alexa Prevention of defects in castings using back propagation neural networks
Engineering

Engineering

Advances in Automobile Engineering

Author(s): D Benny Karunakar, G L Datta

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Defects in castings often lead to rejection, which would ultimately result in loss of productivity for a foundry. Expert systems developed by some researchers mostly act as postmortem tools, discussing and analyzing a defect after it has occurred. Though some investigators have attempted to predict a few important defects, a tool that could predict all the possible defects just before the castings are made has not yet been developed. Hence in the present work, an attempt has been made to predict major casting defects like cracks, misruns, scabs, blowholes and air-locks using back-propagation neural networks from the data collected from a steel foundry. The neural network was trained with parameters like green compression strength (GCS), green shear strength (GSS), permeability, moisture percent, composition of the charge and melting conditions as inputs and the presence/absence of defects as outputs. After the training was over, the set of inputs of the casting that is going to be made was fed to the network and the network could predict whether the casting would be sound or defective. If defective, the nature of the defect was also specified by the neural network. The neural network could predict cracks, misruns and air-locks accurately in most of the cases. The neural network could also predict other defects successfully. Investigating the causes followed by altering the moulding parameters and appropriate treatment of the molten metal can prevent the defects that were predicted by the backpropagation neural network.

This article was published in springer and referenced in Advances in Automobile Engineering

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