Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

Abstract

In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, 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. A trend that has emerged to solve these problems is machining without fluid – a method called dry machining – which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi’s experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.