Abstract
The artificial neural network (ANN) method for the data processing of on-line corrosion fatigue crack growth monitoring is proposed after analyzing the general method for corrosion fatigue crack growth data. A metabolism model for predicting the corrosion fatigue life by ANN is presented, which does not need all kinds of materials and environment parameters, and only needs to measure the relation between a (length of crack) and N (cyclic times of loading) in-service. The feasibility of this model was verified by some examples. It makes up the inadequacy of data processing for current method and on-line monitoring. Hence it has definite realistic meaning for engineering application.
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