# Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
## 1 Introduction
In another study, improved control of the robotic deburring process was achieved by shifting the force control from the robot to a simpler external mechanism that is easier to control and designed to compensate for high nominal forces by varying its stiffness [19]. To completely remove larger burrs, several deburring passes were added. The adjustable stiffness of such a mechanism is easy to determine, but the approach still requires some design effort and reduces the flexibility of an autonomous robotic solution. To some extent, force control can also be shifted directly to the cutting tool [20]. To highlight the advantages of robotic deburring, an optimization of the deburring toolpath with respect to the robot stiffness is required. This is essentially a well-known optimization problem for workpiece or trajectory placement [21; 22; 23], where robot stiffness is used as the optimization objective [24; 25; 26; 27; 28]. To optimize the stiffness of the robot along a trajectory, the stiffness of the robot throughout the workspace must be known. The following two main methods have evolved to determine the stiffness of the robot: global and local [29]. The advantage of the global approach is the ability to capture nonlinear effects due to friction and transmission losses, while the disadvantage is that the model does not generalize well to the entire workspace of the robot. As a result, the established analytical models are sensitive to measurement noise and generate significant errors [30; 31]. On the other hand, the more accurate local approach requires the identification of the stiffness matrices of all the robot components, which means that the robot must be disassembled [32]. The scope of analytical modeling of robot stiffness is presented in [33], where a comprehensive six DOF robot stiffness model with 258 identifiable parameters is presented. Recently, artificial neural networks (ANN) have been discovered in robotics as an alternative to analytical modeling. Their main advantage is that they can approximate nonlinear behavior (which is common in robotics) with the desired accuracy [34; 35]. Similar to analytical modeling, ANN modeling requires a large amount of measured data, but in the case of describing the structural properties of a robot, such as stiffness, an ANNmodel is much easier to implement and can even improve as new data is collected [36; 37]. Since stiffness correlates with accuracy, the kinematic properties of a robot can be used as a general guide for robotic machining [38]. Serial robots, which have the greatest stiffness at the edges of the workspace, are more suitable for machining smaller workpieces, while quasi-serial robots, which have the greatest stiffness in the center of the workspace, are recommended for machining larger workpieces. A peculiarity of the deburring process is that most of the workpieces in the deburring phase were preproduced on a different setup. Due to the new setup, any uncertainty in the placement of the workpiece can lead to a collision during operation. Therefore, a reference system must be used to detect the position and orientation of the workpiece and adjust the deburring toolpath to the new workpiece offset. As a solution, an iterative nearest point based contour matching algorithm was applied in [39] to match the toolpath extracted from the CAD model to the teach points. Another advantage of autonomous robotic deburring is that the robot can autonomously place the workpiece in the deburring fixture and then perform the required calibration. It has been shown that a robotic machining setup with a static worktable can increase the machining quality, and a robotic machining cell with a rotary table has also been developed [40], but using a rotary table to support the robot to increase the machining performance during operation is new. The objective of this paper is to study the performance of robotic deburring with a rotary table. The stiffness of the robot is used as a performance measure and modeled using ANN based on measured data.