Professor
© TUD/NEFM
Professor for Computational and Experimental Solid Mechanics
NameProf. Dr.-Ing. habil. Markus Kästner
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Chair of Computational and Experimental Solid Mechanics
Visitor Address:
Zeunerbau, Room 353 George-Bähr-Straße 3c
01069 Dresden
Research
Data-driven analysis of processes, materials and structures
- Microstructure characterisation and reconstruction
- Description of Process-Structure-Property (PSP) linkages
- Exploration of PSP linkages and optimization
- Virtual Sensing und clustering for load and stress analysis
- Digital twins for predictive maintenance
Development of data- and model-driven modeling techniques
- Physics-constrained neural networks for material modeling
- Data-driven multi-scale modeling with automated data augmentation
- Phase-field modeling of fracture and structural evolution processes
- Modeling of coupled boundary value problems
- Homogenization techniques for coupled problems
- Adaptive Isogeometric Analysis (IGA)
- Extended Finite Element Method (XFEM)
Experimental characterization and modeling of materials
- Damage and failure of additively manufactured materials
- Process-dependent fatigue behavior of materials
- Inelastic, rate-dependent material behavior of polymers
- Damage and failure of fibre reinforced composites
- Magnetosensitive elastomers and fluids
- Parameter identification and experimental validation of material models
Selected projects
- AMTwin - Data-driven analysis of processes, materials and structures for additive manufacturing
- ePredict - Predictive maintenance for electromobility
- LRVTwin - a digital twin for light rail vehicles
- Drucksache - multiscale characterization and modeling of additively manufactured lattice structures
- DFG-Priority Program SPP 2013
Publications
2023
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Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach, May 2023, In: International Journal of Fatigue. 170, 107554Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Conditional diffusion-based microstructure reconstruction, 16 Feb 2023, In: Materials today communications. 35, 105608Electronic (full-text) versionResearch output: Contribution to journal > Research article
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FEANN: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining, 8 Feb 2023, In: Computational mechanics. 25 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
2022
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Numerical and experimental studies on press-bolt joints for sheet metal applications - Static strength assessment, 1 Nov 2022, In: Journal of Advanced Joining Processes. 6, 6, p. 100132, 100132Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Structure-property relationships of imperfect additively manufactured lattices based on triply periodic minimal surfaces, 1 Oct 2022, In: Materials and Design. 222, 18 p., 111036Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Phase-field modeling of fatigue crack growth during tooth flank fracture in case-hardened spur gears, Oct 2022, In: International journal of fatigue. 163, 107091Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Influence of CT image processing on the predicted impact of pores on fatigue of additively manufactured Ti6Al4V and AlSi10Mg, 2 Sep 2022, In: GAMM Mitteilungen. 45, 3-4, 12 p., e202200017Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Microstructure Characterization and Reconstruction in Python, Sep 2022, In: Integrating materials and manufacturing innovation. 11, 3, p. 450-466 17 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Thermo‐mechanical modeling of the temperature dependent forming behavior of thermoplastic prepregs, 17 Aug 2022, In: Engineering Reports. 4, 7-8, 13 p., e12373Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Additively manufactured AlSi10Mg lattices – Potential and limits of modelling as-designed structures, Aug 2022, In: Materials and Design. 220, 15 p., 110796Electronic (full-text) versionResearch output: Contribution to journal > Research article
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