Increasing Robotic Machining Accuracy Using Offline Compensation Based on Joint-Motion Simulation

In this paper an approach for improving robot machining accuracy through simulation-assisted path planning within Computer-Aided Manufacturing (CAM) tools is investigated. The method comprises modeling of dynamic robot behaviour under influence of process forces and a subsequent simulation of the robot motion which results in an offline prediction of deflection errors during the machining task. The error prediction performed inside the CAM-tool is utilised to already offline apply compensations to the planned tool-path for the robot. The whole computer-tool chain has been implemented and integrated within a commercial CAM-software. The focus of this paper is to describe the system architecture and do initial validation and analysis of the proposed method and experimental results

Robot Joint Modeling and Parameter Identification Using the Clamping Method

The usage of industrial robots for milling tasks is limited by their lack of absolute accuracy in presence of process forces. While there are techniques and products available for increasing the absolute accuracy of free-space motions, the mechanical weaknesses of the robot in combination with the milling forces limits the achievable performance. If the dynamic effects causing the deviations can be compensated for, there would be several benefits of using industrial robots for machining applications. To enable the compensation, the causes of the path deviations have to be adequately modeled, and there must be a method for determining the model parameters in a simple and inexpensive way. To that end, we propose a radically new method for identification of robot joint model parameters, based on clamping of the robot to a rigid environment. The rigidity of the environment then eliminates the need for expensive measurement equipment, and the internal sensors of the robot give sufficient feedback. An experimental validation shows the feasibility of the method