## 7 Conclusions  
In this paper, the geometric relation between the milling tool geometry, milling conditions, and milling tool performance was established and verified. The following conclusions can be drawn.  
1. Among the many influencing factors, \(R_{a}\), \(\alpha\), \(R\), \(\gamma\), \(a_{p}\), and \(\beta\) are relatively significant, compared with the other factors, the influence of \(f\) and \(n\) were not as significant in this study, however, this may be affected by their respective ranges of variation. These important factors should be prioritized in the design and use of a tool to ensure consistency of the processing quality.
2. In many influential relationships, some factors either weaken or enhance the other, and the interaction characteristics of such factors should be utilized to balance the design during tool design or optimization.
3. Attention should be paid to the simultaneous changes of a certain couple of factors during the tool design or optimization process as a higher change rate of \(R_{a}\) may occur.
4. This research provides service performance quantification and a general method of cutting tool performance design. The method is shown to be feasible and can be used for tool performance design or optimization.  
Figure 17: Genetic algorithm (GA) optimization process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Tool Design Parameters**} & \multicolumn{2}{c}{**Tool Milling Parameters**} \\ \hline \(\gamma\) (\({}^{\circ}\)) & 5 & \(n\) (\(\pi\)/min) & 4083.6 \\ \(\alpha\) (\({}^{\circ}\)) & 10 & \(f\) (mm/min) & 482.2 \\ \(\beta\) (\({}^{\circ}\)) & 45.8 & \(a_{p}\) (mm) & 0.064 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GA optimization results.  
The material property parameters of the surface roughness model and the time-varying wear model proved to change dynamically with the processing in the experiments.  
The model was established without considering crushing, built-up edge, and the slowing effect of the compound formed by the chemical reaction on the flank surface. Additionally, during the simulation process, the variation of the formula coefficients under different milling temperatures and time periods was not considered and the change of the tool geometry was not corrected with time. These are the main error sources of the model and simulation results.  
Future research will focus on a material time-varying parameter prediction based on an algorithm to achieve tool performance and intelligent design.  
Visualization, validation, formal analysis, writing--original draft preparation, review and editing, Z.Z.; Investigation, resources, project administration, funding acquisition, X.L.; supervision, C.Y.; R.L.; software, H.Z.; Methodology, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by: [National Natural Science Foundation's international (region) cooperation and exchange project: The basis and application of intelligent cutting technology based on open CNC system] grant number [5171001055]. [National Ministry of Science and Technology, National key R & D plan. Network collaborative manufacturing and smart factory special project: \"Complex Tool Monitoring and Full Life Cycle Intelligent Management and Control Technology\"] grant number [2019 YFB1704800]. [National Natural Science Foundation of China (Youth Fund) Research on the Stability of Five-Axis Machining of Double-Turn Table with Weak Rigidity Thin walled Parts] grant number [51905137]. And The APC was funded by [5171001055].  
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of.