## 5 Verification Experiments
### Verification of the Configuration Optimization Method
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \(\mathbf{\theta_{1}}\) (\({}^{\circ}\)) & \(\mathbf{\theta_{2}}\) (\({}^{\circ}\)) & \(\mathbf{\theta_{3}}\) (\({}^{\circ}\)) & \(\mathbf{\theta_{4}}\) (\({}^{\circ}\)) & \(\mathbf{\theta_{5}}\) (\({}^{\circ}\)) & \(\mathbf{\theta_{6}}\) (\({}^{\circ}\)) \\ \hline Config. 1 & 56.85 & \(-54.88\) & 95.82 & \(-248.27\) & 42.74 & \(-147.21\) \\ Config. 2 & 61.13 & \(-58.51\) & 101.86 & \(-241.47\) & 40.89 & \(-152.49\) \\ Config. 3 & 47.09 & \(-52.77\) & 92.24 & 102.02 & 49.46 & \(-142.27\) \\ Config. 4 & 37.73 & \(-29.41\) & 49.91 & 86.66 & 36.88 & 243.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configurations of the clamping robot. Figure 17: The experimental scenes: (**a**) non-clamping, (**b**) Config. 1, (**c**) Config. 2, (**d**) Config. 3, (**e**) Config. 4. Figure 18: Experimental and simulated results under different configurations. the optimized Config. 4 is the smallest of the four sets of configurations. The experimental result is 46.78 m/s\({}^{2}\), and the simulation result is 44.77 m/s\({}^{2}\). Furthermore, the simulated and experimental responses were reduced by 15.37 m/s\({}^{2}\) and 15.91 m/s\({}^{2}\) after configuration optimization, demonstrating the effectiveness of configuration optimization in improving the vibration suppression effect. The milling experiment effects of different configurations are illustrated in Figure 18; in addition, the surface roughness of the corresponding milling path is listed in Table 3. Additionally, C1 to C4 are abbreviations of Config. 1 to Config. 4. It can be intuitively observed from Figure 19 that the non-clamping milling surface has obvious tool marks, and its surface roughness reaches 1.684, which is significantly higher than that of the other four groups of clamping experiments. The result proves that the mobile clamping robot system is an effective solution for the vibration suppression of the cantilever workpiece. From Config. 1 to Config. 4, the experimental vibration response of the workpiece is 62.9 m/s\({}^{2}\), 59.36 m/s\({}^{2}\), 51.63 m/s\({}^{2}\), 46.78 m/s\({}^{2}\) successively, decreasing by 3.54 m/s\({}^{2}\), 7.73 m/s\({}^{2}\), 4.85 m/s\({}^{2}\), respectively. The corresponding surface roughness values were 0.921, 0.801, 0.611 and 0.576, which decreased by 0.12, 0.19 and 0.035, respectively. It can be observed that with the decrease in workpiece vibration response, surface machining quality is gradually improved because the surface roughness is not only affected by vibration, but also related to the geometry of the tool, the workpiece material, the product chipping and so on. Therefore, the amplitude of vibration response reduction cannot be directly mapped linearly to the roughness value. In addition, compared with the milling track in Config.4, burrs appeared on the edges of other tracks, which also confirmed that the occurrence of burr defects in the workpiece was affected by the response amplitude. The changes in surface roughness and burrs prove that the robot configuration optimization based on workpiece response in this paper can effectively improve the machining quality of workpiece.