Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table

Featured Application The proposed robotic deburring cell with integrated rotary table is designed to improve the performance of robotic deburring when deburring large workpieces. Deburring is recognized as an ideal technology for robotic automation. However, since the low stiffness of the robot can affect the deburring quality and the performance of an industrial robot is generally inhomogeneous over its workspace, a cell setup must be found that allows the robot to track the toolpath with the desired performance. In this work, the problems of robotic deburring are addressed by integrating components commonly used in the machining industry. A rotary table is integrated with the robotic deburring cell to increase the effective reach of the robot and enable it to machine a large workpiece. A genetic algorithm (GA) is used to optimize the placement of the workpiece based on the stiffness of the robot, and a local minimizer is used to maximize the stiffness of the robot along the deburring toolpath. During cutting motions, small table rotations are allowed so that the robot maintains high stiffness, and during non-cutting motions, large table rotations are allowed to reposition the workpiece. The stiffness of the robot is modeled by an artificial neural network (ANN). The results confirm the need to optimize the cell setup, since many optimizers cannot track the toolpath, while for the successful optimizers, a performance imbalance occurs along the toolpath.

Automated summary

The proposed robotic deburring cell with integrated rotary table aims to improve the performance of robotic deburring for large workpieces by addressing issues with robot stiffness and performance imbalance. By integrating components commonly used in machining industry, the setup optimizes workpiece placement and maximizes robot stiffness along the deburring toolpath. The study highlights the importance of optimizing the cell setup for successful robotic deburring.