Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network

This paper presents the prediction and evaluation of thrust force and surface roughness in drilling of composite material using candle stick drill. The approach is based on Taguchi method and the artificial neural network. The experimental results indicate that the feed rate and the drill diameter are the most significant factors affecting the thrust force, while the feed rate and spindle speed contribute the most to the surface roughness. In this study, the objective was to establish a correlation between the feed rate, spindle speed and drill diameter with the induced thrust force and surface roughness in drilling composite laminate. The correlations were obtained by multi-variable regression analysis and radial basis function network (RBFN) and compared with the experimental results. The results indicate the RBFN is more effective than multi-variable regression analysis.