Skip to main content
SpringerLink
Log in

A computer vision-based assistant system for the assembly of narrow cabin products

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

Abstract

Due to the narrowness of space and the complexity of structure, the assembly of cabin parts has become one of the major bottlenecks in the whole manufacturing process. This paper presents a computer vision-based assistant system that integrates assembly planning, assembly training and guidance, assembly status inspection, and assembly quality evaluation to improve the assembly efficiency and quality of cabin products. By using a novel real-time 3D object registration approach, the mixed reality technology is applied to assembly training and guidance of cabin parts. A machine vision-based custom-made inspection system is designed for assembly surplus detection and gap measurement in half-enclosed cabin space. A cabin assembly quality model is proposed on the basis of Markov chain to quantitatively evaluate the cabin assembly quality. A case study is given to demonstrate the practical application of the proposed system.

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. Seth A, Vance JM, Oliver JH (2011) Virtual reality for assembly methods prototyping: a review. Virtual Reality 15(1):5–20

    Article  Google Scholar 

  2. Xu LD, Wang CE, Bi ZM, Yu JP (2012) AutoAssem: an automated assembly planning system for complex products. IEEE Trans Ind Inform 8(3):669–678

    Article  Google Scholar 

  3. Jiménez P (2013) Survey on assembly sequencing: a combinatorial and geometrical perspective. J Intell Manuf 24(2):235–250

    Article  Google Scholar 

  4. Zhou W, Zheng JR, Yan JJ, Wang JF (2011) A novel hybrid algorithm for assembly sequence planning combining bacterial chemotaxis with genetic algorithm. Int J Adv Manuf Technol 52(5–8):715–724

    Article  Google Scholar 

  5. Gao L, Qian WR, Li XY, Wang JF (2010) Application of memetic algorithm in assembly sequence planning. Int J Adv Manuf Technol 49(9–12):1175–1184

    Article  Google Scholar 

  6. Wang QH, Li JR, Gong HQ (2006) A CAD-linked virtual assembly environment. Int J Prod Res 44(3):467–486

    Article  Google Scholar 

  7. Xia PJ, Antonio ML, Maria TR, Yao YX (2012) A new type haptics-based virtual environment system for assembly training of complex products. Int J Adv Manuf Technol 58(1–4):379–396

    Article  Google Scholar 

  8. Carmigniani J, Furht B, Anisetti M, Ceravolo P, Damiani E, Lvkovic M (2011) Augmented reality technologies, systems and applications. Multimedia Tools Appl 51(1):341–377

    Article  Google Scholar 

  9. Jayaram S, Wang Y, Jayaram U, Lyons K, Hart P (1999) VADE: a virtual assembly design environment. IEEE Comput Graph Appl 19(6):44–50

    Article  Google Scholar 

  10. Brough JE, Schwartz M, Gupta SK, Anand DK, Kavetsky R, Pettersen R (2007) Towards the development of a virtual environment-based training system for mechanical assembly operations. Virtual Reality 11(4):189–206

    Article  Google Scholar 

  11. Wan H, Gao S, Peng Q, Zhang F (2004) MIVAS: A multi-modal immersive virtual assembly system. ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, September, Salt Lake City, 1–10

  12. Wu DL, Zhen XJ, Fan XM, Hu Y, Zhu HM (2012) A virtual environment for complex products collaborative assembly operation simulation. J Intell Manuf 23(3):821–833

    Article  Google Scholar 

  13. Schwald B, De LB (2003) An augmented reality system for training and assistance to maintenance in the industrial context. J WSCG 1(1):425–432

    Google Scholar 

  14. Didier JY, Roussel D, Mallem M, Otmane S, Naudet S, Pham QC, Bourgeois S, Megard C, Leroux C, Hocquard A (2005) AMRA: augmented reality assistance in train maintenance tasks. The 4th ACM/IEEE International Symposium on Mixed and Augmented Reality, Vienna

  15. Ong SK, Wang ZB (2011) Augmented assembly technologies based on 3D bare-hand interaction. CIRP Ann Manuf Technol 60(1):1–4

    Article  Google Scholar 

  16. Ong SK, Pang Y, Nee AYC (2007) Augmented reality aided assembly design and planning. CIRP Ann Manuf Technol 56(1):49–52

    Article  Google Scholar 

  17. Yuan ML, Ong SK, Nee AYC (2008) Augmented reality for assembly guidance using a virtual interactive tool. Int J Prod Res 46(7):1745–1767

    Article  MATH  Google Scholar 

  18. Meyn S, Sean P, Richard L (2009) Markov chains and stochastic stability. Cambridge University Press

  19. Taguchi G, Elsayed EA, Hsiang TC (1989) Quality engineering in production systems. McGraw-Hill, New York

    Google Scholar 

  20. Peng HP (2012) Concurrent tolerancing for design and manufacturing based on the present worth of quality loss. Int J Adv Manuf Technol 59(9–12):929–937

    Article  Google Scholar 

  21. Lu JP, Tang SY, Yan Y, Zhang FP, Shahid IB (2009) Research on assembly quality evaluation based on Markov chain. IEEE International Conference on Industrial Engineering and Engineering Management, 1366–1370

  22. Wang JF, Liu JH, Zhong YF (2005) A novel ant colony algorithm for assembly sequence planning. Int J Adv Manuf Technol 25(11):1137–1143

    Article  Google Scholar 

  23. Azuma R, Baillot Y, Behringer R, Feiner S, Julier S, Macintyre B (2001) Recent advances in augmented reality. IEEE Comput Graph Appl 21(6):34–47

    Article  Google Scholar 

  24. Ulrich M, Wiedemann C, Steger C (2009) CAD-based recognition of 3D objects in monocular images. Int Conf Robot Autom 9:1191–1198

    Google Scholar 

  25. Rosten E, Porter R, Drummond T (2010) Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell 32(1):105–119

    Article  Google Scholar 

  26. Calonder M, Lepetit V, Strecha C, Fua P (2010) BRIEF: binary robust independent elementary features. Lect Notes Comput Sci 6314:778–792

    Article  Google Scholar 

  27. Li SQ, Xu C, Xie M (2012) A robust o(n) solution to the perspective-n-point problem. IEEE Trans Pattern Anal Mach Intell 34(7):1444–1450

    Article  Google Scholar 

  28. Lepetit V, Moreno-Noguer F, Fua P (2009) EPNP: an accurate o(n) solution to the PnP problem. Int J Comput Vis 81(2):155–166

    Article  Google Scholar 

  29. Lu CP, Hager GD, Mjolsness E (2000) Fast and globally convergent pose estimation from video images. IEEE Trans Pattern Anal Mach Intell 22(6):610–622

    Article  Google Scholar 

  30. Hutteniocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15(9):850–863

    Article  Google Scholar 

  31. Schutze O, Esquivel X, Lara A, Coeool C, Carlos A (2012) Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans Evol Comput 16(4):504–522

    Article  Google Scholar 

  32. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  33. Tabatabai AJ, Mitchell OR (1984) Edge location to subpixel values in digital imagery. IEEE Trans Pattern Anal Mach Intell 6(2):188–201

    Article  Google Scholar 

  34. Hermosilla T, Bermejo E, Balaguer A, Ruiz LA (2008) Non-linear fourth-order image interpolation for subpixel edge detection and localization. Image Vis Comput 26(9):1240–1248

    Article  Google Scholar 

  35. Da F, Zhang H (2010) Sub-pixel edge detection based on an improved moment. Image Vis Comput 28(12):1645–1658

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yi Liu, ShiQi Li, JunFeng Wang & Hongmei Zeng

  2. CSR Zhuzhou Electric Locomotive Co., Ltd., Zhuzhou, 412001, China

    Yi Liu

  3. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    JiPing Lu

Authors
  1. Yi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ShiQi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. JunFeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongmei Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. JiPing Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to JunFeng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Li, S., Wang, J. et al. A computer vision-based assistant system for the assembly of narrow cabin products. Int J Adv Manuf Technol 76, 281–293 (2015). https://doi.org/10.1007/s00170-014-6274-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6274-9

Keywords

Search

Navigation