Validating the virtual clamp with CMM correlation on automotive production lines



Mechanical clamping has major disadvantages when used in the in-line measurement of a welded automotive assembly. It is hard to adjust the welding process, the clamp prevents the flexibility of optical measurement and clamping equipment is expensive to buy and maintain. A method called “virtual clamp” has been developed to provide the results of clamped measurement without physically clamping the part. This eliminates the disadvantages of the mechanical clamp. The accuracy of the virtual clamp was validated on four real-world automotive production lines. Correlation between the mechanically clamped parts using a CMM and virtually clamped parts using an in-line measurement system showed the accuracy of the virtual clamp to be around 0.04 mm in half- and full-vehicle size welded assemblies. Although this is a considerable contribution to the overall measurement uncertainty budget, the virtual clamp appeared to reduce other uncertainties affecting the budget. Correlation actually improved when using the virtual clamp. The research has opened a way to validate the accuracy of a virtual clamp that does not require additional investments and uses existing technology. The market indicates that the next generation of manufacturing needs more flexibility from in-line measurement, and this research clearly shows that the virtual clamp can provide it.


In-line measurement 100% inspection Clamp Correlation to CMM FEM Automotive body and chassis manufacturing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Roland Berger (2012) Mastering Product Complexity. Roland Berger Strategy Consultants. November 2012Google Scholar
  2. 2.
    Pine BJ (1993) (1993) mass customization: the new frontier in business competition. Harvard Business School Press, BostonGoogle Scholar
  3. 3.
    European Commission. (2004). Manufuture — A vision for 2020. Report of the high-level groupGoogle Scholar
  4. 4.
    National Research Council (1998) Visionary manufacturing challenges for 2020. National Academy Press, Washington (DC), p 1998Google Scholar
  5. 5.
    ElMaraghy HA (2006) Flexible and reconfigurable manufacturing systems paradigms. Int J Flex Manuf Syst 2006(17):261–276. doi: 10.1007/s10696-006-9028-7 MATHGoogle Scholar
  6. 6.
    Wiendahl H (2005) Justifying changeability. A methodical approach to achieving cost effectiveness. Int J Manuf Sci Prod 6(1–2):33Google Scholar
  7. 7.
    Nyhuis P, Kolakowski M (2006) (2006) Heger CL. Evaluation of factory transformability — a systematic approach. Prod Eng 13(1):147–152Google Scholar
  8. 8.
    Dove R (1994) The meaning of life and the meaning of agile. Production 1994 106(11):14–15Google Scholar
  9. 9.
    Goldman SL, Nagel RN, Preiss K (1995) (1995) agile competitors and virtual organizations. Van Nostrand Reinhold, New YorkGoogle Scholar
  10. 10.
    Yusuf YY, Sarhadi M, Gunasekaran A (1999) Agile manufacturing: the drivers, concepts and attributes. Int J Prod Econ 62(1–2):33–43CrossRefGoogle Scholar
  11. 11.
    Gunasekaran A, Yusuf YY (2002) Agile manufacturing: a taxonomy of strategic and technological imperatives. Int J Prod res 40(6):1357–1385CrossRefGoogle Scholar
  12. 12.
    Slack N (1988) (1988) manufacturing system flexibility — an assessment procedure. Comput-Integr Manuf Syst 1(1):25–31CrossRefGoogle Scholar
  13. 13.
    Upton DM (1994) (1994) the management of manufacturing flexibility. Calif Manag rev 36(2):72–89CrossRefGoogle Scholar
  14. 14.
    Upton DM (1995) (1995) flexibility as process mobility: the management of plant capabilities for quick response manufacturing. J Oper Manag 12(3–4):205–224CrossRefGoogle Scholar
  15. 15.
    Koren Y, Shpitalni M (2010) Design of reconfigurable manufacturing systems. J Manuf Syst 2010(29):130–141CrossRefGoogle Scholar
  16. 16.
    Jeang A (1994) Tolerance design: choosing optimal tolerance specification in the design machined parts. Qual Reliab Eng Int 10:2735CrossRefGoogle Scholar
  17. 17.
    Chase KW, Greenwood WH, Loosli BG, Haugland LF (1990). Least cost tolerance allocation for mechanical assemblies with automated process selection. Manufacturing review 1990;3(1):4959.Google Scholar
  18. 18.
    Tuominen V (2012) Cost Modeling of Inspection Strategies in Automotive Quality Control. Engineering Management Research; Vol. 1, No. 2; 2012. ISSN 1927–7318 E-ISSN 1927–7326Google Scholar
  19. 19.
    Jeang A (2009) Optimal determination of the process means, process tolerances, and resetting cycle for process planning under process shifting. J Manuf Syst 28(2009):98106Google Scholar
  20. 20.
    Frost & Sullivan (2014) Greater emphasis on automation in automotive industry to drive investments for dimensional metrology equipment (NDC7–30)Google Scholar
  21. 21.
    Frost & Sullivan (2015) Understanding industry 4.0—Impact on the global Inline metrology marketGoogle Scholar
  22. 22.
    Tuominen, V. (2011). Virtual clamping in automotive production line measurement. Expert Systems with Applications Vol. 38, Issue 12, November/December 2011. ISSN 0957–4174Google Scholar
  23. 23.
    MSA (2002) Measurement System Analysis, Reference Manual, Third Edition. Automotive Industry Action Group. ISBN 978–1–60-534082-1Google Scholar
  24. 24.
  25. 25.
    VDI 2634 (2002) Optical 3D measuring systems. Imaging systems with point-by-point probing. Part 1. VDI-guideline, Verein Deutscher Ingenieure, Düsseldorf 2002Google Scholar
  26. 26.
    Haggrén H (1992) On system development of photogrammetric stations for on-line manufacturing control. Acta Polytechnica Scandinavica, Civil Engineering and Building Construction Series No. 97, Helsinki 1992, 31 pp. Published by the Finnish Academy of Technology. ISBN 951–666–350-8. ISSN 0355–2705Google Scholar
  27. 27.
    Tuominen V (2007) Verification of the accuracy of a real-time optical 3D–measuring system. Master’s thesis, Helsinki University of TechnologyGoogle Scholar
  28. 28.
    Tuominen V, Niini I (2008) Verification of the accuracy of a realtime optical 3D-measuring system on production line. Int arch Photogrammetr, rem Sens spatial inform Sci 38, part B5-1, Comission V 13–19. ISSN 1682–1750Google Scholar
  29. 29.
    Hemming B (2007) Measurement traceability and uncertainty in machine vision applications. Julkaisu / Mittaustekniikan keskus. J, 6/2007. ISBN:978–952–5610-41-3. ISSN:1235–5704Google Scholar
  30. 30.
    Tikka, H., Lehto, H. and Esala, V-P. (2003) Konepajamittaukset ja kalibroinnit: Metalliteollisuuden kustannus Oy. Tekninen tiedotus/Teknologiateollisuus 3, Tampere: 79p. In Finnish (2003).Google Scholar
  31. 31.
    Kay & Laby (2010). Tables of Physical & Chemical Constants. 2.3.5. Thermal Expansion. Kaye & Laby Online. Version 1.1
  32. 32.

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Department of Surveying SciencesAalto UniversityAaltoFinland

Personalised recommendations