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Prediction of residual stresses in girth welded pipes using an artificial neural network approach

Mathew, J.; Moat, R. J.; Paddea, S.; Fitzpatrick, M. E. and Bouchard, P. J. (2017). Prediction of residual stresses in girth welded pipes using an artificial neural network approach. International Journal of Pressure Vessels and Piping, 150 pp. 89–95.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.ijpvp.2017.01.002
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Abstract

Management of operating nuclear power plants greatly relies on structural integrity assessments for safety critical pressure vessels and piping components. In the present work, residual stress profiles of girth welded austenitic stainless steel pipes are characterised using an artificial neural network approach. The network has been trained using residual stress data acquired from experimental measurements found in literature. The neural network predictions are validated using experimental measurements undertaken using neutron diffraction and the contour method. The approach can be used to predict through-wall distribution of residual stresses over a wide range of pipe geometries and welding parameters thereby finding potential applications in structural integrity assessment of austenitic stainless steel girth welds.

Item Type: Journal Item
Copyright Holders: 2017 Elsevier Ltd.
ISSN: 0308-0161
Keywords: residual stress profile; girth welds; stainless steel; neural network; neutron diffraction; contour method
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 50926
Depositing User: Richard Moat
Date Deposited: 19 Sep 2017 13:28
Last Modified: 02 Nov 2017 15:06
URI: http://oro.open.ac.uk/id/eprint/50926
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