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The present work investigates the effect and optimization of process parameters on the cutting temperature, tool wear and metal removal rate during hard milling of 100MnCrW4 (AISI O1) tool steel using (TiN/TiAlN) coated carbide tools. The central composite rotatable design is utilized to plan the experiments. The empirical models were developed and analysis of variance tests were used for the investigation of significant parameters and adequacy of models. Scars, adhered materials and coating peel off were observed on the rake face of the tool due to the existence tool flank wear through the SEM and EDX analysis. In order to seek optimal parameters, a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted and a set of Pareto-optimal solution set was obtained. Further, a set of confirmation experiments were conducted and the adopted optimization method was proved to be feasible. Also, the results would be more useful to guide the actual hard milling process parameters for predicting the responses.

Investigations and optimization for hard milling process parameters using hybrid method of RSM and NSGA-II