English, Article edition: PREDICTION OF THE PROPERTY OF CORROSION RESISTANCE OF A SURFACE ALLOYED LAYER BY USING ARTIFICIAL NEURAL NETWORKS JIANG XU; WENJIN LIU; ZHONG XU

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Bookmark: http://trove.nla.gov.au/version/83891
Physical Description
  • article
Language
  • English

Edition details

Title
  • PREDICTION OF THE PROPERTY OF CORROSION RESISTANCE OF A SURFACE ALLOYED LAYER BY USING ARTIFICIAL NEURAL NETWORKS
Author
  • JIANG XU
  • WENJIN LIU
  • ZHONG XU
Physical Description
  • article
Notes
  • 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.
  • Double glow, artificial neural network (ANN), multi-element alloying
  • RePEc:wsi:srlxxx:v:12:y:2005:i:04:p:569-572
Language
  • English
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