Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al-SiCp metal matrix composites and ANOVA analysis

Abstract

In the present work, surface roughness prediction model in cylindrical grinding of LM25/SiC/4p metal matrix composites (MMC) was developed using artificial neural network (ANN) methodology. The independent input machining parameters considered in the modeling were wheel velocity, feed, work piece velocity and depth of cut. The neural network architecture 4-12-1 with logsig transfer function was found optimum with 94.20 % model accuracy. The analysis of variance (ANOVA) was carried to study influence of the machining parameters on surface roughness. The study revealed higher F-ratio for wheel velocity and it found to be the most influencing parameter in prediction of surface roughness. The percentage of contribution for wheel velocity was 32.47 %, feed was 26.50 % and work piece velocity was 25.08 %. The depth of cut was found to have least effect on surface roughness with 13.22 % contribution. The independent and combined effect of process parameters on predicted value of surface roughness was studied using two-dimensional graphs and surface plots. The study showed that surface roughness increases as feed increases while it decreases with increase in wheel velocity. It was also observed that minimum surface finish could be obtained at high wheel and work piece velocities, and low feed and depth of cut.