The Open UniversitySkip to content
 

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

Mathew, J.; Moat, R.J.; Paddea, S.; Francis, J.A.; Fitzpatrick, M.E. and Bouchard, P.J. (2017). Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study. Metallurgical and Materials Transactions A (Early Access).

DOI (Digital Object Identifier) Link: https://doi.org/10.1007/s11661-017-4359-4
Google Scholar: Look up in Google Scholar

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.

Item Type: Journal Item
Copyright Holders: 2017 The Minerals, Metals & Materials Society and ASM International
ISSN: 1543-1940
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 51560
Depositing User: Richard Moat
Date Deposited: 11 Oct 2017 10:28
Last Modified: 02 Nov 2017 11:02
URI: http://oro.open.ac.uk/id/eprint/51560
Share this page:

Altmetrics

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU