Skip to main content
Log in

Dimensional errors of rollers in the stream of variation modeling in cold roll forming process of quadrate steel tube

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

Abstract

In the cold roll forming process, the sheet metal strip is gradually bent into a desired profile by successive roller stations. During this process the roller locating errors of each roller station are introduced, transformed and accumulated until the sheet metal is bent into the final desired profile, which does influence the product’s dimension quality, affect the end-welding of quadrate steel tube, and elongate the costly error-and-trial phase in ramp-up. This paper introduces procedures for expressing the influence of roller locating errors in the forming process of quadrate steel tube, which is based on the formulation of the stream of variation (SOV) model of roller dimensional errors using the CAD/CAPP parameters of the cold roll forming process. The SOV model is utilized to reveal the variation propagation in the manufacturing process. The modeling process is experimentally validated in a two-station forming process.

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

Access this article

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

Reference

  1. Kiuchi M, Koudabashi T (1983) “Roll forming of circular tubes- automated design system of optimal roll profiles”. Proc. International Conference and Energy Eurogress - Aachen Germany

  2. Nitin Duggal, Mustafa AA, Gary LK, Taylan A (1996) “Computer aided simulation of cold roll forming - a computer program for simple section profiles”. J Mater Process Tech 59:41–48

    Article  Google Scholar 

  3. Bhattacharyya D, Smith PD, Yee CH, Collins IF (1984) “The prediction of deformation length in cold roll forming”. J Mech Work Technol 9:181–191

    Article  Google Scholar 

  4. Kiuchi M, Koudabashi T (1984) “Automated design system of optimal roll profiles for cold roll forming”. Proc. the third international conference on rotary metalworking processes, Kyoto, Japan 423–436

  5. Kiiuchi M, Abe K, Onodera R (1996) “Computerized numerical simulation of roll forming process”. Proc. the Fifth ICTP, Columbus, OH 493–496

  6. Panton SM (1987) “Computer-aided form roll design”. Ph.D. Thesis, University of Aston, Birmingham

  7. Panton SM, Zhu SD, Duncan JL (1996) “Longitudinal and shear strain development in cold roll forming”. J Mater Process Tech 60:219–224

    Article  Google Scholar 

  8. Brunet M, Mguil S, Pol P (1998) “Modelling of a roll-forming process with a combined 2D and 3D FEM code”. J Mater Process Tech 80–81:213–219

    Article  Google Scholar 

  9. Farzin M, Salmani TM, Shameli E (2002) “Determination of buckling limit of strain in cold roll forming by the finite element analysis”. J Mater Process Tech 125–126:626–632

    Article  Google Scholar 

  10. Alsamhan A, Pillinger I, Hartely P (2004) “The development of real time re-meshing technique for simulating cold-roll-forming using FE methods”. J Mater Process Tech 147:1–9

    Article  Google Scholar 

  11. Zhi-Wu H, Cai L, Wei-Ping L, Lu-Quan R, Jin T (2005) “Spline finite strip analysis of forming parameters in roll forming a channel section”. J Mater Process Tech 159:383–388

    Article  Google Scholar 

  12. Hu SJ (1997) “Stream of variation theory for automotive-body assembly”. CIRP Ann 46(1):1–6

    Article  Google Scholar 

  13. Hu S, Wu SM (1992) “Identifying root causes of variation in autobody assembly using principal component analysis”. Transactions of NAMRI 20:311–316

    Google Scholar 

  14. Roan CM, Hu SJ (1995) “Monitoring and classification of dimensional faults in automotive body assembly”. Int J Flex Manuf Syst 7(2):103–125

    Article  Google Scholar 

  15. Ceglarek D, Shi J (1995) “Dimensional variation reduction for automotive body assembly”. Manuf Rev 8:139–154

    Google Scholar 

  16. Ceglarek D, Shi J (1996) “Fixture failure diagnosis for the autobody assembly using pattern recognition”. ASME J Ind Eng 118(1):55–66

    Google Scholar 

  17. Yang K (1996) “Improving automotive dimensional quality by using principal component analysis”. Qual Reliab Eng Int 12(6):401–409

    Article  Google Scholar 

  18. Jin J, Shi J (1999) “State of variation theory for automotive-body assembly”. CIRP Ann 46(1):1–6

    Google Scholar 

  19. Ding Y, Ceglarek D, Shi J (2000) “Modelling and diagnosis of multi-stage manufacturing processes, Part I-State space model”, Japan-USA Symposium, Ann Arbor, Michigan

  20. Ding Y, Ceglarek D, Shi J (2000) “Modelling and diagnosis of multi-stage manufacturing processes, Part II-Fault diagnosis”, Japan-USA Symposium, Ann Arbor, Michigan

  21. Ding Y, Ceglarek D, Shi J (2002) “Fault diagnosis of multi-stage manufacturing processes by using state space approach”. ASME J Manuf Sci Eng 124:313–322

    Article  Google Scholar 

  22. Ding Y, Shi J, Ceglarek D (2002) “Diagnosability analysis of multi-station manufacturing processes”. ASME J Dyn Syst Meas Control 124:1–13

    Article  Google Scholar 

  23. Ding Y, Pansoo K, Ceglarek D, Jionghua J (2003) “Optimal sensor distribution for variation diagnosis in multi-station assembly processes”. IEEE Trans Robot Autom 19(4):543–556

    Article  Google Scholar 

  24. Liu C, Ding Y, Chen Y (2005) “Optimal coordinate sensor placements for estimating mean and variance components of variation sources”. IIE Trans 37(9):877–889

    Article  Google Scholar 

  25. Huang Q, Zhou N, Shi J (2000) “Stream of variation modeling and diagnosis of multi-station machining processes”. Proc. of IMECE 2001, Orlando, FL

  26. Djurdjanovic D, Ni J (2001) “Linear state space modeling of dimensional machining errors”. Trans Of NAMRI/SME 29:541–548

    Google Scholar 

  27. Zhou S, Huang Q, Shi J (2003) “State space modeling of multi-stage machining systems by using differential motion vector”. IEEE Trans Rob Autom:19(2):296–309

    Article  Google Scholar 

  28. Djurdjanovic D (2002) “Stream of variation modelling of machining errors and its applications”. Doctoral Dissertation, University of Michigan

  29. Djurdjanovic D, Ni J (2001) “Stream of variation based analysis and synthesis of measurement schemes in multi-station machining systems”. Proc. of the ASME IMECE

  30. Djurdjanovic D, Ni J (2003) “Dimensional errors of fixtures, locating and measurement datum features in the stream of variation modeling in machining”. ASME J Manuf Sci Eng 125:716–730

    Article  Google Scholar 

  31. Ceglarek D, Huang W, Zhou S, Ding Y, Kumar R, Zhou Y (2004) “Time-based competition in multistage manufacturing: Stream-of-variation analysis (SOVA)”. Int J Flex Manuf Syst 16:11–14

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, L., Ni, J. & Lai, X. Dimensional errors of rollers in the stream of variation modeling in cold roll forming process of quadrate steel tube. Int J Adv Manuf Technol 37, 1082–1092 (2008). https://doi.org/10.1007/s00170-007-1066-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-007-1066-0

Keywords

Navigation