Skip to main content
Log in

Development of a welding residual stress predictor using a function-replacing hybrid system

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

Abstract

Welding is a reliable and efficient metal-joining process widely used in industries. Residual stresses are inherent and detrimental in welded structures. Researchers have developed many direct measuring techniques for welding residual stress. Intelligent techniques have been developed to predict residual stresses to meet the demands of advanced manufacturing planning. The existing tools are limited in application and need attention. This research paper details the development and use of a function-replacing hybrid for predicting the residual stress in butt-welding.

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.

Similar content being viewed by others

References

  1. Prevey PS (1986) X-ray diffraction residual stress techniques, in: Mills K (ed), Metals Handbook, 9th edn., vol. 10. American Society for Metals, Metals Park, OH, pp 380–392

    Google Scholar 

  2. Schajer GS (1998) Measurement of non-uniform residual stresses using the hole-drilling method. Part I. Stress calculation procedure. J Eng Mater Technol 110:338–343

    Article  Google Scholar 

  3. Weng CC, Lo SC (1992) Measurement of residual stresses in welded steel joints using the hole-drilling method. Mater Sci Technol 8:213–218

    Google Scholar 

  4. Nelson D, Fuchs E, Makino A, Williams D (1994) Residual-stress determination by single-axis holographic interferometry and hole drilling. Part II. Experiments. Exp Mech 34:79–88

    Article  Google Scholar 

  5. Ueda Y, Yamakawa T (1971) Analysis of thermal elastic-plastics stress and strain during welding by finite element method. Trans Japan Welding Soc 2:90–100

    Google Scholar 

  6. Nomoto T (1971) Finite element analysis of thermal stress during welding, PhD Thesis, University of Tokyo, Tokyo

  7. Haykin S (1994) Neural networks: a comprehensive foundation, Macmillan, New York

    MATH  Google Scholar 

  8. Burke L, Ignizio JP (1997) A practical overview of neural networks. J Intell Manuf, pp 157–165

  9. Kim S, Jeong YJ, Lee CW, Yarlagadda PKD V (2003) Prediction of welding parameters for pipeline welding using an intelligent system. Int J Adv Manuf Technol 22:713–719

    Article  Google Scholar 

  10. Nagesh DS, Datta GL (2002) Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. J Mater Process Technol 123:303–312

    Article  Google Scholar 

  11. Toparli M, Sahin S, Ozkaya E, Sasaki S (2002) Residual thermal stress analysis in cylindrical steel bars using finite element method and artificial neural networks. Comput Struct 80:1763–1770

    Article  Google Scholar 

  12. Goonatilake S, Khebbal S (1995) Intelligent hybrid system. Wiley, New York

    Google Scholar 

  13. Schaffer JD, Whitely D, Eshelman LJ (1992) Combinations of genetic algorithms and neural networks: a survey of the state of the art. Proc IEEE Workshop on Combinations of Genetic Algorithm and Neural Networks, vol. 1, IEEE, New York, p 32

  14. Koehn Philipp (1994) Combining genetic algorithms and neural networks: the encoding problem. MSc Thesis. The University of Tennessee, Knoxville, TN

  15. Mitchell Melanie (1996) An introduction to genetic algorithms. MIT Press, Cambridge, MASS

    Google Scholar 

  16. Larry Singerland J (1984) Applied finite element analysis, 2nd edn., Wiley, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Kumanan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumanan, S., Ashok Kumar, R. & Raja Dhas, J.E. Development of a welding residual stress predictor using a function-replacing hybrid system. Int J Adv Manuf Technol 31, 1083–1091 (2007). https://doi.org/10.1007/s00170-005-0297-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-005-0297-1

Keywords

Navigation