International Journal of Frontiers in Engineering Technology, 2022, 4(8); doi: 10.25236/IJFET.2022.040802.
Kuan Zhao, Bangwen Wang, He Xue, Zheng Wang
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China
The welding residual stress is one of the main factors that lead to Stress Corrosion Cracking (SCC) in dissimilar metal welded (DMW) joints of the safe-end in nuclear power plants. Based on ABAQUS software, a thermal-elastic plastic finite element method is developed to simulate residual stress for DMW joints of the safe-end. Considering the randomness of material parameters of the welding metal, the neural network response surface method is applied to calculate the change of welding residual stress distribution. Meanwhile, to improve the efficiency of numerical analysis, MATLAB is employed in the secondary development for ABAQUS. With the help of existing experimental data, the effect of random parameters of the welding metal on the residual stress in DMW joints is simulated and analyzed in this study. The results show that the residual stress distribution of the DMW joints is significantly affected by the random parameters. Among the parameters, the randomness of Yield strength and Module of Elasticity has the most significant influence on the uncertainty of the distribution of welding residual stress.
Dissimilar metal welded joint; Residual stress; Randomness; Response surface method
Kuan Zhao, Bangwen Wang, He Xue, Zheng Wang. Influence of Material Randomness on Welding Residual Stress in Dissimilar Metal Welded Joints of Nuclear Power Plants. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 8: 7-12. https://doi.org/10.25236/IJFET.2022.040802.
 Maganioti, A.E., Chrissanthi, H.D., Charalabos, P.C., Andreas, R.D., George, P.N. and Christos, C.N. (2010) Cointegration of Event-Related Potential (ERP) Signals in Experiments with Different Electromagnetic Field (EMF) Conditions. Health, 2, 400-406.
 Bootorabi, F., Haapasalo, J., Smith, E., Haapasalo, H. and Parkkila, S. (2011) Carbonic Anhydrase VII—A Potential Prognostic Marker in Gliomas. Health, 3, 6-12.
 LI S, CHEN W, HU L, et al. Influence of Strain Hardening and Annealing Effect on the Prediction of Welding Residual Stresses in a Thick-Wall 316 Stainless Steel Butt-Welded Pipe Joint[J]. Acta Metall Sin, 2021, 57(12): 1653-1666.
 Zhong W, Lin J L, Chen Y, et al. Microstructure, hardness, and residual stress of the dissimilar metal weldments of SA508-309L/308L-304L[J]. Metallurgical and Materials Transactions A, 2021, 52(5): 1927-1938.
 Ye C, Telang A, Gill A, et al. Effects of Ultrasonic Nanocrystal Surface Modification on the Residual Stress, Microstructure, and Corrosion Resistance of 304 Stainless Steel Welds[J]. Metallurgical and Materials Transactions A, 2018, 49(3): 972-978.
 Liu R F, Wang J C. Finite element analyses of the effect of weld overlay sizing on residual stresses of the dissimilar metal weld in PWRs[J]. Nuclear Engineering and Design, 2021, 372: 110959.
 Yadroitsev I, Yadroitsava I. Evaluation of residual stress in stainless steel 316L and Ti6Al4V samples produced by selective laser melting[J]. Virtual and Physical Prototyping, 2015, 10(2): 67-76
 Ogawa N, Muroya I, Iwamoto Y, et al. Residual Stress Evaluation of Dissimilar Weld Joint Using Reactor Vessel Outlet Nozzle Mock-Up Model: Report 2[C].//ASME 2009 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2009: 353-364.
 Balakrishnan J, Vasileiou A N, Francis J A, et al. Residual stress distributions in arc, laser and electron-beam welds in 30 mm thick SA508 steel: A cross-process comparison[J]. International Journal of Pressure Vessels and Piping, 2018, 162: 59-70.
 Deng D, Kiyoshima S. Numerical simulation of welding residual stress in a multi-pass butt-welded joint of austenitic stainless steel using variable length heat source[J]. Acta Metallurgica Sinica, 2010, 46(2):195-200.
 Nose M , Amano H , Okada H , et al. Computational crack propagation analysis with consideration of weld residual stresses[J]. Engineering Fracture Mechanics, 2017, 182.
 Wu G, Wang Z, Gan J, et al. FE analysis of shot-peening-induced residual stresses of AISI 304 stainless steel by considering mesh density and friction coefficient[J]. Surface Engineering, 2019, 35(3): 242-254.
 Deng D , Murakawa H . Numerical simulation of temperature field and residual stress in multi-pass welds in stainless steel pipe and comparison with experimental measurements[J]. Computational Materials Science, 2006, 37(3):0-277.
 Lewis J R, Brooks D. Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions[J]. SAND2016-10932, Albuquerque, New Mexico, 2016, 87185.
 Salvati E, Sui T, Korsunsky A M. Uncertainty quantification of residual stress evaluation by the FIB–DIC ring-core method due to elastic anisotropy effects[J]. International Journal of Solids and Structures, 2016, 87: 61-69.
 Zhang Y, Gao X, Katayama S. Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding[J]. Journal of Manufacturing Systems, 2015, 34: 53-59.
 Li J, Cheng J, Shi J, et al. Brief introduction of back propagation (BP) neural network algorithm and its improvement[M]. Advances in computer science and information engineering. Springer, Berlin, Heidelberg, 2012: 553-558.