Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study
Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science - Tập 48 - Trang 6178-6191 - 2017
Tóm tắt
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.
Tài liệu tham khảo
P.J. Withers: Reports Prog. Phys., 2007, vol. 70, pp. 2211–64.
R6 Revision 4: Assessment of the integrity of structures containing defects, Gloucester, 2009.
G.S. Schajer: Practical Residual Stress Measurement Methods, Wiley, Chichester, 2013, pp. 6-24.
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.
M.B. Prime: J. Eng. Mater. Technol., 2001, vol. 123, pp. 162-68.
M.C. Smith, P.J. Bouchard, M. Turski, L. Edwards and R.J. Dennis: Comput. Mater. Sci., 2012, vol. 54, pp. 312–28.
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.
P.J. Withers, M. Preuss, A. Steuwer and J.W.L. Pang: J. Appl. Crystallogr., 2007, vol. 40, pp. 891–904.
F. Hosseinzadeh, J. Kowal, and P.J. Bouchard: J. Eng., 2014, pp. 1–16, DOI:10.1049/joe.2014.0134.
B. Ahmad and M.E. Fitzpatrick: Metall. Mater. Trans. A, 2016, vol. 47, pp. 301–13.
P.J. Bouchard: Int. J. Press. Vessel. Pip., 2008, vol. 85, pp. 152–65.
Christopher M. Bishop: Neural networks for Statistical Pattern Recognition, Oxford University Press, Oxford, 1994, pp. 1-27.
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
İ. Toktaş and A.T. Özdemir: Expert Syst. Appl., 2011, vol. 38, pp. 553–63.
M.G. Na, J.W. Kim, D.H. Lim and Y.-J. Kang: Nucl. Eng. Des., 2008, vol. 238, pp. 1503–10.
S. Song, P. Dong and X. Pei: Int. J. Press. Vessel. Pip., 2015, vol. 126–127, pp. 58–70.
A. H. Mahmoudi, S. Hossain, M. J. Pavier, C.E. Truman and D.J. Smith: Exp. Mech., 2009, vol. 49, pp. 595-604.
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.
T. Pirling, G. Bruno and P.J. Withers: Mater. Sci. Eng. A, 2006, vol. 437, pp. 139-44.
F. Hosseinzadeh and P.J. Bouchard: Exp. Mech., 2012, vol. 53, pp. 171–81.
M.B. Prime, R.J. Sebring, J.M. Edwards, D.J. Hughes and P.J. Webster: Exp. Mech., 2004, vol. 836, pp. 1–10.
P. Pagliaro, M. B. Prime, H. Swenson and B. Zuccarello: Exp. Mech., 2009, vol. 50, pp. 187–94.
D.E. Rumelhart, G.E. Hinton and R.J. Williams: Nature, 1986, vol. 323, pp. 533–36
-K. Hornik, M. Stinchcombe and H. White: Neural Networks, 1989, vol. 2, pp. 359-66.
MATLAB: MATLAB and Neural Network Toolbox Release 2012a, The MathWorks Inc., Natick, MA, 2012.
M.F. Møller: Neural Networks, 1993, vol. 6, pp. 525–33.
P.J. Bouchard: Int. J. Press. Vessel. Pip., vol. 84, 2007, pp. 195–222.
D.J.C. Mackay: PhD thesis, California Institute of Technology, 1991.
R.J. Mammone: Artificial Neural Networks for Speech and Vision, Chapman & Hall Inc., New york, 1993, pp. 126-42.
S. Hossain: PhD thesis, University of Bristol, 2005.