Optimization of cutting parameters in micro end milling operations in dry cutting condition using genetic algorithms

Abstract

Micro end milling with fine grained carbide end mills is an economical way to machine small and medium lots of micro components. Considering the sensitivity of the slender end mills which are very costly compared to conventional end mills, it is imperative that the machining parameters are chosen appropriately so as to ensure maximum tool life and minimum possible production cost without violating any of the imposed constraints. Unlike in conventional end milling operations the tool life in micro end milling operations increases with axial depth of cut till it equals the tool diameter and this makes it even difficult to ascertain the appropriate depth of cut to machine a specific component. In this paper the influence of depth of cut on tool life is illustrated and depth of cut is also considered as one of the decision variables in the optimization problem. More over in this paper Genetic Algorithms (GA) based on natural laws of evolution is used to optimize the cutting parameters. Finally a test case is presented to give a clear picture of the application of the optimization algorithm.