An artificial neural network (ANN) has been developed to describe intergranular stress corrosion cracking in sensitized Type 304SS in high temperature aqueous solutions. The ANN predictions of crack growth rate (CGR) versus oxygen concentration, flow velocity, stress intensity, hydrogen concentration, and ECP are compared with the predictions of the deterministic Coupled Environment Fracture Model (CEFM). The predictions of these two approaches, which represent the extremes in the spectrum of predictive technologies, are generally in good accord, except that the cGRs obtained from the ANN are up to a factor of three higher than those predicted by the CEFM under some conditions. However, this difference is within the uncertainty in the experimental data used to train the ANN.

Subject
Crack growth, Water, Intergranular stress corrosion cracking, Boiling water reactors, Hydrogen concentration, Stress intensity, Flow velocity, Acidity, Experimental data, Oxygen, Stainless steel, Corrosion potential, Conductivity
© 1994 Association for Materials Protection and Performance (AMPP). All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise) without the prior written permission of AMPP. Positions and opinions advanced in this work are those of the author(s) and not necessarily those of AMPP. Responsibility for the content of the work lies solely with the author(s).
1994
Association for Materials Protection and Performance (AMPP)
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