2017
DOI: 10.1007/s11661-017-4359-4
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Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

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 cl… Show more

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Cited by 21 publications
(17 citation statements)
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“…To characterize the through-thickness distribution of residual stresses as a function of welding heat input and geometry in stainless steel pipes, two pieces of research in 2017 proposed a new data-based approach based on an artificial neural network (ANN) and verified the results with those of contour and neutron diffraction methods. [194,195] The ANN method successfully learnt the nonlinear patterns associated with residual stress fields in the HAZ and weld centerline.…”
Section: Thermalmentioning
confidence: 99%
“…To characterize the through-thickness distribution of residual stresses as a function of welding heat input and geometry in stainless steel pipes, two pieces of research in 2017 proposed a new data-based approach based on an artificial neural network (ANN) and verified the results with those of contour and neutron diffraction methods. [194,195] The ANN method successfully learnt the nonlinear patterns associated with residual stress fields in the HAZ and weld centerline.…”
Section: Thermalmentioning
confidence: 99%
“…[31] In some studies, a combination of the two classes of experimental methods is used and supported by simulations. [32,33] A critical limitation for diffraction techniques to accurately determine RS is the use of an appropriate stress-free-reference d 0 . [34] This challenge is harder when using ND, since no simplifying assumptions can be used if one wants to determine a fully triaxial RS.…”
Section: Introductionmentioning
confidence: 99%
“…An attempt to study residual stress profiles in welds using an artificial neural network approach was made in [15]. Although this scattering of results is often attributed to the texture of the dendrites, it has been shown that texture does not play a key role in austenitic steel stress analysis [9].…”
Section: Introductionmentioning
confidence: 99%