# Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
## 6 Conclusions  
In this study, a methodology for planning a deburring toolpath of a robot considering its structural stiffness is presented. When deburring a large workpiece, such as a cast iron or aluminum alloy engine block, path optimization is advantageous because the robot's stiffness varies greatly over its workspace, which affects the quality of the process. The same is true when the parts produced by machining operations such as grinding, drilling, milling, engraving or turning are intended for robotic deburring. The proposed methodology helps in planning efficient deburring, especially when the workpiece is large and complex in shape. To improve the deburring performance, an industry-oriented robotic deburring cell with integrated rotary table is presented, which helps the robot to increase the effective reach and maintain high stiffness. To achieve the required deburring accuracy and still exploit the functionality of the rotary table, large rotations of the table are allowed during non-cutting motions and small rotations of the table are allowed during cutting motions. To maximize the overall stiffness of the robot along the deburring toolpath, an optimization of the workpiece placement is first performed, followed by an optimization of the toolpath tracking. A genetic algorithm is used to find the optimal workpiece placement and a local minimizer is used to find the optimal table rotation at each toolpath segment. The structural stiffness of the robot is modeled by a novel approach using an ANN. The advantage of ANN modeling is the possibility to collect data through global stiffness measurements. In this way, the ANN stiffness model considers nonlinear effects such as friction, wear, and gaps between the components, in addition to the full flexibility of all the  
Figure 11: Traverse between (**a**) the left side and (**b**) the right side of the engine block. points and links and allows the structural stiffness to be considered as a function of load. The errors and trends of the ANN structural stiffness model are comparable to the results of other studies on similar robots. The optimization results show that the deburring of large workpieces, such as an engine block, must be carefully planned. By placing the engine block in the middle of the rotary table, the robot was not able to track the toolpath. With the optimized setup, the robot can track the toolpath, but since the effective table rotations are small, the deburring performance could be further improved. The main reason for the small table rotations is that table rotations only occur at the beginning of each toolpath segment and only to an extent that does not affect the required deburring accuracy, which was evaluated in a worst-case scenario. Larger table rotations could be enabled by considering the coordinates of the current toolpath segment. In addition, only every tenth toolpath segment was considered in the optimization and the long linear motions were not broken down into smaller segments. If each toolpath segment was considered and the long linear motions were broken into smaller toolpath segments, the table rotations would occur more frequently. However, increasing the number of toolpath segments would not only increase the computational effort, but also the effort for generating the toolpath, since the decomposition of the toolpath into smaller segments is not readily possible in the CAM software. The optimization also showed that many optimizers did not find a solution, mainly because the robot could not reach both sides of the engine block. Moreover, the optimized solution showed better flexibility and average stiffness of the robot when it worked on one side of the engine block than when it worked on the other side. This imbalance could be reduced by allowing the table to rotate during long linear movements.