Methodology for model-based uncertainty quantification of the vibrational properties of machining robots

In order to increase the efficiency of modern, robot-based machining processes, a precise model of the robot’s vibrational properties is essential. In particular, a reliable estimation of the robot’s eigenfrequencies is crucial to estimate stable process parameters. However, the prediction of the eigenfrequencies is often imprecise, since the model relies on joint compliance parameters, whose identification process itself is prone to errors. The following paper addresses this issue by quantifying the uncertainty of the eigenfrequency prediction based on a novel, probabilistic compliance identification and a subsequent Monte Carlo uncertainty propagation. The uncertainty quantification is completed by a sensitivity analysis. •The prediction of the robot’s eigenfrequencies is often imprecise, since the model relies on joint compliance parameters, whose identification process itself is prone to errors.•A Bayesian inference approach allows a posterior distribution of the joint compliance parameters to be estimated by updating physical domain knowledge in the form of the prior with the observed system behavior.•The Bayesian identification of the robot joint compliance parameters allows quantifying the resulting uncertainty of the eigenfrequency prediction using the dynamic robot model and a Monte Carlo simulation approach.•A variance-based global sensitivity analysis is able to quantify the effect of the uncertain compliance parameters on the uncertainty of each estimated eigenfrequency of the robot.