Volume 38, Issue 2 (March 2010)

    Application of Artificial Neural Network for Fatigue Life Prediction under Interspersed Mode-I Spike Overload

    (Received 29 May 2008; accepted 7 September 2009)

    Published Online: 2009

    CODEN: JTEOAD

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    Abstract

    The objective of this study is to design multi-layer perceptron artificial neural network (ANN) architecture in order to predict the fatigue life along with different retardation parameters under constant amplitude loading interspersed with mode-I overload. Fatigue crack growth tests were conducted on two aluminum alloys 7020-T7 and 2024-T3 at various overload ratios using single edge notch tension specimens. The experimental data sets were used to train the proposed ANN model to predict the output for new input data sets (not included in the training sets). The model results were compared with experimental data and also with Wheeler’s model. It was observed that the model slightly over-predicts the fatigue life with maximum error of + 4.0 % under the tested loading conditions


    Author Information:

    Mohanty, J. R.
    Dept. of Metallurgical and Materials Engineering, National Institute of Technology, Rourkela,

    Verma, B. B.
    Dept. of Metallurgical and Materials Engineering, National Institute of Technology, Rourkela,

    Ray, P. K.
    Dept. of Mechanical Engineering, National Institute of Technology, Rourkela,

    Parhi, D. R. K
    Dept. of Mechanical Engineering, National Institute of Technology, Rourkela,


    Stock #: JTE101907

    ISSN: 0090-3973

    DOI: 10.1520/JTE101907

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    Author
    Title Application of Artificial Neural Network for Fatigue Life Prediction under Interspersed Mode-I Spike Overload
    Symposium , 0000-00-00
    Committee E08