A genetic algorithmic approach for optimization of surface roughness prediction model in dry milling

Majority of machining operations require the cooling and lubricating action of cutting fluids. But, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. In the present work, experimental studies have been conducted to see the effect of tool geometry ( radial rake angle and nose radius) and cutting conditions ( cutting speed and feed rate) on the machining performance in dry milling with four fluted solid TiAlN coated carbide end mill cutters. The significance of process parameters on surface finish has been evaluated using analysis of variance. Mathematical models have been developed for surface roughness prediction using Response Surface Methodology. Then the optimization has been carried out with Genetic Algorithms using the surface roughness models developed and validated in this work. This methodology helps to obtain the best possible tool geometry and cutting conditions for dry milling.