Baysesian Neural Network Approach to Detecting Temper Embrittlement of 30Cr2MoV Rotor Steels

Abstract:

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Baysesian Neural Network approach for predicting the temper embrittlement of steam turbine rotor in service was proposed. The FATT50 (the fracture appearance transition temperature) of the rotors was predicted as a function of ratio of the two peak current densities (Ipr / Ip ) tested by electrochemical potentiodynamic reaction method, temperature of electrolyte, J-factor and grain size ( N ). A database was obtained from the test of electrochemical potentiodynamic reaction and Charpy impact. The Bayesian neural network technique was used for modeling of temper embrittlement. The neural network shows a more precise prediction of temper embrittlement of rotor steels than the prediction using multiple linear regression. The training error and verifying error is with the scatter of ±20°C. The results show that, for the temper embrittlement of rotor steels prediction, the prediction model based on Bayesian neural network is feasible and effective.

Info:

Periodical:

Edited by:

Shengyi Li, Yingchun Liu, Rongbo Zhu, Hongguang Li, Wensi Ding

Pages:

1046-1051

DOI:

10.4028/www.scientific.net/AMM.34-35.1046

Citation:

Y. Z. Fan et al., "Baysesian Neural Network Approach to Detecting Temper Embrittlement of 30Cr2MoV Rotor Steels", Applied Mechanics and Materials, Vols. 34-35, pp. 1046-1051, 2010

Online since:

October 2010

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Price:

$35.00

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