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    Modelling of wrinkling in deep drawing of different grades of annealed commercially pure aluminium sheets when drawn through a conical die using artificial neural network

    New search for: Sivasankaran, S.
    New search for: Narayanasamy, R.
    New search for: Jeyapaul, R.
    New search for: Loganathan, C.

    AbstractThis paper presents an artificial neural network (ANN) model for predicting and avoiding surface failure such as wrinkling of sheet metals. Commercially pure aluminium sheets of different grades are drawn into cylindrical cups through conical die. An ANN model has been developed to map the mechanical properties and instantaneous geometry features of deep drawing process. This model is a feed forward backpropagation neural network and is trained and tested with pairs of inputs/outputs data. A very good performance of the neural network, in terms of agreement with the experimental data has been achieved. This model can be used for analysis and prediction of onset of wrinkling.

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    Modelling of wrinkling in deep drawing of different grades of annealed commercially pure aluminium sheets when drawn through a conical die using artificial neural network

    New search for: Sivasankaran, S.
    New search for: Narayanasamy, R.
    New search for: Jeyapaul, R.
    New search for: Loganathan, C.

    AbstractThis paper presents an artificial neural network (ANN) model for predicting and avoiding surface failure such as wrinkling of sheet metals. Commercially pure aluminium sheets of different grades are drawn into cylindrical cups through conical die. An ANN model has been developed to map the mechanical properties and instantaneous geometry features of deep drawing process. This model is a feed forward backpropagation neural network and is trained and tested with pairs of inputs/outputs data. A very good performance of the neural network, in terms of agreement with the experimental data has been achieved. This model can be used for analysis and prediction of onset of wrinkling.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Modelling of wrinkling in deep drawing of different grades of annealed commercially pure aluminium sheets when drawn through a conical die using artificial neural network

    New search for: Sivasankaran, S.
    New search for: Narayanasamy, R.
    New search for: Jeyapaul, R.
    New search for: Loganathan, C.

    AbstractThis paper presents an artificial neural network (ANN) model for predicting and avoiding surface failure such as wrinkling of sheet metals. Commercially pure aluminium sheets of different grades are drawn into cylindrical cups through conical die. An ANN model has been developed to map the mechanical properties and instantaneous geometry features of deep drawing process. This model is a feed forward backpropagation neural network and is trained and tested with pairs of inputs/outputs data. A very good performance of the neural network, in terms of agreement with the experimental data has been achieved. This model can be used for analysis and prediction of onset of wrinkling.

    Access

    Check access
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    Order at Subito €

    Access

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    Title:

    Modelling of wrinkling in deep drawing of different grades of annealed commercially pure aluminium sheets when drawn through a conical die using artificial neural network

    Contributors:

    Sivasankaran, S. (author) / Narayanasamy, R. (author) / Jeyapaul, R. (author) / Loganathan, C. (author)

    Publication date:

    2009-01-15

    Size:

    13 pages

    ISSN:

    0261-3069

    DOI:

    https://doi.org/10.1016/j.matdes.2009.01.020

    Type of media:

    Article (Journal)

    Type of material:

    Electronic Resource

    Language:

    English

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