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Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

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Abstract

Economic and safe management of nuclear plant components relies on accurate prediction of welding-induced residual stresses. In this study, the distribution of residual stress through the thickness of austenitic stainless steel welds has been measured using neutron diffraction and the contour method. The measured data are used to validate residual stress profiles predicted by an artificial neural network approach (ANN) as a function of welding heat input and geometry. Maximum tensile stresses with magnitude close to the yield strength of the material were observed near the weld cap in both axial and hoop direction of the welds. Significant scatter of more than 200 MPa was found within the residual stress measurements at the weld center line and are associated with the geometry and welding conditions of individual weld passes. The ANN prediction is developed in an attempt to effectively quantify this phenomenon of ‘innate scatter’ and to learn the non-linear patterns in the weld residual stress profiles. Furthermore, the efficacy of the ANN method for defining through-thickness residual stress profiles in welds for application in structural integrity assessments is evaluated.

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References

  1. P.J. Withers: Reports Prog. Phys., 2007, vol. 70, pp. 2211–64.

    Article  Google Scholar 

  2. R6 Revision 4: Assessment of the integrity of structures containing defects, Gloucester, 2009.

  3. G.S. Schajer: Practical Residual Stress Measurement Methods, Wiley, Chichester, 2013, pp. 6-24.

    Book  Google Scholar 

  4. M.T. Hutchings, P.J. Withers, T.M. Holden and T. Lorentzen: Introduction to the characterization of residual stress by neutron diffraction, Taylor and Francis, London, 2005, pp. 149-99.

    Google Scholar 

  5. M.B. Prime: J. Eng. Mater. Technol., 2001, vol. 123, pp. 162-68.

    Article  Google Scholar 

  6. M.C. Smith, P.J. Bouchard, M. Turski, L. Edwards and R.J. Dennis: Comput. Mater. Sci., 2012, vol. 54, pp. 312–28.

    Article  Google Scholar 

  7. W. Woo, G.B. An, E.J. Kingston, A. T. DeWald, D.J. Smith and M.R. Hill: Acta Materialia, 2013, vol. 61, pp. 3564–74.

    Article  Google Scholar 

  8. P.J. Withers, M. Preuss, A. Steuwer and J.W.L. Pang: J. Appl. Crystallogr., 2007, vol. 40, pp. 891–904.

    Article  Google Scholar 

  9. F. Hosseinzadeh, J. Kowal, and P.J. Bouchard: J. Eng., 2014, pp. 1–16, DOI:10.1049/joe.2014.0134.

  10. B. Ahmad and M.E. Fitzpatrick: Metall. Mater. Trans. A, 2016, vol. 47, pp. 301–13.

    Article  Google Scholar 

  11. P.J. Bouchard: Int. J. Press. Vessel. Pip., 2008, vol. 85, pp. 152–65.

    Article  Google Scholar 

  12. Christopher M. Bishop: Neural networks for Statistical Pattern Recognition, Oxford University Press, Oxford, 1994, pp. 1-27.

    Google Scholar 

  13. H.K.D.H. Bhadeshia, R.C. Dimitriu, S. Forsik, J.H. Pak and J. H. Ryu: Mat. Sci. Technol., 2009, vol 25, pp. 504-10

    Article  Google Scholar 

  14. İ. Toktaş and A.T. Özdemir: Expert Syst. Appl., 2011, vol. 38, pp. 553–63.

    Article  Google Scholar 

  15. M.G. Na, J.W. Kim, D.H. Lim and Y.-J. Kang: Nucl. Eng. Des., 2008, vol. 238, pp. 1503–10.

    Article  Google Scholar 

  16. S. Song, P. Dong and X. Pei: Int. J. Press. Vessel. Pip., 2015, vol. 126–127, pp. 58–70.

    Article  Google Scholar 

  17. A. H. Mahmoudi, S. Hossain, M. J. Pavier, C.E. Truman and D.J. Smith: Exp. Mech., 2009, vol. 49, pp. 595-604.

    Article  Google Scholar 

  18. R.D. Haigh, M.T. Hutchings, J.A. James, S. Ganguly, R. Mizuno, K. Ogawa, S. Okido, A.M. Paradowska and M. E. Fitzpatrick: Int. J. Press. Vessel. Pip., 2013, vol. 101, pp. 1-11.

    Article  Google Scholar 

  19. T. Pirling, G. Bruno and P.J. Withers: Mater. Sci. Eng. A, 2006, vol. 437, pp. 139-44.

    Article  Google Scholar 

  20. F. Hosseinzadeh and P.J. Bouchard: Exp. Mech., 2012, vol. 53, pp. 171–81.

    Article  Google Scholar 

  21. M.B. Prime, R.J. Sebring, J.M. Edwards, D.J. Hughes and P.J. Webster: Exp. Mech., 2004, vol. 836, pp. 1–10.

    Google Scholar 

  22. P. Pagliaro, M. B. Prime, H. Swenson and B. Zuccarello: Exp. Mech., 2009, vol. 50, pp. 187–94.

    Article  Google Scholar 

  23. D.E. Rumelhart, G.E. Hinton and R.J. Williams: Nature, 1986, vol. 323, pp. 533–36

    Article  Google Scholar 

  24. -K. Hornik, M. Stinchcombe and H. White: Neural Networks, 1989, vol. 2, pp. 359-66.

    Article  Google Scholar 

  25. MATLAB: MATLAB and Neural Network Toolbox Release 2012a, The MathWorks Inc., Natick, MA, 2012.

  26. M.F. Møller: Neural Networks, 1993, vol. 6, pp. 525–33.

    Article  Google Scholar 

  27. P.J. Bouchard: Int. J. Press. Vessel. Pip., vol. 84, 2007, pp. 195–222.

    Article  Google Scholar 

  28. D.J.C. Mackay: PhD thesis, California Institute of Technology, 1991.

  29. R.J. Mammone: Artificial Neural Networks for Speech and Vision, Chapman & Hall Inc., New york, 1993, pp. 126-42.

    Google Scholar 

  30. S. Hossain: PhD thesis, University of Bristol, 2005.

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Acknowledgments

The authors are grateful for funding received from EDF Energy, AMEC Power and Process Europe, and the Lloyd’s Register Foundation. The award of neutron beamtime by the Institut Laue-Langevin (ILL) is also gratefully acknowledged. Michael Fitzpatrick and Jino Mathew are funded by the Lloyd’s Register Foundation (LRF), a charitable foundation helping to protect life and property by supporting engineering-related education, public engagement, and the application of research. Pete Ledgard and Stan Hiller from the Open University, and Paul English from the University of Manchester are also acknowledged for their assistance for carrying out the experiments.

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Authors and Affiliations

  1. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK

    J. Mathew & M. E. Fitzpatrick

  2. Department of Engineering and Innovation, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK

    J. Mathew, R. J. Moat, S. Paddea & P. J. Bouchard

  3. School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, M13 9PL, UK

    J. A. Francis

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  1. J. Mathew
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Correspondence to J. Mathew.

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Manuscript submitted January 25, 2017.

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Mathew, J., Moat, R.J., Paddea, S. et al. Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study. Metall Mater Trans A 48, 6178–6191 (2017). https://doi.org/10.1007/s11661-017-4359-4

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  • DOI: https://doi.org/10.1007/s11661-017-4359-4

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