AISI D6 is a cold work tool steel that is widely used in cutting, drawing, deep drawing tools and other similar applications. This steel has a very high wear resistance and very poor machinability. With regard to wide usage of this steel, it is necessary to economize AISI D6 machining process. The goal of this research work is to optimize AISI D6 machining process with regard to machining forces and surface roughness. The aim of this optimization is to introduce cutting conditions under which minimum machining forces and surface roughness are achieved. Several experiments were planned based on full factorial design (FFD) with using analysis of variance (ANOVA) and the effect of machining parameters on surface roughness and machining forces is determined. Such an experiment allows studying the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable. Then the attempt has been made to model the machining forces and surface roughness through the response surface methodology(RSM). Response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Researchers suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but use it because such a model is easy to estimate and apply, even when little is known about the process. At last the optimum cutting condition has been introduced with using genetic algorithm (GA). Genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Furthermore, machining process has been simulated using finite element method. In this simulation, effect of tool nose radius and depth of cut on radial and cutting forces has been investigated. Key words AISI D6 cold work tool steel, analysis of variance, genetic algorithm, response surface methodology.
