## 3 Analysis of Results
### First-Order Shovel Blade Excavation Test Study
#### 3.2.2 Analysis of Test Results  
The data in Table 12 were regressed by multivariate quadratic fitting using the Design Expert software, and the quadratic multinomial regression equation of excavation resistance  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Serial Number**} & **Running Speed** & **Shovel Surface** & **Digging Depth** & **Excavation Resistance \(R_{2}\)** \\  & \(A\) **(m/s)** & **Inclination** \(B\) **(\(\uparrow\))** & \(C\) **(mm)** & **(N)** \\ \hline
1 & 1 & –1 & 0 & 341.45 \\
2 & 0 & 0 & 0 & 378.25 \\
3 & 1 & 0 & 1 & 408.2 \\
4 & 0 & \(-1\) & 1 & 334.61 \\
5 & \(-1\) & \(-1\) & 0 & 286.07 \\
6 & 0 & \(-1\) & \(-1\) & 278.51 \\
7 & 0 & 1 & \(-1\) & 439.37 \\
8 & 0 & 0 & 0 & 370.15 \\
9 & \(-1\) & 0 & \(-1\) & 348.49 \\
10 & \(-1\) & 0 & 1 & 374.37 \\
11 & 0 & 1 & 1 & 492.36 \\
12 & 1 & 0 & \(-1\) & 361.4 \\
13 & 0 & 0 & 0 & 385.25 \\
14 & \(-1\) & 1 & 0 & 490.93 \\
15 & 1 & 1 & 0 & 520.77 \\
16 & 0 & 0 & 0 & 379.45 \\
17 & 0 & 0 & 0 & 368.56 \\ \hline \end{tabular}
\end{table}
Table 12: Box–Behnken Mining simulation test design and result parameters.  
From Table 13, it can be seen that the regression fit model of excavation resistance \(R_{1}\) has \(p<0.0001\) (highly significant) and the influencing factor misfit term has \(p=0.0554\) (not significant). In addition, the corrected coefficient of determination is Adj.\(R^{2}=0.9661\) and the model coefficient of determination is \(R^{2}=0.9852\) for this regression fitting model. The above parameters indicate that the regression fitting model of excavation resistance \(R_{1}\) fits very significantly, the correlation between predicted and measured values is good, and the model as a whole is very reliable.  
The multiple regression equation can be summarized as response value = (constant + predicted value) + error; that is, response value = deterministic parameters + random error. The deterministic parameters mainly refer to the predictive functions of the independent  
Figure 20: Three\(-\)dimensional response surface diagram of excavation resistance.  
variables, which refer to all the interpretable and predictable data in the regression model. Random error refers to all the uninterpretable and unpredictable values in the model, which is a collection of the data aggregate of randomness and unpredictability. From Figure 19a, it can be seen that the residuals of the regression fitting model of excavation resistance \(R_{1}\) obey a normal distribution, which is consistent with the principle of randomness. From Figure 19b, it can be seen that none of the residuals of the regression fitting model of excavation resistance \(R_{1}\) have any regular distribution, which is consistent with the principle of unpredictability. In summary, the regression fitting model of the excavation resistance \(R_{1}\) can be used for the prediction and analysis of the excavation resistance of the shovel body.  
As seen from Figure 20, the most significant effect is of shovel face inclination \(B\) on digging resistance \(R_{1}\). With the growth in shovel face inclination, digging resistance significantly increases. In contrast, the effect of digging depth \(C\) on digging resistance is relatively weak. As the operating speed \(A\) grows, so does the value of the digging resistance \(R_{1}\), which grows at a rate that lies between the shovel surface inclination \(B\) and the digging depth \(C\). Among the three factors, the magnitude of the ability to influence the digging resistance is shovel surface inclination \(B\) > operating speed \(A\) > digging depth \(C\). This result remains consistent with the analysis above.