Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network

Abstract

Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R2 and lower RMSE and MEP.