Multi-Objective Optimization of Finishing Milling of C45 Steel using Factorial Design

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Abstract
This study presents an innovative approach to optimizing the milling process of C45 steel using a factorial design of experiments. The impact of key technological parameters, including cutting speed (Vc), feed per tooth (fz), depth of cut (ap), and lubrication conditions (dry and flood), on surface roughness (Ra) and Material Removal Rate (MRR) was thoroughly analyzed. Through the application of Analysis of Variance (ANOVA), significant factors and their interactions were identified, with fz and lubrication conditions showing the most substantial influence on Ra. The interaction between fz and lubrication condition was particularly notable, highlighting the importance of these parameters in achieving optimal surface quality. Multi-objective optimization was conducted using the desirability function method to balance the objectives of minimizing Ra and maximizing MRR. The optimal Vc and fz conditions under flood lubrication were found 200 m/min and 0.3 mm/tooth, respectively, achieving a desirability index of 0.801. Under dry lubrication, the optimal conditions were 200 m/min and 0.3 mm/tooth, respectively, with a desirability index of 0.803. These results demonstrate that both lubrication conditions can be effectively optimized to enhance machining performance. The findings provide a comprehensive framework for improving the milling process of C45 steel, contributing valuable insights into the effects of cutting parameters and lubrication conditions on surface quality and MRR.

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