Prediction of Hydroforming Characteristics using Random Neural Networks |
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Authors: | M A Karkoub |
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Affiliation: | (1) Mechanical Engineering Department, The Petroleum Institute, UAE |
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Abstract: | In recent years, hydroforming has become the topic of a lot of active research. Researchers have been looking for better procedures
and prediction tools to improve the quality of the product and reduce the prototyping cost. Similar to any other metal forming
process, hydroforming leads to non-homogeneous plastic deformations of the workpiece. In this paper, a model is developed
to predict the amount of deformation caused by hydroforming using random neural networks (RNNs). RNNs learn the behavior of
a system from the provided input/output data in a manner similar to the way the human brain does. This is different from the
usual connectionist neural network (NN) models which are based on simple functional analyses. Experimental data is collected
and used in training as well as testing the RNNs. The RNN models have feedforward architectures and use a generalized learning
algorithm in the training process. Multi-layer RNNs with as few as six neurons were used to capture the nonlinear correlations
between the input and output data collected from an experimental setup. The RNN models were able to predict the center deflection,
the thickness variation, as well as the deformed shape of circular plate specimens with good accuracy.
Received: February 2004 / Accepted: September 2005 |
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Keywords: | Hydroforming Plastic deformation Circular plate Random neural networks |
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