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http://dx.doi.org/10.3795/KSME-A.2004.28.4.460

Evaluation of Thermal Embrittlement Susceptibility in Cast Austenitic Stainless Steel Using Artificial Neural Network  

Kim, Cheol (한전기술(주) 재료기술연구그룹)
Park, Heung-Bae (한전기술(주) 재료기술연구그룹)
Jin, Tae-Eun (전력연구원 원자력연구실)
Jeong, Ill-Seok (한전기술(주) 재료기술연구그룹)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.28, no.4, 2004 , pp. 460-466 More about this Journal
Abstract
Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained teaming data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.
Keywords
Thermal Embrittlement; Cast Austenitic Stainless Steel; Ferrite Content; Artificial Neural Network;
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  • Reference
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