PREDICTION OF THE PROPERTY OF CORROSION RESISTANCE OF A SURFACE ALLOYED LAYER BY USING ARTIFICIAL NEURAL NETWORKS

Jiang Xu (), Wenjin Liu and Zhong Xu
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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

Surface Review and Letters (SRL), 2005, vol. 12, issue 04, 569-572

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.

Keywords: Double glow; artificial neural network (ANN); multi-element alloying (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1142/S0218625X05007451

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