry, 100% quality assurance of all work-in-process and finished goods is required in order to reduce the scrap costs and re-work rate. One of the challenges for PCB inspection is in the use of a surface mount device (SMD) placement check. Missing, misaligned or wrongly rotated components are the critical causes of defects. To prevent the PCB from having these defects, inspection must be done before the solder reflow process commences, otherwise, everything will be too late. The research reported in this paper concentrates on automatic object searching techniques, in a grey-scale captured image, for locating multiple components on a PCB. The presented approach includes the normalized cross correlation (NCC) based multi-template matching (MTM) method. The searching process has been carried out by using the proposed accelerated species based particle swarm optimization (ASPSO) method and the genetic algorithm (GA) approach as a reference. The experimental results of the ASPSO-based MTM approaches are reported.
Similar content being viewed by others
References
Aksoy M.S., Torkul O. and Cedimoglu I.H. (2004). An industrial visual inspection system that uses inductive learning. Journal of Intelligent Manufacturing 15(4): 569–574
Beasley D., Bull D.R. and Martin R.R. (1993). A sequential niche technique for multimodal function optimization. Evolutionary Computation 1(2): 101–125
Brits R., Engelbrecht A.P. and van den Bergh F. (2007). Locating multiple optima using particle swarm optimization. Applied Mathematics and Computation 189(2): 1859–1883
Cagnoni S., Mordonini M. and Sartori J. (2007). Particle swarm optimization for object detection and segmentation. Lecture Notes in Computer Science 4448: 241–250
Crispin A.J. and Rankov V. (2006). Automated inspection of PCB components using a genetic algorithm template-matching approach. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-006-0730-0.
Duda R.O. and Hart P.E. (1973). Pattern classification and scene analysis. Wiley, New York
Goldberg D.E. and Richardson J. (1987). Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette, J.J. (eds) Genetic algorithms and their applications, pp 41–49. Hillsdale, Lawrence Erlbaum, New Jersey
Gonzalez R.C. and Woods R.E. (1992). Digital image processing (3rd ed.). Addison-Wesley, Reading, Massachusetts
Hata, S. (1990). Vision systems for PCB manufacturing in Japan. In Proceedings of the 16th Annual Conference of IEEE Industrial Electronics Society (IECON ’90) (pp. 792–797).
Holland J.H. (1975). Adaptation in natural and artificial systems. The University of Michigan Press, Michigan
Hou, Z. X., Zhou, Y., & Li, H. Q. (2007). Multimodal function optimization based on multigrouped mutation particle swarm optimization. In Proceedings of the 3rd International Conference on Natural Computation (ICNC 2007), Haikou, China.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (ICNN) (Vol.4, pp. 1942–1948). Perth, Australia.
Li, X. D. (2004). Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In Genetic and Evolutionary Computation 2004 (GECCO 2004) (pp. 105–116). Seattle.
Li, X. D. (2007). A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (pp. 78–85). London.
Li J.P., Balazs M.E., Parks G. and Clarkson P.J. (2002). A species conserving genetic algorithm for multimodal function optimization. Evolutionary Computation 10(3): 207–234
Li D. and Yu C.F. (2006). The application of genetic algorithm in detecting printed circuit board components. Journal of Fudan University (Natural Science) 45(4): 452–456
Ling, Q., Wu, G., & Wang, Q. (2005). Restricted evolution based multimodal function optimization in holographic grating design. In IEEE Congress on Evolutionary Computation 2005 (pp. 789–794). München: IEEE Press.
Loh H.H. and Lu M.S. (1999). Printed circuit board inspection using image analysis. IEEE Transactions on Industrial Application 35(2): 426–432
Mahfoud, S. W. (1992). Crowding and preselection revisited. In R. Manner & B. Manderick (Eds.), Parallel problem solving from nature (Vol. 2, pp. 27–36). Amsterdam: Elsevier Science.
Mashohor, S., Evans, J. R., & Arslan, T. (2004). Genetic algorithm based printed circuit board (PCB) inspection system. In Consumer Electronics, 2004 IEEE International Symposium (pp. 519–522). Sept 1–3.
Mitchell M. (1996). An introduction to genetic algorithms. MIT Press, Cambridge
Moganti M., Ercal F., Dagli C.H. and Tsunekawa S. (1996). Automated PCB inspection algorithms: A survey. Computer Vision and Image Understanding 63(2): 287–313
Onwubolu G.C. and Babu B.V. (2004). New optimization techniques in engineering. Springer, New York
Parrott D. and Li X. (2006). Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Transactions on Evolutionary Computation 10(4): 440–457
Parsopoulos K.E. and Vrahatis M.N. (2004). On the computation of all global minimizers through particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3): 211–224
Seo J.H., Im C.H., Heo C.G., Kim J.K., Jung H.K. and Lee C.G. (2006). Multimodal function optimization based on particle swarm optimization. IEEE Transactions on Magnetics 42(4): 1095–1098
Seul M., O’Gorman L. and Sammon M.J. (2000). Practical Algorithms references for image analysis: Description, examples and code. Cambridge University Press, Cambridge
Smith R.E., Forrest S. and Perelson A.S. (1992). Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation 1(2): 127–149
Stefano, L. D., Mattoccia, S., & Tombari, F. (2004). An algorithm for efficient and exhaustive template matching. In International Conference on Image Analysis and Recognition 2004 (ICIAR 2004) (pp. 408–415).
Ting T.O., Rao M.V.C., Loo C.K. and Ngu S.S. (2003). Solving unit commitment problem using hybrid particle swarm optimization. Journal of Heuristics 9: 507–520
Wachowiak M.K., Smolikova R., Zheng Y., Zurada J.M. and Elmaghraby A.S. (2004). An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3): 289–301
Author information
Authors and Affiliations
Corresponding author
Additional information
An erratum to this article can be found at http://dx.doi.org/10.1007/s10845-008-0235-9
Rights and permissions
About this article
Cite this article
Wu, CH., Wang, DZ., Ip, A. et al. A particle swarm optimization approach for components placement inspection on printed circuit boards. J Intell Manuf 20, 535–549 (2009). https://doi.org/10.1007/s10845-008-0140-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-008-0140-2