The objective of this study was to relate the results from electrochemical impedance spectroscopy (EIS) collected in 24 hours to salt spray exposure data collected over 1500 hours for conversion coated metal surfaces. To develop such a relationship, an approach based on artificial neural networks (ANNs) was used. The output of this study was a matrix of weights and threshold values that predicted the salt spray performance of coated components based on EIS results. A model based on phase angle data input from EIS measurements collected after 24 hours exposure to 0.5M NaCl was able to account for 85% of the variation in the salt spray time to failure from a randomly selected subset of the sample population. This exercise illustrates the utility of ANNs in corrosion prediction and suggests that they may play a key role in making lifetime predictions for components in service based on laboratory measurements. Conversion coated substrates and salt spray exposure data used in this study were furnished by the National Center for Manufacturing Sciences Ann Arbor MI, as part of the Alternatives to Chromium in Metal Finishing Study [1].

Subject
Test methods, Electrochemical impedance spectroscopy, Coatings, Voltage, Modeling, Slopes, Impedance, Failure analysis, Mechanical failure, Corrosion modeling, Salt spray corrosion testing, Training, Aluminum alloys
Keywords:
conversion coatings, EIS, artificial neural networks, corrosion prediction, aluminum alloys
© 2004 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).
2004
Association for Materials Protection and Performance (AMPP)
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