Christian Brecher’s research while affiliated with RWTH Aachen University and other places

Dynamic multi-body simulation (MBS) models are important for the development and analysis of gearboxes. With the help of these models, the operational behavior of the gearbox can be examined in detail, without having to produce a prototype. This makes it possible to investigate the influence of various factors, such as misalignments and deformations of components, on the operational behavior of the gearbox. The modelling of the gear mesh is particularly relevant to be able to model the behavior of the gearbox realistically. If the gear mesh should be calculated with an enhanced level of detail, numerical FE-based tooth contact analyses (TCA) are used for each integration step of the dynamic model. These TCA models can be integrated in the Simpack MBS with the help of User Forces. However, the disadvantage of this highly precise method is the longer calculation time that results from solving the TCA for each calculation step. This prevents efficient use in the product development. To improve the calculation time of the MBS model, a neural network is developed in Tensorflow. In the trained range, the network predicts the behavior of the gear contact as accurate as the TCA can calculate it. A User Force is then used to load the Tensorflow network in the MBS and predict the resulting toques and forces of the gear mesh in each integration step. This speeds up the simulation enormously and enables the efficient use of highly accurate gear contact modelling also for complex models in the MBS.

The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. Realistic simulations require precise dynamics and stiffness models of the robot. Regarding the dynamics, the frictional component is highly complex and difficult to model. Therefore, this paper follows a grey-box approach to combine the advantages of the state-of-the-art Lund–Grenoble model (white-box) with those of a data-driven one (black-box) in the first part. The resulting grey-box LuGre model proves to be superior to the white- and black-box models. In the second part, a model-based simulation planning assistance tool is developed, which makes use of the grey-box LuGre model. The simulation assistance provides the manufacturing planner with process knowledge using the identified robot and cutting force models. Furthermore, it provides optimization methods such as a switching point analysis. Finally, the assistance tool gives predictions about the machining result and a process evaluation. The third part of the paper shows the evaluation of the simulation assistance on a real machining process and workpiece, showing an increase in accuracy using the tool.

Die größte Herausforderung des Einsatzes von KI in der Produktion ist die Anpassung bestehender Lösungen an spezifische Unternehmensanforderungen und die Bewertung ihres wirtschaftlichen Nutzens. Das ProKI-Zentrum Aachen entwickelte den „Use-Case-First-Ansatz“ und erprobte diesen innerhalb eines Konsortialprojektes mit elf Unternehmen. Dabei werden in einem dreistufigen Verfahren Use Cases identifiziert, auf technisches und wirtschaftliches Potenzial geprüft und individuelle Roadmaps erstellt.

An efficient gear design requires minimal safety margins in order to realize a full material utilization in terms of load capacity. This leads to a necessity of increasingly accurate calculation methods for the load capacity of gears. In this paper, a method for the calculation of the stressability of surface densified powder metal gears considering local material properties is presented. The approach to consider local porosity characteristics for the calculation of the gear load capacity is implemented by an image-processing tool that analyses metallographic micrographs. The stressability is calculated based on the local porosity characteristics, regarding porosity distribution and local pore morphology. Based on this, the local load capacity can be calculated by comparing the local stress profile resulting from the tooth mesh and the local stressability profile.

This report deals with the macro geometric design of a gear set for investigating the influence of reverse loading on the tooth flank fracture load capacity. Due to power density increase of gearboxes in the past, gears are becoming more and more subject to interior fatigue, since the tribological system as well as the material in the surface zone have been optimized continuously over the years. The influence of reverse loading on the tooth flank fracture load capacity is currently only known to a limited extent in research work. An FE-based approach for calculating the tooth flank fracture load capacity was therefore extended to include the influence of the reverse loading. In an initial analysis, the load capacity reducing influence of the reverse bending load on the tooth flank fracture load capacity has been simulated. Building on this, the present work addresses a more detailed analysis about the influence of the selected stress hypothesis and the influence of the macro geometry. The aim is, to find a gear design that reacts as sensitive as possible to an applied reverse bending load regarding the risk of tooth flank fracture. Finally, the robustness regarding a modification in hardness and residual stress curve is investigated.

Passive vibration isolation is a key factor in achieving precise results in milling processes and extending the service life of tools. An innovative approach involves using damping elements made from NiTi shape memory alloys. This approach is based on the alloys ability to convert large amounts of mechanical energy into thermal energy through the pseudo-elastic effect, with the pronounced transformation hysteresis of the material providing its damping potential. While previous experimental studies have offered valuable insights, analytical models are essential for accurately predicting the complex behavior of these materials. Therefore, the present work aims to develop an analytical model capable of predicting the characteristic properties of shape memory alloys under dynamic compressive loading. This not only offers a deeper understanding of the underlying physical mechanisms but also provides a solid foundation for the optimization and application of these alloys in real-world scenarios.

Zusammenfassung Künstliche Neuronale Netze, Verzögerungsglieder oder Polynomfunktionen werden genutzt, um das in Referenzversuchen identifizierte thermo-elastische Maschinenverhalten in mathematischen Modellen beschreibbar zu machen. Alle Ansätze haben gemein, dass die Modellqualität signifikant mit der verfügbaren Datenmenge korreliert. Insbesondere in der praktischen Anwendung zeigt sich allerdings, dass die Bereitstellung entsprechender Daten zeit- und damit kostenintensiv ist, sodass die Potenziale etwaiger Korrekturansätze aufgrund begrenzter Datensätze nicht vollständig genutzt werden können.

Zusammenfassung Die eigenschaftsmodellbasierte Korrektur ist eine sogenannte Grey-Box-Korrektur. Sie zeichnet sich durch einen mittleren Abstraktionsgrad aus und weist noch einen Bezug zu den für thermo-elastische Fehler ursächlichen physikalischen Phänomenen der Wärmelehre auf. Der Bezug wird durch die Verwendung von Verzögerungsgliedern niedriger Ordnung (PT-Glieder) als Ansatzfunktion zur Abbildung des Maschinenverhaltens hergestellt, vgl. Abb. 1.