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

Jino Mathew; R.J. Moat; S. Paddea; Michael E. Fitzpatrick; P.J. Bouchard

doi:10.1016/j.ijpvp.2017.01.002

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

Jino Mathew, R.J. Moat, S. Paddea, Michael E. Fitzpatrick, P.J. Bouchard

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

56 Downloads (Pure)

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.

Original language	English
Pages (from-to)	89-95
Number of pages	6
Journal	International Journal of Pressure Vessels and Piping
Volume	150
DOIs	https://doi.org/10.1016/j.ijpvp.2017.01.002
Publication status	Published - 27 Jan 2017

Bibliographical note

Due to publisher policy, the full text is not available on the repository until the 27 January 2018.

Keywords

Residual stress profile
Girth welds
Stainless steel
Neural network
Neutron diffraction
Contour method

Access to Document

10.1016/j.ijpvp.2017.01.002

Post-print.pdfAccepted author manuscript, 3.55 MBLicence: CC BY-NC-ND

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Jino Mathew
- Institute for Future Transport and Cities - Assistant Professor (Research)
Person: Teaching and Research

Cite this

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title = "Prediction of residual stresses in girth welded pipes using an artificial neural network approach",

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.",

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AU - Mathew, Jino

AU - Moat, R.J.

AU - Paddea, S.

AU - Fitzpatrick, Michael E.

AU - Bouchard, P.J.

N1 - Due to publisher policy, the full text is not available on the repository until the 27 January 2018.

PY - 2017/1/27

Y1 - 2017/1/27

N2 - 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.

AB - 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.

KW - Residual stress profile

KW - Girth welds

KW - Stainless steel

KW - Neural network

KW - Neutron diffraction

KW - Contour method

U2 - 10.1016/j.ijpvp.2017.01.002

DO - 10.1016/j.ijpvp.2017.01.002

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EP - 95

JO - International Journal of Pressure Vessels and Piping

JF - International Journal of Pressure Vessels and Piping

SN - 0308-0161

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