## 2 Materials and Methods
### Cutting Simulation Single-Factor Experiment Results
#### 3.2.4 Quadratic Regression Orthogonal Combination Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & **Source of** & **Square** & **Degrees of** & **Mean** & \multirow{2}{*}{_F-Value_} & \multirow{2}{*}{_p-Value_} \\  & **the Variance** & **Sum** & **Freedom** & **Square** & & \\ \hline \multirow{8}{*}{\(Y_{1}\)} & Model & 1503.62 & 9 & 167.07 & 1811.75 & \textless{}0.0001 ** \\  & \(x_{1}\) & 152.28 & 1 & 152.28 & 1651.37 & \textless{}0.0001 ** \\  & \(x_{2}\) & 645.53 & 1 & 645.53 & 7000.35 & \textless{}0.0001 ** \\  & \(x_{3}\) & 677.22 & 1 & 677.22 & 7344.02 & \textless{}0.0001 ** \\  & \(x_{1}x_{2}\) & 0.9773 & 1 & 0.9773 & 10.6 & 0.014 * \\  & \(x_{1}x_{3}\) & 0.8475 & 1 & 0.8475 & 9.19 & 0.0191 * \\  & \(x_{2}x_{3}\) & 3.76 & 1 & 3.76 & 40.82 & 0.0004 ** \\  & \(x_{1}^{2}\) & 0.3184 & 1 & 0.3184 & 3.45 & 0.1055 * \\  & \(x_{2}^{2}\) & 4.56 & 1 & 4.56 & 49.43 & 0.0002 ** \\  & \(x_{3}^{2}\) & 19.17 & 1 & 19.17 & 207.85 & \textless{}0.0001 ** \\  & Residual & 0.6455 & 7 & 0.0922 & & \\  & Lost item & 0.6455 & 3 & 0.2152 & & \\  & Aggregate & 1504.26 & 16 & & & \\ \hline \multirow{8}{*}{\(Y_{2}\)} & Model & 111.97 & 9 & 12.44 & 288.55 & \textless{}0.0001 ** \\  & \(x_{1}\) & 6.35 & 1 & 6.35 & 147.40 & \textless{}0.0001 ** \\  & \(x_{2}\) & 50.40 & 1 & 50.40 & 1169.06 & \textless{}0.0001 ** \\  & \(x_{3}\) & 43.24 & 1 & 43.24 & 1003.01 & \textless{}0.0001 ** \\  & \(x_{1}x_{2}\) & 1.07 & 1 & 1.07 & 24.82 & 0.0016 ** \\  & \(x_{1}x_{3}\) & 0.8379 & 1 & 0.8379 & 19.44 & 0.0031 ** \\  & \(x_{2}x_{3}\) & 5.66 & 1 & 5.66 & 131.18 & \textless{}0.0001 ** \\  & \(x_{1}^{2}\) & 0.0003 & 1 & 0.0003 & 0.0078 & 0.9321 * \\  & \(x_{2}^{2}\) & 4.13 & 1 & 4.13 & 95.79 & \textless{}0.0001 ** \\  & \(x_{3}^{2}\) & 0.1523 & 1 & 0.1523 & 3.53 & 0.1023 * \\  & Residual & 0.3018 & 7 & 0.0431 & & \\  & Lost item & 0.3018 & 3 & 0.1006 & & \\  & Aggregate & 112.27 & 16 & & & \\ \hline \end{tabular} Note: ** means extremely significant impact (\(p<0.01\)); * represents significant impact (\(0.01\leq p<0.05\)); * denotes no significant impact (\(p>0.05\)).  
\end{table} 
Table 7: Variance analysis of the experimentally observed data. On this basis, in order to be able to visually analyze the relationship between the changes of each test factor and the cutting rate of the device, the contour plots of the effects of the crank length, crank rotation speed, and forward speed on both the repeated-cutting rate and the miss-cutting rate are obtained via the Design-Expert software, as illustrated in Figure 17. Figure 17a-c presents the effects relationship plots of the test factors on the repeated-cutting rate, while Figure 17d-f demonstrates the effects relationship plots of the test factors on the miss-cutting rate. As presented in Figure 17a, the variation interval of the repeated-cutting rate of the cutting device operation is larger when the crank rotation speed is varied, indicating that the effect of the crank rotation speed on the repeated-cutting rate is more significant than the effect of the crank length on the repeated-cutting rate. As illustrated in Figure 17b, the variation interval of the repeated-cutting rate of the cutting device operation is larger when the forward speed is varied, indicating that the effect of the forward speed on the repeated-cutting rate is significant. Figure 17c-f is analyzed by following the same procedure mentioned above and, for the sake of brevity, its details have been not provided. In addition, the magnitude of the coefficient value of each factor in the regression equation reflects the magnitude of the effect of the factor on the test index, and the sign of the coefficient signifies the positive or negative impact of the factor on the test index [30; 31].