Improving milling performances is an effective solution to decrease the costs required. This paper addressed a multi-response optimization to simultaneously decrease the machining power consumed Pm, arithmetical roughness Ra, and ten-spot roughness Rz. The Grey-Response Surface Method-Multi Island Genetic Algorithm (GRMA) consisting of grey relational analysis (GRA), response surface method (RSM), and multi-island genetic algorithm (MA) was proposed to predict the optimal parameters and yield optimum milling performances. The experimental trials were conducted with the support of a CNC milling center. The influences of spindle speed (S), depth of cut (ap), feed rate (fz), and tip radius (r) were explored using GRA. The nonlinear relationship between machining parameters and grey grade (GG) model was developed using RSM. Finally, two optimization techniques, including desirability approach (DA) and MA were performed to observe the optimal values. The results indicated that the machining power was greatly affected by processing factors and the radius has a significant impact on the roughness criteria. The measured reductions using optimal parameters of Pm, Ra, and Rz are approximately 77.05%, 50.00%, and 58.02%, respectively, as compared to initial settings. The GRMA can be considered as an effective approach to generate reliable values of processing conditions and technological performances in the milling process.

Improving the technical parameters of the machining process is an effective solution to save manufacturing costs. The purpose of this work is to decrease energy consumption (EC) and average surface roughness(ASR) for the milling process of AISI H13 steel. The spindle speed (S), depth of cut (a), and feed rate (f) were the processing inputs. The milling runs were performed using the experimental plan generated by the Box-Behnken method approach. The relationships between inputs and outputs were established using the response surface models (RSM). The desirability approach (DA) was used to observe the optimal values. The results showed that the reductions of EC and ASR are approximately 33.75% and 40.58%, respectively, as compared to the initial parameter setting. In addition, a hybrid approach using RSM and DA can be considered as a powerful solution to model the milling process and obtain a reliable optimal solution.

Improving the technical parameters of the machining process is an effective solution to save manufacturing costs. The purpose of this work is to decrease energy consumption (EC) and average surface roughness(ASR) for the milling process of AISI H13 steel. The spindle speed (S), depth of cut (a), and feed rate (f) were the processing inputs. The milling runs were performed using the experimental plan generated by the Box-Behnken method approach. The relationships between inputs and outputs were established using the response surface models (RSM). The desirability approach (DA) was used to observe the optimal values. The results showed that the reductions of EC and ASR are approximately 33.75% and 40.58%, respectively, as compared to the initial parameter setting. In addition, a hybrid approach using RSM and DA can be considered as a powerful solution to model the milling process and obtain a reliable optimal solution.