Prediction of defects in castings using back propagation neural networks Online publication date: Tue, 08-Jul-2008
by D. Benny Karunakar, G.L. Datta
International Journal of Modelling, Identification and Control (IJMIC), Vol. 3, No. 2, 2008
Abstract: Defects in castings often lead to rejection, which would result in loss of productivity. Expert systems developed by some investigators mostly act as postmortem tools, discussing and analysing a defect after it has occurred. A tool that could predict all the possible defects before the castings are actually made is not yet developed, which would be highly useful in the shop floor. Hence, in the present work, an attempt has been made to predict the major casting defects using back propagation neural networks. The neural network was trained with moulding properties, composition of the charge and melting conditions as the inputs and the nature of the defects as outputs. After the training was over, the set of inputs of the casting that is going to be made, were 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 specified by the neural network.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook