## 5 Conclusions  
In order to improve the processing quality such as surface burrs and cutting damage caused by the cutting force during rotary-vibration-cutting honeycomb-core material, several key technologies involved in finite element simulation of honeycomb-core material model were studied in this paper, following conclusions could be obtained:  
1. The three-dimensional finite element simulation model of disc-cutter which used in honeycomb-core material ultrasonic vibration-assisted cutting process was established, the material properties and material failure criteria were studied in detail;
2. The validation experiment was executed in order to verify the simulation resulted, the relative error between simulation results and experimental results was less than 13.2%;
3. The cutting force prediction model was established though the regression analysis based on the simulation results. According to the cutting force model, the influence of cutting parameters on cutting force could be predicted; the processing quality and the processing efficiency could be improved due to cutting parameters regulation;
4. By analyzing the influence of cutting parameters on the cutting force, it was concluded that larger cutting depth and smaller cutting width had better cutting performance which was reflected in less burrs and better surface integrity.  
Conceptualization, writing--original draft, W.C.; funding acquisition, writing--review & editing, J.Z.; supervision, Y.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the Science and Technology Planning Project of Shenzhen Municipality (JCYJ20180306170733170), National Science and Technology Major Project of the Ministry of Science and Technology of China (2018 ZX04002-001). The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sources of** & **Sum of** & **Degrees of** & **Mean** & **F** & **P** \\  & **Variance** & **Squares** & **Freedom** & **Square** & **F** & **(Prob \textgreater{} F)** \\ \hline \multirow{5}{*}{Fx} & Model & 27.71 & 9 & 3.08 & 21.56 & \textless{}0.0001 \\ \cline{2-6}  & residual & 1.43 & 10 & 0.14 & & \\ \cline{2-6}  & Loss of quasi & 0.53 & 5 & 0.11 & 0.59 & 0.7098 \\ \cline{2-6}  & Pure error & 0.90 & 5 & 0.18 & & \\ \cline{2-6}  & Sum & 29.14 & 19 & & & \\ \cline{2-6}  & & \multicolumn{4}{c}{R\({}^{2}\) = 0.9510, After the adjustment: R\({}^{2}\) = 0.9069} \\ \hline \multirow{5}{*}{Fy} & Model & 37.66 & 9 & 4.18 & 24.61 & \textless{}0.0001 \\ \cline{2-6}  & residual & 1.7 & 10 & 0.17 & & \\ \cline{2-6}  & Loss of quasi & 1.27 & 5 & 0.25 & 2.99 & 0.1271 \\ \cline{2-6}  & Pure error & 0.43 & 5 & 0.085 & & \\ \cline{2-6}  & Sum & 39.36 & 19 & & & \\ \cline{2-6}  & & \multicolumn{4}{c}{R\({}^{2}\) = 0.9568, After the adjustment: R\({}^{2}\) = 0.9179} \\ \hline \multirow{5}{*}{\(Fz\)} & Model & 32.27 & 9 & 3.59 & 9.14 & 0.0009 \\ \cline{2-6}  & residual & 3.92 & 10 & 0.39 & & \\ \cline{2-6}  & Loss of quasi & 3.14 & 5 & 0.63 & 4.01 & 0.0767 \\ \cline{2-6}  & Pure error & 0.78 & 5 & 0.16 & & \\ \cline{2-6}  & Sum & 36.19 & 19 & & & \\ \cline{2-6}  & & \multicolumn{4}{c}{R\({}^{2}\) = 0.8916, After the adjustment: R\({}^{2}\) = 0.7940} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Regression model analysis of variance.