# Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
## 1 Introduction  
The increasing heterogeneity of industrial robots and the development of new manufacturing techniques have helped to extend the use of robots to a variety of non-optimized manufacturing processes. Nowadays, robots are more commonly used in machining than ever before. Some of the advantages of machining with robots, such as their price competitiveness, wide range of applications and flexibility, have proven to be beneficial for profitability compared to the general disadvantages, such as lower accuracies due to the low stiffness of the robot and higher cycle times when machining parts with long toolpaths [1]. Deburring is recognized as a technology where robots have a performance advantage over traditional methods such as manual labor or CNC deburring. CNC machines, which are mainly specialized for milling, are oversized and costly, making them inefficient for the deburring process. The short deburring toolpath also requires frequent workpiece changes, which are best handled by a robotic machine service. The ability of a robot to manipulate the workpiece from an input buffer of a deburring cell, perform the deburring operation and transport the finished product to the output buffer suggests that a robotic deburring solution could be very efficient. For robotic deburring to be competitive, standard deburring accuracies must be achieved. Compared to CNC machines, robots have some significant disadvantages. In general, inaccuracies are caused by loading conditions that the robot cannot compensatefor. Characteristic sources of inaccuracies in robotic machining include a robot control loop that is not optimized for the machining tasks, backlash hysteresis [2; 3] and robot structural and posture effects. Some combinations of robot posture and cutting parameters cause chatter, which can lead to poor machining accuracy or even tool breakage [4; 5; 6]. It is interesting to note that the uncertainty and imperfections of a real electromechanical system do not always result in poor system performance, as in some cases it has been found that chaotic control can compensate for mechanical imperfections, which can have a positive effect on the overall performance of the system [7]. To increase the accuracy of robotic machining, many authors have addressed these problems by optimizing the robot trajectory based on a dynamic robot model and the model of the machining process [8; 9; 10], as well as by online force control [11; 12]. Laser trackers [13] and optical measurements [14] have been used as an alternative to force control to efficiently track the desired path with the robot and achieve accurate machining. A particular problem in deburring is also the high variance of burr sizes, which can lead to a sudden increase in cutting force, damaging the workpiece, the deburring tool or even the robot. In deburring, the normal force, which is perpendicular to the workpiece surface, and the tangential force, which is aligned with the feed direction, are important. The most common approach to deal with the stochastic forces is to use adaptive algorithms that control the contact force based on online force measurements [15]. The control of tangential force has been successfully solved by reducing the feed rate of the tool [16] and the control of normal force by position correction [17]. An impedance controller has been found to be most effective in achieving a stable contact force between the deburring tool and the workpiece. However, the implementation of sensors and complex control algorithms in an \"out-of-the-box\" robotic deburring cell is a major obstacle in transitioning the technology from a clean research laboratory environment to a highly stressed industrial environment. As an alternative, an industry-oriented engineering approach to optimize the deburring parameters considering spindle speed, feed rate and contact force was presented in [18].