Florian Schnoes’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.

The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.

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.

Industrial robots, used for milling processes, have to execute highly dynamic and accurate movements. External static and dynamic process forces lead to static deflections and dynamic excitations. In this paper, we present a coupled offline simulation and planning strategy of the machine-process interaction with online adaptation mechanisms for increased system robustness. The process planning, optimization and milling force prediction are executed offline, while the online compensation and adaptation accounts for static deflections and unmodeled disturbances. The benefits of the combined offline and online approach are demonstrated by stabilizing machining processes and accurate deflection compensation with unmodeled changes in spindle speed and feed rate for the machining of aluminum workpieces.

The static and dynamic mechanical properties of standard industrial robots differ strongly from common CNC-machines. For robot-based machining operations, these properties have to be considered. In this paper, a method for the optimal placement of the workpiece within the workspace, the design of the machining process and the compensation of toolpath deviations during the machining process of metallic workpieces is presented. The method is based on a coupled machine-process-model and the derivation of performance, accuracy and reliability indicators. The method was validated by the machining of aluminum workpieces and the evaluation of the accuracy improvement due to the multi-axis compensation mechanism.

Megatrends wie die Kundenindividualisierung erfordern eine gesteigerte Wandlungsfähigkeit in der Produktion. Mobile Roboter zeigen hier großes Potenzial durch ihre Ortsungebundenheit, Skalierbarkeit und Konfigurationsfähigkeit. Vorgestellt wird ein Ansatz zur dynamischen Adaption durch modulare Softwarebausteine (Apps). Auf dieser Basis wird die Integration dieses Konzepts in die Gesamtarchitektur einer Smart Factory und die zugehörige Produktionsplanung beschrieben. --- https://www.werkstattstechnik.de/wt/currentarticle.php?data[article_id]=90331

One of the main goals of the collaborative research project FORobotics is to gain new insights into human-related aspects and pro-cesses that might be essential to the development and effective implementation of cooperative mobile robots. In order to achieve this goal, a qualitative, task-related work system analysis was conducted as a first step. This analysis served to identify potentially relevant context-related aspects that need to be considered in early stages of the development process. Four work systems in three companies were analyzed that included order-picking, manufacturing and assembly tasks. For the analysis, a method was devised based on different modules of RIHA/VERA (Oesterreich, Leitner, & Resch, 2000) and KOMPASS (Grote, Wäfler, Ryser, & Weik, 1999). As such, it comprised document analyses, workplace observations, semi-structured interviews and questionnaires. Further modules assessing technical aspects were developed and added. In a multi-disciplinary workshop, development recommendations were formulated based on the data analysis and taking into account the KOMPASS criteria (Grote et al., 1999) and additional criteria for designing human-machine interaction (Klein, Woods, Bradshaw, & Feltovich, 2004). The recommendations addressed topics such as robot equipment, robot movement and path planning, function allocation and task planning, and interface design.