## 3 Results
### Box-Behnken Test  
The Box-Behnken test design and results are shown in Table 6.  
To obtain the optimal combination of discrete-element parameters, a regression analysis was performed on the experimental results. The regression equation was obtained with the critical load Y as the response value and the unit area normal stiffness X1, critical normal stress X3, and critical shear stress X4 as independent variables. \[\text{Y}=132.87+1.38\text{X}1-0.99\text{X}3+9.76\text{X}4-1.28\text{X}1\text{ X}3+5.70\text{X}1\text{X}4-1.73\text{X}3\text{X}4+1.27\text{X}1^{2}+6.49\text{X}3^{2 }+2.40\text{X}4^{2} \tag{9}\]  
An ANOVA was performed on the regression models and the results are shown in Table 7. A \(p<0.01\) for the model indicates that the regression equation is very significant, indicating the relationship between the factors and the response values. The lack-of-fit F-value is more than 0.05, which implies the lack-of-fit term is not significant; the regression model is reliable and has a high degree of fit. According to the value of \(p\), the influence of the first term X4 of the model is very significant, and the quadratic terms X1, X4 and X3\({}^{2}\) are significant. In addition, the coefficient of determination R\({}^{2}\) = 0.9598 indicates a good correlation between the regression equation and the experimental data. The adjusted \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **X1** & **X3** & **X4** & **Critical Load/N** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 6: Design and results of the Box–Behnken test. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **X1** & **X3** & **X4** & **Critical Load/N** & **Relative Error/\%** \\ \hline
1 & 7 & 5 & 5 & 110.44 & 7.05\% \\
2 & 7.4 & 5.6 & 5.6 & 158.428 & 33.33\% \\
3 & 7.8 & 6.2 & 6.2 & 167.921 & 41.32\% \\
4 & 8.2 & 6.8 & 6.8 & 205.096 & 72.61\% \\
5 & 8.6 & 7.4 & 7.4 & 216.869 & 82.52\% \\
6 & 9 & 8 & 8 & 246.144 & 107.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental design and results of the steepest-ascent slope. coefficient of determination, adjR\({}^{2}\) = 0.9080, shows a high correlation between the predicted values and the actual values. The coefficient of variation (CV) is 1.9%, which indicates that the model is reasonable. In conclusion, this model can be used to analyze the factors affecting the critical load of wheat crushing. According to the regression equation, the response surface of the interaction effects of each factor to the critical load of the wheat crushing period is shown in Figures 4-6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & \begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & \
\begin{tabular}{c} **F** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **_p_** \\ **Value** \\ \end{tabular} \\ \hline model & 1142.34 & 9.00 & 126.93 & 18.55 & 0.0004 \\ X1 & 15.23 & 1.00 & 15.23 & 2.23 & 0.1793 \\ X3 & 7.77 & 1.00 & 7.77 & 1.14 & 0.3218 \\ X4 & 748.46 & 1.00 & 748.46 & 109.41 & \textless{}0.0001 \\ X1X3 & 6.54 & 1.00 & 6.54 & 0.96 & 0.3608 \\ X1X4 & 130.05 & 1.00 & 130.05 & 19.01 & 0.0033 \\ X3X4 & 11.99 & 1.00 & 11.99 & 1.75 & 0.2271 \\ X12 & 6.78 & 1.00 & 6.78 & 0.99 & 0.3525 \\ X32 & 177.29 & 1.00 & 177.29 & 25.92 & 0.0014 \\ X42 & 24.34 & 1.00 & 24.34 & 3.56 & 0.1012 \\ Residual & 47.88 & 7.00 & 6.84 & \textless{}--- & 0.9921 \\ Lack of Fit & 1.04 & 3.00 & 0.35 & 0.03 & 0.0004 \\ Pure Error & 46.85 & 4.00 & 11.71 & \textless{}--- & 0.1793 \\ Cor Total & 1190.22 & 16.00 & \textless{}--- & \textless{}--- & 0.3218 \\ CV = 1.9\%, R\({}^{2}\) = 0.9598, AdjR\({}^{2}\) = 0.9098, PredR\({}^{2}\) = 0.9246, Adep Precision = 15.755 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANOVA for regression models. Figure 4: Interaction effects of X3 and X4. X3 is the critical normal stress; X4 is the critical shear stress.