# Design and Experimental Study of the General Mechanical Pneumatic Combined Seed Metering Device
## 4 Seed Performance Test
### Multivariate Test
#### 4.4.2 Test Results and Establishment of Regression Model  
The test results are listed in Table 2, and the test results were analyzed by the variance analysis, with the analysis results are listed in Table 3. As indicated from Table 3, the P value of the regression model for the qualified index, the replay index, and the missed index were all lower than 0.01, and the model could be effective through the significance test. In the qualified seeding index model, \(X_{1}\), \(X_{2}\), \(X_{1}^{2}\), \(X_{2}^{2}\), and \(X_{3}^{2}\) significantly affected the equation, and the other terms insignificantly impacted the equation; in the replay seeding index model, \(X_{1}\), \(X_{2}\), \(X_{1}\)\({}^{2}\), \(X_{2}\)\({}^{2}\), and \(X_{3}\)\({}^{2}\) significantly affected the equation, and the remaining terms significantly affected the equation. The effect was insignificant; in the missed seeding index model, \(X_{2}\), \(X_{1}\)\({}^{2}\), \(X_{2}\)\({}^{2}\), and \(X_{3}\)\({}^{2}\) significantly affected the equation, and the other terms insignificantly affected the equation. With the insignificant coefficients in the regression equation excluded, the regression equations for the respective factor and the qualified index, replay index, and missed index are written below:  
\[\left\{\begin{array}{c}A=97.45+1.52X_{1}-2.44X_{2}-3.64X_{1}^{2}-4.55X_{2}^{ 2}-3.11X_{3}^{2}\\ D=0.47-1.66X_{1}+0.89X_{2}+1.18X_{1}^{2}+1.42X_{2}^{2}+1.36X_{3}^{2}\\ M=2.09+1.55X_{2}+2.45X_{1}^{2}+3.13X_{2}^{2}+1.75X_{3}^{2}\end{array}\right\} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & & \\ \cline{2-4}
**Levels** & **Clearing Airflow Velocity** & **Speed of Feeding Drum** & **Cone Angles of Filling Holes** \\  & \(X_{1}\)/m/s** & \(X_{2}\)/r-min\({}^{-1}\) & \(X_{3}\)\({}^{c}\) \\ \hline
1.682 & 21 & 23.4 & 43.4 \\
1 & 19 & 20 & 40 \\
0 & 16 & 15 & 35 \\ \(-1\) & 13 & 10 & 30 \\ \(-1.682\) & 11 & 6.6 & 26.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coding with factors and levels.  
According to the regression results, the significant effect of the seed cleaning air speed \(X_{1}\), the seed feeding drum speed \(X_{2}\), the seed filling hole cone angle \(X_{3}\) on the qualified seeding index A, from large to small, could be the seed feeding drum speed \(X_{2}\), the cleaning air flow speed \(X_{1}\), and the seed filling hole cone angle \(X_{3}\); the order of the significant influence on replay seeding index D was from large to small as: clearing airflow velocity \(X_{1}\), seed conveying drum speed \(X_{2}\), and seed hole cone angle \(X_{3}\);  
The significant effect on missed index M was in order from large to small. It could be the rotation speed of the feeding drum \(X_{2}\), the cleaning airflow speed \(X_{1}\), as well as the seed filling hole cone angle \(X_{3}\). The qualified seeding index could be the critical performance index of the seed metering device. Given the regression results, the corresponding response surface was drawn, and the influence law of the test factors on the qualified seeding index was analyzed (Figure 11).  
The qualified seeding index first increased slowly and then decreased with the increase in the speed of the seed feeding drum; it first increased and then decreased slowly with the increase in the seed cleaning air velocity; it first slowly increased and then decreased slowly with the increase in the cone angle of the hole.