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Prediction Of The Property Of Corrosion Resistance Of A Surface Alloyed Layer By Using Artificial Neural Networks

Author

Listed:
  • JIANG XU

    (Department of Material Science and Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing, 210016, P. R. China)

  • WENJIN LIU

    (Laser Processing Research Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, P. R. China)

  • ZHONG XU

    (Research Institute of Surface Engineering, Taiyuan University of Technology, Taiyuan, 030024, P. R. China)

Abstract

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 in20%H2SO4and20%HClsolutions. 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.

Suggested Citation

  • Jiang Xu & Wenjin Liu & Zhong Xu, 2005. "Prediction Of The Property Of Corrosion Resistance Of A Surface Alloyed Layer By Using Artificial Neural Networks," Surface Review and Letters (SRL), World Scientific Publishing Co. Pte. Ltd., vol. 12(04), pages 569-572.
    • Handle: RePEc:wsi:srlxxx:v:12:y:2005:i:04:n:s0218625x05007451
    DOI: 10.1142/S0218625X05007451
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