Experimental study on peck drilling of GFRP and prediction of drilling-induced damage using ANN

Abstract

This study investigated the effect of cutting speed, pecking depth and feed rate on drilling of glass fiber reinforced polymer composite materials. Glass fiber reinforced polymer composite parts has been started to get extensively used at the industry of space, aviation, ship, chemistry and automotive nowadays. Other than these fields of usage, at processing composite parts by machining, some problems have been revealed due to the anisotropic structure that the composite materials have. Drilling-induced damage is a serious problem in laminated composite materials. The worldwide research and development efforts have been focused on the area, but a few numbers of studies have investigated peck drilling. In this study, the effect of the above mentioned parameters was investigated and damage factor was estimated using Artificial Neural Network. The artificial neural network topology has been adopted as a predictive tool. The feed rate, cutting speed, pecking depth and damage place have been used as the input parameters. The drilling-induced damage was the output. The experimental data for drilling of woven glass-fiber-reinforced plastic composite laminates were used for training and testing the model. The results of the predictive model have been found to be in good agreement with the test data. ©2011 Academic Journals.