The study of metal removal rate and cutting temperature is most significant among the others like features of tools and work materials. Since these are the determinant factors of the production rate and cost-efficiency of the tools. Milling of hardened tool steels became a highly expensive for the manufacturing industries today as these are being widely used in many applications like automobile, structural, etc. A significant improvement in the efficiency of this process may be obtained with the development of mathematical relations between the set of input and output parameters of a machining process. In the first part of this investigation, CNC milling experiments are conducted to machine hardened EN 31 tool steel with carbide cutting inserters. Initially, the design of experiments was conducted to plan the experimentation by considering the machining variables of depth of cut, feed and spindle speed. Metal removal rate and cutting temperature were considered as the response variables to measure during the each experimental run. Response surface methodology was used to build the mathematical surface models for the measured values of responses. The ANOVA technique has been used to verify the adequacy of the models at 95% confidence interval. Since the influence of machining parameters on the metal removal rate and cutting temperature are with conflicting nature, the problem is considered as multi-objective optimization problem. In the second part, a multi-objective optimization algorithm Gray relational analysis (GRA) was adapted to the measured machining response values to obtain the optimal set of input parameters. Therefore, the present work enables the industries to perform the CNC milling operations on the hardened EN 31 material within the optimal levels of tool temperatures by maximizing the metal removal rate.
