Skip to main content
SpringerLink
Log in

Automated SMD LED inspection using machine vision

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Light-emitting diodes (LED) are used in many different applications. However, some LED defects are unavoidable in large-volume fabrication and taping processes. These defects may include missing components, incorrect orientations, inverse polarity, mouse bites, missing gold wires, and surface stains. Human visual inspection has traditionally been used in LED-packaging factories. However, it is subjective, time consuming, and lacking consistent inspection results. This paper proposes a machine vision system combining an automatic system-generated inspection regions (IR) method to inspect two types of LED surface-mounted devices (SMDs). Experimentation revealed that the proposed automatic inspection method could successfully detect defects with up to 95% accuracy for both types (Types 1 and 2) of SMD LEDs. The online inspecting speed was on average under 0.3 s per image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chang YC, Ou CJ, Tsai YS, Juang FS (2009) Nonspherical LED packaging lens for uniformity improvement. Opt Rev 16(3):323–325. doi:10.1007/s10043-009-0059-7

    Article  Google Scholar 

  2. Reinhard E, Khan EA, Akyüz AO, Johnson G (2010) Color imaging: fundamentals and applications. AK Peters, Cambridge, p 518

    Google Scholar 

  3. Orton KR (1994) Sufrace-Monut LED. Available via Google Patents. http://www.google.com.tw/patents?id=3A0aAAAAEBAJ. Accessed 10 Dec 2010

  4. Watanabe S, Ogawa Y (2006) Surface Mount LED. Available via Google Patents. http://www.google.com.tw/patents/about?id=xP65AAAAEBAJ. Accessed 10 Dec 2010

  5. Newman TS, Jain AK (1995) A Survey of automated visual inspection. Comput Vis Image Underst 61(2):231–262. doi:10.1006/cviu.1995.1017

    Article  Google Scholar 

  6. Malamas EN, Petrakis EGM, Zervakis M, Petit L, Legat JD (2003) A survey on industrial vision systems, applications and tools. Image Vis Comput 21(2):171–188. doi:10.1016/S0262-856(02)00152-XDOI:dx.doi.org

    Article  Google Scholar 

  7. Chang CY, Lin SY, Jeng MD (2009) Application of Two Hopfield Neural Networks for Automatic Four-Element LED Inspection. IEEE Trans Syst Man Cybern Part C-Appl Rev 39(3):352–365. doi:10.1109/TSMCC.2009.2013817 DOI:dx.doi.org

    Article  Google Scholar 

  8. Golnabi H, Asadpour A (2007) Design and application of industrial machine vision systems. Robot Comput-Integr Manuf 23(6):630–637. doi:10.1016/j.rcim.2007.02.005

    Article  Google Scholar 

  9. Wu WY, Wang M-JJ, Liu CM (1996) Automated inspection of printed circuit boards through machine vision. Comput Ind 28(2):103–111. doi:10.1016/0166-3615(95)00063-1 DOI:dx.doi.org

    Article  Google Scholar 

  10. Perng DB, Liu CP, Chen YC, Chou CC (2002) Advanced SMD PCB vision inspection machine development. 15th IPPR Conf Comput Vis Graph Image Process, pp 311–317

  11. Rau H, Wu CH (2005) Automatic optical inspection for detecting defects on printed circuit board inner layers. Int J Adv Manuf Technol 25(9–10):940–946. doi:10.1007/s00170-004-2299-9

    Article  Google Scholar 

  12. Jiang BC, Wang CC, Hsu YN (2007) Machine vision and background remover-based approach for PCB solder joints inspection. Int J Prod Res 45(2):451–464. doi:10.1080/00207540600607184

    Article  Google Scholar 

  13. Perng DB, Chen SH, Chang YS (2010) A novel internal thread defect auto-inspection system. Int J Adv Manuf Technol 47(5–8):731–743. doi:10.1007/s00170-009-2211-8

    Article  Google Scholar 

  14. Peng XQ, Chen YP, Yu WY, Zhou Z, Sun G (2008) An online defects inspection method for float glass fabrication based on machine vision. Int J Adv Manuf Technol 39(11–12):1180–1189. doi:10.1007/s00170-007-1302-7

    Article  Google Scholar 

  15. Carvalho P, Araujo H, Dourado A (1999) An automatic optical sensor for vessels and fibbers quality inspection in pulp production. Comput Ind Eng 37(1–2):355–358. doi:10.1016/S0360-8352(99)00092-3

    Article  Google Scholar 

  16. Perng DB, Chou CC, Lee SM (2007) Design and development of a new machine vision wire bonding inspection system. Int J Adv Manuf Technol 34(3–4):323–334. doi:10.1007/s00170-006-0611-6

    Article  Google Scholar 

  17. Cheng HD, Jiang XH, Sun Y, Wang J (2001) Color image segmenation: advances and prospects. Pattern Recognit 34(12):2259–2281. doi:10.1016/S0031-3203(00)00149-7

    Article  MATH  Google Scholar 

  18. Munoz X, Freixenet J, Cufi X, Marti J (2003) Strategies for image segmentation combining region and boundary information. Pattern Recognit Lett 24(1–3):375–392. doi:10.1016/S0167-8655(02)00262-3

    Article  Google Scholar 

  19. Revol C, Jourlin M (1997) A new minimum variance region growing algorithm for image segmentation. Pattern Recognit Lett 18(3):249–258. doi:10.1016/S0167-8655(97)00012-3

    Article  Google Scholar 

  20. Haralick RM, Shapiro LG (1985) Image segmentation techniques. Comput Vis Graph Image Processing 29(1):100–132. doi:10.1016/S0734-189X(85)90153-7

    Article  Google Scholar 

  21. Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern SMC 9(1):62–66

    Article  MathSciNet  Google Scholar 

  22. Gonzalez RC, Woods RE (2008) Digital image processing (3rd edn). Prentice Hall, Upper Saddle River, pp 635–639

    Google Scholar 

  23. Haralick RM, Shapiro LG (1992) Computer and Robot Vision. Addison-Wesley, Massachusetts 1:73–75

    Google Scholar 

  24. Shapiro LG, Stockman GC (2001) Computer Vision. Prentice Hall, Upper Saddle River, p 330

    Google Scholar 

  25. Rosenfeld A, Kak AC (1982) Digital Picture Processing (2nd ed). Academic Press, New Jersey 2:33–36

    Google Scholar 

  26. Montgomery DC (2005) Introduction to statistical quality control (5th ed). Wiley, New York, p 153

    MATH  Google Scholar 

  27. Carsten S, Markus U, Christian W (2008) Machine Vision Algorithms and Applications. Wiley, Weinheim

    Google Scholar 

  28. Chang CC (2010) An AOI system for side-view SMD-LED defect inspection. Master thesis, Department of Industrial Engineering and Management, National Chiao Tung University, Taiwan

Download references

Author information

Authors and Affiliations

  1. Department of Multimedia and Game Science, Yu-Da University, Miao-Li, 36143, Taiwan, People’s Republic of China

    Der-Baau Perng

  2. Department of Industrial Engineering and Management, National Chiao Tung University, Hsin-Chu, 30010, Taiwan, People’s Republic of China

    Hsiao-Wei Liu & Ching-Ching Chang

Authors
  1. Der-Baau Perng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsiao-Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ching-Ching Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Der-Baau Perng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Perng, DB., Liu, HW. & Chang, CC. Automated SMD LED inspection using machine vision. Int J Adv Manuf Technol 57, 1065–1077 (2011). https://doi.org/10.1007/s00170-011-3338-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3338-y

Keywords

Search

Navigation