Abstract
An artificial neural network is developed to predict the corrosion behavior of different series of aluminum alloys when exposed to a variety of corrosive substances, short term and long term aircraft carrier exposures. Given the corrosion environment and time of exposure the neural network predicts the ASTM G34 corrosion rating and the resulting material loss. The trained and limited test results predicted from the neural network are in good comparison to the experimental data. The effects of corrosion environment and material type from neural network simulation are presented to illustrate the trends. Based on the preliminary results, the neural network approach to corrosion predictions is encouraging and can be used for a variety of materials and environments if more data is available. It is possible to use another neural network to predict the required exposure time to produce a particular corrosion classification in an environment. It is intended that the approach developed here will assist in the structural integrity evaluation of aging aircraft.
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