## 4 Results
### Experimental Results  
The following section examines the use of RL models for bending angle control in an experimental environment. As shown in Section 3, simulatively trained agents are initially used to predict the springback. Subsequently, the models are retrained based on experimental data and validated on an additional experimental dataset. For the experimental validation of the agent trained with simulatively generated data, the angular range is traversed with a stepsize of 2.5\({}^{\circ}\) (see Table 5). Figure 14 shows the springback \(\Delta\alpha\) plotted against the nominal angle \(\alpha_{\text{des}}^{\text{(un)}}\). The data points represent the experimentally measured values and the dashed lines the springback predicted by the agent. The used agent was trained on the nu data set, as information on material fluctuations cannot be fed back in the test setup shown. The agent can therefore only learn the mean springback of datasets subject to uncertain material parameters, which corresponds to training on the dataset without applied uncertainty. Figure 14 shows the springback behavior broken down by material. For copper sheets, it can be seen for both thicknesses that the material springs back significantly more in the experimental case than predicted by the agent. Since it could be shown that the agent reduces the deviation in the simulated process to an average of 0.16\({}^{\circ}\)(max. 0.88\({}^{\circ}\)), this means that the material springs back significantly more in the real process. This may be due to the fact that the material parameters of copper used for the simulation were not determined experimentally with the material used, but represent literature values. As shown by [28], the prediction quality in V-die bending is also highly dependent on the rolling direction of the sheet and the underlying criteria and laws for different materials. For 1 mm thick aluminum sheets, the graph shows a good match between predicted and actual springback. Small deviations in the springback behavior can be seen, but these should be largely due to fluctuations in the material or process. Even for 0.5 mm thick aluminum sheets, there is good agreement between prediction and experiment for desired angles \(\alpha_{\text{des}}^{\text{(un)}}\) greater than 95\({}^{\circ}\). Below this, the springback increases in the predicted and thus simulative case compared to the experimental investigation as the desired angle decreases. As already discussed in Section 4.1, this could also be due to the coining component during die bending, which behaves slightly differently in the real process than in the simulation. For sheets of the two thinner thicknesses of DC01, higher springbacks occur in the simulation than in the real test. For 1 mm thick sheets, on the other hand, the predicted and the experimentally determined springback agree well. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **nu** & **lu** & **mu** & **hu** \\ \hline \(\max\)(\(|\Delta\alpha_{\text{ctrl}}|\)) in \({}^{\circ}\) & 0.88 & 2.28 & 2.58 & 4.52 \\ \(\max\)(\(|\Delta\alpha_{\text{ctrl}}|\)) in \({}^{\circ}\) & - & 1.59 & 1.96 & 1.72 \\ \(|\Delta\alpha_{\text{ctrl}}|\) in \({}^{\circ}\) & 0.16 & 0.21 & 0.33 & 0.51 \\ \(|\Delta\alpha_{\text{ctrl}}|\) in \({}^{\circ}\) & - & 0.17 & 0.19 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum and average occurring bending angle deviations using the SAC algorithm as an AI-supported product controller on the different simulation data sets without (black) and with knowledge (gray) about uncertainty of the material parameters (mean values based on 30 trained models each). In summary, it can be said that the deviation \(\Delta\alpha_{\text{ctrl}}\) is already improved by the simulative training of the agents. However, as simulation and reality do not align exactly, there is a sim-to-real gap that limits the prediction quality of the agent. In order to obtain more accurate results, the gap must be closed through transfer learning.