## 4 Conclusions and Future Prospects  
In this study, we have developed an automatic feeding system for dynamic workpieces of the stamping industry using a robotic arm and 3D cameras. The main contents covered in the study were divided into three sections. The first part was the formation of a 3D point cloud using depth cameras and combining two point clouds from these cameras with different angles setup into a complete point cloud by stereo calibration. The accuracy of the point cloud has been found to be satisfactory to perform computational tasks, with a 4.7 mm mean distance of point-to-point cloud error. In terms of the accuracy of object information in the point cloud, it has also been shown to be steady, with less than a 7% error in positioning and a 5% error in height. Using the generated point cloud as input for the pose estimation system was the second part of this study. We used several point cloud processing algorithms, with emphasis on Euclidean cluster extraction to segment the workpiece with other objects on the worktable, FPFH to evaluate the feature points of the target object as well as the reference object, and SAC-IA and ICP respectively, to align the target and reference point cloud and find the transformation matrix. The error of the estimated system was a high-grade error, with an average of less than 6 mm for the translation error and 3 deg for the rotation error. The repeatability error of the system was also kept stable, with an average of only 7% error. The last part was the transmission system with motion synchronization between the robotic arm and the conveyor to successfully pick up the objects. Before being used to pick up the object, the robotic arm will be calibrated by the nonlinear correction algorithm to find the appropriate adjustment coefficients that contribute to the accuracy of the manipulator. Compared to before compensation, the average translation errors were able to reduce by over 20 times with the \(X\)-axis and 40 times with the \(Y\)-axis, to 0.49 and 0.37 mm on average, respectively. The dynamic feeding experiment was performed with quite satisfactory success (>65%), despite the influence of limited hardware installation. This has proven to be viable in production with more invested equipment. In the future, in addition to improving the device to meet the stability, another algorithm (potentially a Neural Network application algorithm) can be applied to calculate the position of the workpiece faster, to obtain real-time estimation. With the use of the fast refresh rate--Azure Kinect, the advantage of this study is that the scanning time for point cloud formation was within a second. However, using FPFH in combination with SAC-IA and ICP to compare and provide estimated locations of a workpiece requires a minimum of seconds. Therefore, the study can hardly be achieved in real-time. Besides, to increase the success rate of dynamic workpiece grabbing, as mentioned above, it is also necessary to have more stable devices. Since the depth sensors have a certain operating temperature (10-25 \({}^{\circ}\)C), the accuracy of processing point cloud data can be affected if the temperature \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Static**} & \multicolumn{2}{c}{**Dynamic**} \\  & **Case 2** & **Case 1** & **Case 2** & **Case 1** \\ \hline Number of experiments & 20 & 20 & 20 & 20 \\ Successful case & 18 & 19 & 13 & 14 \\ Failure case & 3 & 1 & 7 & 6 \\ Successful rate (\%) & 90 & 95 & 65 & 70 \\ Pose estimation time & & & & \\ (seconds) & 12 & 7 & 12 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of dynamic workpiece feeding compared with static workpiece feeding. is high. As a result, the workpiece modeling has to be carried out before undergoing the austenitization process. An experiment examining the influence of thermal radiation on modeling will be performed in future work.