Studies on Effect of Cutting Parameters on Surface Roughness of Al-Cu-TiC MMCs: An Artificial Neural Network Approach

Abstract

An artificial neural network model of ‘Feed Forward Back Propagation’ type is developed for the analysis and prediction of surface roughness, the relationship between cutting and process parameters of Al-4.5Cu-1.5TiC Metal Matrix Composites. The effect of the process parameters namely, Cutting speed, feed, depth of cut upon the responses like: surface roughness parameter Ra, Rz and Rt of Al-4.5Cu-1.5TiC MMC are analyzed during this investigation. The Experiments have been carried out as per Taguchi's L25 orthogonal array with five levels defined for each of the factors for developing the knowledge base for ANN training. To have all the data in a same scale the experimental results have been normalized before being used in the Artificial Neural Network model.