Mechanics for Modelling
The research area Mechanics for Modelling focuses on analytical, numerical and experimental methods for the mechanical modelling of materials, systems and processes. The focus is on mechanical aspects of the industrial digitalisation. The synergy between realistic experiments, non-linear simulations and data-based methods is systematically used to describe, analyse and optimise complex mechanical behaviour.
The research group's expertise currently includes mechanical characterisation and modelling of metals and polymers, non-linear structural analysis using FEM and thermo-mechanical process simulation for additive manufacturing. By 2024, competences in the field of experimental mechanics (dynamics, structural testing, measurement/sensor technology) and data-based methods (design of experiment, response surface modelling, neural networks, etc.) will be specifically developed.
Material modelling for FEM

The detailed analysis and reliable design of thermo-mechanically highly loaded structures requires a physically well-founded description of the underlying material behaviour. Material models are primarily used in analysis and design by means of nonlinear finite element methods (FEM). Examples range from steam turbines, sealing and damping elements to additively manufactured, metallic components. Particularly in the context of the energy transition, consistent lightweight construction and the realisation of a circular economy, the characterisation and description of the mechanical behaviour of new materials is of special importance.
Our expertise includes the mechanical, thermo-mechanical and thermo-physical characterisation and modelling of polymers and metals. The material models used describe linear and non-linear elasticity, viscoelasticity, plasticity and viscoplasticity as well as fatigue (LCF, HCF) and creep damage for lifetime prediction. Temperature and rate dependencies are also taken into account. Thermo-physical parameters include density, thermal conduction as well as thermal expansion.
Process simulation for additive manufacturing
Additive manufacturing of metallic components enables the production of complex and highly integrated lightweight and functional parts. At the same time, it has great potential to disrupt the existing manufacturing landscape with its high degree of flexibility and sustainability. However, the great complexity of this promising manufacturing technology requires a great deal of know-how in order to realise the necessary reliability and economic efficiency.
Thermo-mechanical process simulation can help in the design and production preparation of additively manufactured components to improve the reliability and economic efficiency of this promising process. Our expertise lies in the simulation of process-related thermo-mechanical distortion. For this purpose, both simplified inherent strain and advanced thermo-mechanical approaches are pursued. To ensure reliable predictions, we use a comprehensive basic thermo-physical characterisation of the additively processed materials. We are also specialised in the component-like calibration of all leading process simulation solutions. In this way, we ensure reliable predictions of stresses and distortions. On this basis, build strategies, support structures and additively manufactured components can be optimised and distortion compensated for in advance.
Project Examples
We conduct research in the field of mechanics and have already successfully completed numerous projects. You can find a selection of these projects here
- Machine Learning Enhanced Process Simulation in Laser Powder Bed Fusion (LPBF) / ProjektleiterIn / Projekt laufend
- Development of a Neural Network based Finite Element Method for Thermo-Mechanically coupled Problems / ProjektleiterIn / Projekt laufend
- Evaluation eines Machine Learning Tools in der Mechanik / ProjektleiterIn / Projekt abgeschlossen
- Fertigbarkeitsstudie eines Rotors für neuen Druckwellenlader / Stellv. ProjektleiterIn / Projekt abgeschlossen
- Machbarkeitsstudie zum Prozessmonitoring mittels smarter Gummifederelemente / ProjektleiterIn / Projekt abgeschlossen
- Optimierung additiv gefertigter metallischer Bauteile mit Prozesssimulation / ProjektleiterIn / Projekt abgeschlossen
- Optimierung der Zahnfussgeometrie von Kunststoffverzahnungen / Stellv. ProjektleiterIn / Projekt laufend
- Mechanobiologisches Kunststoff-Modell – simuliert + getestet = validiert! / Teammitglied / Projekt laufend
- Machine Learning Enhanced Process Simulation in Laser Powder Bed Fusion (LPBF) / Stellv. ProjektleiterIn / Projekt laufend
- Neuentwicklung eines Explosionsschutzventils / Co-ProjektleiterIn / Projekt abgeschlossen
- Development of new family of high-endurance sealing system(s) for high-pressure / fast pneumatic valves / ProjektleiterIn / Projekt abgeschlossen
Publications
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Brenner, Lorenz; Jenni, Christian; Guyer, Flurin; Stähli, Patrick; Eberlein, Robert; Huber, Matthias; Zahnd, André; Schneider, Martin Albert; Tillenkamp, Frank,
2021.
Journal of Loss Prevention in the Process Industries.
75, S. 104706.
Verfügbar unter: https://doi.org/10.1016/j.jlp.2021.104706
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Mayer, Thomas; Brändle, Gabriel; Schönenberger, Andreas; Eberlein, Robert,
2020.
Simulation and validation of residual deformations in additive manufacturing of metal parts.
Heliyon.
6(5), S. e03987.
Verfügbar unter: https://doi.org/10.1016/j.heliyon.2020.e03987
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Eberlein, Robert; Pasieka, Lucian,
2020.
Prediction of long-term behavior for dynamically loaded TPU.
Advanced Materials Letters.
11(1), S. 1-6.
Verfügbar unter: https://doi.org/10.5185/amlett.2020.011458
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Eberlein, Robert; Pasieka, Lucian; Rizos, Dimosthenis,
2019.
Validation of advanced constitutive models for accurate FE modeling of TPU.
Advanced Materials Letters.
10(12), S. 893-898.
Verfügbar unter: https://doi.org/10.5185/amlett.2019.0031
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Eberlein, Robert; Fukada, Yuta; Pasieka, Lucian,
2020.
Fatigue life analysis of solid elastomer-like polyurethanes
.
In:
Fatigue Crack Growth in Rubber Materials.
Berlin, Heidelberg:
Springer.
S. 179-202.
Advances in Polymer Science.
Verfügbar unter: https://doi.org/10.1007/12_2020_68