Prediction of Surface Texture Characteristics in Turning of FRPs Using ANN

Abstract

The objective of the present study is to develop an artificial neural network (ANN) in order to predict surface texture characteristics for the turning performance of a fiber reinforced polymer (FRP) composite. Full factorial design of experiments was designed and conducted. The process parameters considered in the experiments were cutting speed and feed rate, whilst the depth of cut has been held constant. The corresponding surface texture parameters that have been studied are the Ra and Rt. A feed forward back propagation neural network was fitted on the experimental results. It was found that accurate predictions of performance can be achieved through the feed forward back propagation (FFBP) neural network developed for the surface texture parameters. © Springer-Verlag Berlin Heidelberg 2013.