Maximilian Busch’s research while affiliated with Technical University of Munich and other places

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.

Robotic machining is a promising technology for post-processing large additively manufactured parts. However, the applicability and efficiency of robot-based machining processes are restricted by dynamic instabilities (e.g., due to external excitation or regenerative chatter). To prevent such instabilities, the pose-dependent structural dynamics of the robot must be accurately modeled. To do so, a novel data-driven information fusion approach is proposed: the spatial behavior of the robot’s modal parameters is modeled in a horizontal plane using probabilistic machine learning techniques. A probabilistic formulation allows an estimation of the model uncertainties as well, which increases the model reliability and robustness. To increase the predictive performance, an information fusion scheme is leveraged: information from a rigid body model of the fundamental behavior of the robot’s structural dynamics is fused with a limited number of estimated modal properties from experimental modal analysis. The results indicate that such an approach enables a user-friendly and efficient modeling method and provides reliable predictions of the directional robot dynamics within a large modeling domain.

Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property. Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.

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.

Industrieroboter werden aufgrund ihres großen Arbeitsraumes zunehmend für die Fräsbearbeitungen großer Werkstücke eingesetzt. Dynamische Instabilitäten während des Prozesses schränken jedoch ihre Produktivität ein. Maschinelle Lernverfahren gewinnen hierbei an Popularität, um Strukturmodelle aus experimentellen Daten abzuleiten. Das Institut für Werkzeugmaschinen und Betriebswissenschaften (iwb) der Technischen Universität München entwickelt in Zuge dessen Methoden, die mit maschinellen Lernverfahren Simulations- und Experimentaldaten verbinden, um dadurch die Strukturdynamik von Fräsrobotern zu modellieren. Industrial robots are increasingly used for milling applications of large workpieces due to their large working area. However, dynamic instabilities during the process limit their productivity. Thus, machine learning methods are becoming increasingly popular for deriving system models from experimental data. The Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich is developing methods to fuse simulation data and experimental data using machine learning methods to model the structural dynamics of milling robots.