Application of Artificial Neural Network for the Prediction of Surface Roughness in Drilling GFRP Composites

Abstract:

Composite materials are used in different fields, due to their excellent properties. Glass fiber reinforced composite materials are used in aerospace, automobile, sport goods, etc. Joining by drilling operation is necessary for this composite to perform assembly. Surface roughness of the holes plays an important role in mechanical joints. Good surface leads to the precision fits and efficient joints. The present article discusses the use of artificial neural network (ANN) for the prediction of surface roughness in drilling glass fiber reinforced plastic (GFRP) composites. The experiments are carried out on computer numeric control machining center. The results indicated that the well-trained ANN model could able to predict the surface roughness in drilling of GFRP composites.

• Periodical: Materials Science Forum (Volume 766)
• Pages: 21-36
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.766.21
• Online since: July 2013
• Authors: K. Palanikumar, B. Latha, V.S. Senthilkumar, J. Paulo Davim
• Keywords: Artificial Neural Network (ANN), GFRP Composites, Hole Making, Surface Roughness (SR)

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