Title: Application of artificial intelligence for the prediction of delamination in drilling GFRP composites

Authors: B. Latha, V.S. Senthilkumar

Addresses: Faculty of Information and Communication Engineering, Anna University Chennai, Chennai – 600 025, India. ' Department of Mechanical Engineering, College of Engineering Guindy, Anna University Chennai, Chennai – 600 025, India

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.

Keywords: precision hole making; GFRP composites; artificial neural networks; ANNs; drilling; delamination; glass fibre reinforced polymer composites.

DOI: 10.1504/IJPTECH.2010.031660

International Journal of Precision Technology, 2010 Vol.1 No.3/4, pp.314 - 330

Published online: 16 Feb 2010 *

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