Prediction of drilling induced delamination and circularity deviation in GFRP nanocomposites using deep neural network

Abstract

Drilling of Glass Fiber Reinforced Polymer (GFRP) nanocomposites is most prevailing topic to understand the composite behaviour under different cutting conditions. The present study is mainly focused on prediction of drilling output responses such as delamination factor and circularity error randomly with the help of deep neural network (DNN) model. L9 orthogonal array is used for experimentation. Drilling operation is performed on 0.3 wt% multi-walled carbon nano tubes reinforced GFRPs with solid carbide, TiCN and TiAlN coated (6 mm- diameter) twist drills. Based on experimental results, two different deep neural network models are prepared with single and double hidden layers by varying node numbers such as 8, 16, 32, 64, and 128. Thrust force, Acoustic Emission RMS voltage, and drill type (coating) are considered as input to the neural network and delamination factor at exit, circularity error are treated as predicted output responses for the given network model. The revealed predicted results recommended that two hidden layers with 32 nodes network model give the lowest absolute error of 0.08% and 3.13% in delamination factor and circularity errors respectively. Similarly, the highest absolute error is identified as 4.19% in delamination factor and 13.14% in circularity error by single hidden layer with 128 nodes. Therefore, it is urged that DNN is the most suitable modelling technique for prediction of drilling responses on GFRP nano composites