Multi-response optimization of hard milling process: RSM coupled with grey relational analysis

This paper presents the effect of process parameters on cutting forces, workpiece surface temperature and sound pressure level in hard milling of 100MnCrW4 tool steel using TiN+TiAlN coated WC inserts. A response surface methodology based box-behnken design coupled with grey relational analysis was utilized for statistical optimization. The individual weight of each quality characteristic is determined by employing the entropy measurement method and a GRG obtained from the GRA is used to optimize the hard milling process. The optimum process parameters are determined by the GRG as the overall performance index. An empirical relationship was established to predict the GRG by incorporating independently controllable hard milling process parameters effectively. In ANOVA indicate, the work material hardness, feed rate per tooth and axial depth of cut showed as the most influential factors associated with the grey relational grade. To validate the study, confirmation experiment has been carried out at optimal set of parameters, and predicted results have been found to be in good agreement with experimental findings. The optimal results show that the RSM-Grey relational