Artificial neural networks are computer simulations that have the potential of "finding" the same patterns that corrosion practitioners recognize to relate experimental test results to lifetime predictions. This potential ability was utilized to construct an artificial neural network to recognize the pattern between results from a sequential immersion test for organic non-metallic lining materials and their ability to function as linings in actual applications. The network so constructed has been shown to predict field performance from this test. The network was incorporated within an Expert System to simplify data input and output, allow for simple consistency checks, and to make the final prediction.

Subject
Field performance, Artificial neural networks, Immersion tests, Materials, Absorption, Diffusion, Immersion, Training, Rubber, Hardness, Lining, Drying, Leveling
Keywords:
artificial neural network, expert system, non-metallic, plastic, elastomer, lining, degradation, corrosion, lifetime prediction, sequential immersion test
© 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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