PREDICTION OF THE PROPERTY OF CORROSION RESISTANCE OF A SURFACE ALLOYED LAYER BY USING ARTIFICIAL NEURAL NETWORKS
In this study, the potential of artificial neural network techniques to predict and analyze the properties of the corrosion resistance of a double glow alloyed layer is investigated. The input parameters of the neural network (NN) are: source voltage; workpiece voltage; working pressure; and the distance between source electrode and workpiece. These parameters have great effect on the properties of corrosion resistance of a double-glow alloyed layer. The output of the NN model are the corrosion results of a 200-hour immersion test in 20%H2SO4 and 20%HCl solutions. The process parameter and corrosion results are then used as a training set for an artificial neural network (ANN). The model is based on a multiple-layer feed-forward neural network. The ANN model can predict the properties of the corrosion resistance of the alloyed layer regardless of whether the process parameter interacts or not. A very good performance of the neural network is achieved. The calculation results are in good agreement with the experimental results.
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