Probabilistic information fusion to model the pose-dependent dynamics of milling robots
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MACHINE TOOL
Probabilistic information fusion to model the pose‑dependent dynamics of milling robots Maximilian Busch1 · Florian Schnoes1 · Thomas Semm1 · Michael F. Zaeh1 · Birgit Obst2 · Dirk Hartmann2 Received: 22 May 2020 / Accepted: 27 July 2020 / Published online: 28 August 2020 © The Author(s) 2020
Abstract Conventional industrial robots are increasingly used for milling applications of large workpieces due to their workspace and their low investment costs in comparison to conventional machine tools. However, static deflections and dynamic instabilities during the milling process limit the efficiency and productivity of such robot-based milling systems. Since the pose-dependent dynamic properties of the industrial robot structures are notoriously difficult to model analytically, machine learning methods are recently gaining more and more popularity to derive system models from experimental data. In this publication, a modeling concept based on a modern information fusion scheme, fusing simulation and experimental data, is proposed. This approach provides a precise model of the robot’s pose-dependent structural dynamics and is validated for a one-dimensional variation of the robot pose. The results of two information fusion algorithms are compared with a conventional, data-driven approach and indicate a superior model accuracy regarding interpolation and extrapolation of the pose-dependent dynamics. The proposed approach enables decreasing the necessary amount of experimental data needed to assess the vibrational properties of the robot for a desired pose. Additionally, the concept is able to predict the robot dynamics at poses where experimental data is very costly to gather. Keywords Robotic milling · Structural dynamics · Machine learning · Multi-fidelity information fusion
1 Introduction In order to increase the economic efficiency of milling processes in terms of investment, operating and maintenance costs, conventional industrial robots are an attractive alternative for machining of large workpieces [9, 26]. However, the low static and dynamic stiffness of industrial robots often lead to static displacements of the tool or to dynamic instabilities, also called chatter [1, 10, 21, 25]. The static displacements of the tool during the process result in deviations from the target workpiece geometry, whereas dynamic instabilities result in an insufficient surface quality or might even lead to increased tool wear as well as failure of spindle components.
* Maximilian Busch [email protected] 1
Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstr. 15, 85748 Garching b. Muenchen, Germany
Siemens AG, Otto‑Hahn‑Ring 6, 81739 Muenchen, Germany
2
Thus, current research projects address the precise modeling and the system identification of the static and dynamic structural behavior of milling robots. This allows to compensate the estimated errors by choosing a compensated tool path or by choosing stable process parameters [22]. The dynamics
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