Application of artificial intelligence for the prediction of delamination in drilling GFRP composites Online publication date: Tue, 16-Feb-2010
by B. Latha, V.S. Senthilkumar
International Journal of Precision Technology (IJPTECH), Vol. 1, No. 3/4, 2010
Abstract: Glass fibre reinforced composite materials are used in different engineering fields due to their excellent properties. Precision hole making drilling operation is essentially needed for this composite to perform assembly. Delamination is one of the important problems associated with composite drilling process. Delamination after drilling leads to rejection and imposes heavy loss in production. In this paper, a neural network based on back-propagation (BP) algorithm with two hidden layers are used for the modelling of delamination factor in drilling glass fibre reinforced plastic (GFRP) composites. The input-output data sets required for training are obtained from drilling experimentation. Fifty-four sets of data were used for training and 18 sets of data were used for testing. Residuals were used for checking the performance. The result shows that the well trained BP network model can precisely predict the delamination in drilling of GFRP composites.
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