Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis

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In robotic machining applications, the precision of the robot is of great importance. In heavy machining process, the lower stiffness of industrial robots results in greater position errors than that of the CNC machine executing the same process. In this contribution, a new stiffness model with 36 degrees of freedom and nonlinear descriptions are presented together with a new identi cation method. Experimental results outline the potential of the model in machining application. Acknowledgment: The authors would like to acknowledge Mr. Justus Kopp and Mr. Julian Ricardo Diaz Posada at Fraunhofer IPA for taking part in the laboratory experiments and the implementation