# Experiment and Research on Cutting Mechanical Properties of Little Cabbage
## 3 Results
### Results of Response Surface Method Test  
The response surface cutting test was carried out using Design-Expert 10.0.3 software and the central composite design scheme on the basis of the single factor test. The test was carried out in 20 groups, and each group of tests was carried out five times, and the average values of the test were selected as the evaluation indexes. The test results are shown in Table 3.  
Multivariate regression fitting analysis was performed on the test results, and a quadratic polynomial model of the independent variables such as the maximum cutting stress and specific cutting energy on the oblique angle, sliding angle, and cutting gap was established. After removing the insignificant items, quadratic polynomial models were obtained, as shown in Equations (8) and (9), respectively. \[\tau=8.22-0.77\alpha-0.68\theta+0.43d+0.45\theta d+0.59\alpha^{2}+0.30\theta^ {2}+0.85d^{2} \tag{8}\]  
Figure 5: Effect of different factors on maximum cutting stress and specific cutting energy. (**a**) Effect of different oblique angles on maximum cutting stress and specific cutting energy. (**b**)Effect of different sliding angles on maximum cutting stress and specific cutting energy. (**c**) Effect of different cutting gaps on maximum cutting stress and specific cutting energy. Through the analysis of variance (ANOVA) in Table 4, it can be seen that the \(p\) value of the maximum cutting stress \(\tau\) model was less than 0.01, indicating that the regression equation model was significant. The lack-of-fit term of the regression equation was 0.095, which was not significant, indicating that the model had high fitting accuracy. The coefficient of determination \(R^{2}\) was 93.87%, indicating that the regression equation of the maximum cutting stress could explain more than 90% of the evaluation indicators, and the model had high reliability, so the model can be used to study the optimal parameter combination of the cutter. The \(p\) value reflects the influence degree of each factor on the model. The \(p\) values of the three factors of oblique angle \(\alpha\), sliding angle \(\theta\), and cutting gap \(d\) were 0.0001, 0.0004, and 0.0078, respectively, all less than 0.01, indicating that the three factors had a very significant influence. The influence of each factor on the maximum cutting stress was the oblique angle, the sliding angle, and the cutting gap in descending order [22; 23]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{5}{c}{\(\tau\)} & \multicolumn{5}{c}{Es} \\ \cline{2-10}  & **Sum of** & **Squires** & **Freedom** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Sum of** & **Freedom** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 34.67 & 9 & 3.85 & 17.00 & \(<\)0.0001 & 0.16 & 9 & 0.017 & 14.07 & 0.0001 \\ \(\alpha\) & 8.09 & 1 & 8.09 & 35.71  
The results of variance analysis of cutting energy were analyzed. It can be seen that the \(p\) value of the model was 0.0001, indicating that the model was significant, and the lack-of-fit term of the regression equation was greater than 0.05, and the lack-of-fit term was insignificant. The coefficient of determination of the model was 92.68%, indicating that the regression equation could explain 92.68% of the evaluation indicators, and the model was relatively reliable. The significance of each factor was analyzed according to the \(p\) value, where the influence of each factor on the cutting energy was significant (\(p<0.05\)), and the cutting gap had the greatest influence, followed by the oblique angle and the sliding angle [24]. The influence of the interaction among the factors on the evaluation index were discussed. Design-Expert 10.0.3 software was used to analyze the influence of the interaction between the sliding cutting angle and the cutting gap on the maximum cutting stress.