This paper presents an application of an adaptive-predictive probabilistic model for forecasting localized-corrosion-induced pit population and pit depth distributions. The application involves predicting pitting corrosion damage, under sea salt deposits, in 304 stainless steel (UNS number S30400). Pitting corrosion was induced on several rectangular 304 stainless steel coupons by depositing the simulated sea salt, and placing the coupons at 50 °C and 30 g/m3 absolute humidity environment. One by one, the coupons were removed from the environment. These coupons were used to gather initial information regarding the pitting corrosion depth and population distribution. This information was fed in the adaptive-predictive probabilistic model. The output from the model was compared with the data collected from the coupons which were removed from the environment after making the prediction. The model description, the coupon data, and a comparison between the forecasted pit depth distribution from the model and the coupon data are presented. Forecast distributions are in reasonable agreement with measurements, indicating the strength of the method to produce detailed information on pit depths from limited empirical data used as input.

Subject
Pits, Humidity, Marine environments, Salts, Profilometry, Steel coupons, Inspection, Clusters, Algorithms, Sea salt, Corrosion modeling, Pitting, Corrosion coupons
Keywords:
pipeline corrosion, localized corrosion, and distribution forecast
© 2014 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).
2014
Association for Materials Protection and Performance (AMPP)
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