## 4 Discussion  
In this paper, the principal research parameters are the spindle speed, \(n\), the feed rate, \(v_{\text{f}}\), and the axial milling depth, \(a_{\text{p}}\). These parameters are crucial process variables in CNC milling and directly exert an influence on tool wear and machining quality. Specifically, the spindle speed, \(n\), determines the frequency of interaction between the tool and the workpiece, while the feed rate, \(v_{\text{f}}\), is associated with the material removal rate and surface roughness. Additionally, the axial milling depth, \(a_{\text{p}}\), has an impact on the distribution of cutting forces and the loading condition of the tool. Thus, appropriately setting these parameters holds significant value for tool machining.  
Through orthogonal experiments, representative level combinations were chosen, and the patterns of the wear of the rake face and milling force alterations under diverse parameter combinations were disclosed through in-depth data analysis. The primary and secondary factors influencing the wear of the rake face and milling force were identified by employing range analysis. The findings indicate that the influence of milling parameters on the wear width follows this order: spindle speed \(n>\) feed speed \(v_{\text{f}}>\) axial milling depth \(a_{\text{p}}\). In terms of milling force, the influence of the parameters is ranked as follows: axial milling depth \(a_{\text{p}}>\) feed speed \(v_{\text{f}}>\) spindle speed \(n\). A model for tool wear and a multi-quadratic regression model for milling force were put forward to predict the variations in tool wear and milling force. The fitting results demonstrate that the coefficient of determination R\({}^{2}\) for the tool wear model and the milling force model are 0.989 and 0.904, respectively, indicating that these models exhibit high predictive accuracy and reliability.  
In response to the conflicting problems regarding milling force, tool wear, and milling efficiency in mechanical processing, a multi-objective optimization strategy based on genetic algorithms was put forward, effectively resolving the contradiction. Considering the variations in emphasis during actual processing, specific milling parameter combinations were presented. The results indicate that, depending on the processing priorities, the following milling parameter combinations are recommended: To prioritize milling efficiency, use \(n=\) 13,750.264 r/min, \(v_{\text{f}}=\) 1999.996 mm/min, and \(a_{\text{p}}=\) 5 mm; to minimize tool wear, use \(n=\) 12,000 r/min, \(v_{\text{f}}=\) 1000 mm/min, and \(a_{\text{p}}=\) 3 mm; to balance efficiency and wear, use \(n=\) 13,191.062 r/min, \(v_{\text{f}}=\) 1770.6876 mm/min, and \(a_{\text{p}}=\) 3.718 mm.  
In the future, we need to persistently pay attention to the potential influence of emerging technologies, such as big data and artificial intelligence, on multi-objective optimization and contemplate how to address the new issues brought about by technological transformation. Additionally, we should explore effective strategies and methods for multi-objective optimization under circumstances with uncertain factors such as parameter fluctuations and environmental changes.