Zeit- und kosteneffiziente Prozess- und Produktentwicklung für den Hochleistungs-Faserverbundleichtbau im Rahmen der Nasspresstechnologie
Poppe, C.; Zimmerling, C.; Albrecht, F.; Hüttl, J.; Fial, J.; Engelfried, M.
2017, January 31. Marktplatz Leichtbau (2017), Ludwigsburg, Germany, January 31, 2017

AI and Simulation for Efficient Composite Manufacturing Process Development
Zimmerling, C.
2024, March 4. Society for the Advancement of Material and Process Engineering Europe Summit (SAMPE 2024), Paris, France, March 4, 2024

Machine Learning for Efficient Process Optimization in Textile Draping for Composite Production
Zimmerling, C.
2023, September 26. ITA Reinforced! Innovation Day: “Automation” and “Composite Testing and Sensor Integration” (2023), Aachen, Germany, September 26, 2023

Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming | Lionel Fourment PhD-prize for Industrial Research
Zimmerling, C.
2023, April 20. 26th International ESAFORM Conference on Material Forming (ESAFORM 2023), Krakow, Poland, April 19–21, 2023

Techniken des Maschinenlernens zur effizienten Prozessoptimierung bei veränderlichen Bauteilgeometrien am Beispiel der Textilumformung
Zimmerling, C.
2023, March 22. 27. Nationales SAMPE Symposium Deutschland (2023), Munich, Germany, March 21–22, 2023

Rapid Determination of Suitable Reinforcement Type in Continuous-Fibre-Reinforced Composites For Multiple Load Cases
Zimmerling, C.; Fengler, B.; Wen, H.; Fan, Z.; Kärger, L.
2020, September 1. 23rd / 6th Joint Event: International Conference on Composite Structures - International Conference on Mechanics of Composites (ICCS / MECHCOMP 2020), Porto, Portugal, September 1–4, 2020

Zeit- und kosteneffiziente Prozess und Produktentwicklung für den Hochleistungs-Faserverbundleichtbau unterstützt durch Techniken des Maschinellen Lernens
Zimmerling, C.; Kärger, L.; Carosella, S.; Middendorf, P.; Henning, F.
2019, May 20. 6. Technologietag Hybrider Leichtbau (2019), Leinfelden-Echterdingen, Germany, May 20–21, 2019

Advanced Macroscopic Modelling Approaches for FE Composite Forming Simulation Using Abaqus
Dörr, D.; Poppe, C.; Zimmerling, C.; Krauß, C.; Schäfer, B.; Henning, F.; Kärger, L.
2018. SIMULIA Regional User Meeting (2018), Hanau, Germany, December 4, 2018

Continuous Process Simulation for Continuous Fiber Reinforced Composites
Kärger, L.; Dörr, D.; Poppe, C.; Seuffert, J.; Bernath, A.; Galkin, S.; Zimmerling, C.; Henning, F.
2018. International VDI Conference - Simulation in Automotive Lightweight Engineering (2018), Amsterdam, Netherlands, April 25–26, 2018

Zeit- und kosteneffiziente Prozess- und Produktentwicklung für den Hochleistungs-Faserverbundleichtbau mittels Nasspresstechnologie
Poppe, C.; Fial, J.; Kärger, L.; Carosella, S.; Albrecht, F.; Zimmerling, C.; Draskovic, M.; Engelfried, M.
2017, May 30. 4. Technologietag Hybrider Leichtbau (2017), Stuttgart, Germany, May 30–31, 2017

Forming process optimisation for variable geometries by machine learning – Convergence analysis and assessment
Zimmerling, C.; Kärger, L.
2023. Material Forming 26th International ESAFORM Conference on Material Forming (ESAFORM 2023) Krakau, Polen, 19.04.2023–21.04.2023, 1155–1166, Materials Research Forum LLC. doi:10.21741/9781644902479-126

Deep neural networks as surrogate models for time-efficient manufacturing process optimisation
Zimmerling, C.; Schindler, P.; Seuffert, J.; Kärger, L.
2021. ESAFORM 2021 - 24th International Conference on Material Forming, ULiège Library. doi:10.25518/esaform21.3882

Estimation of Load-Time Curves Using Recurrent Neural Networks Based On Can Bus Signals
Herz, D.; Krauß, C.; Zimmerling, C.; Grupp, B.; Gauterin, F.
2021. 14th World Congress on Computational Mechanics - WCCM & ECCOMAS Congress 2020 : virtual congress, 11-15 January, 2021 / IACM, ECCOMAS. Ed.: F. Chinesta, International Centre for Numerical Methods in Engineering (CIMNE). doi:10.23967/wccm-eccomas.2020.138

An approach for rapid prediction of textile draping results for variable composite component geometries using deep neural networks
Zimmerling, C.; Trippe, D.; Fengler, B.; Kärger, L.
2019. Proceedings of the 22nd International ESAFORM Conference on Material Forming ; Vitoria-Gasteiz, Spain, 8–10 May 2019. Ed.: L. Galdos, Art.-Nr.: 020007, American Institute of Physics (AIP). doi:10.1063/1.5112512

Application and Evaluation of Meta-Model Assisted Optimisation Strategies for Gripper-Assisted Fabric Draping in Composite Manufacturing
Zimmerling, C.; Pfrommer, J.; Liu, J.; Beyerer, J.; Henning, F.; Kärger, L.
2018. 18th European Conference on Composite Materials (ECCM 2018), Athen, GR, June 24-28, 2018

Optimisation of manufacturing process parameters using deep neural networks as surrogate models
Pfrommer, J.; Zimmerling, C.; Liu, J.; Kärger, L.; Henning, F.; Beyerer, J.
2018. 51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018; Stockholm Waterfront Congress CentreStockholm; Sweden; 16 May 2018 through 18 May 2018. Ed.: T. Kjellberg, 426–431, Elsevier. doi:10.1016/j.procir.2018.03.046

A meta-model based approach for rapid formability estimation of continuous fibre reinforced components
Zimmerling, C.; Dörr, D.; Henning, F.; Kärger, L.
2018. Proceedings of the 21st International ESAFORM Conference on Material Forming : ESAFORM 2018 : Palermo, Italy, 23-25 April 2018. Ed.: L. Fratini, Art.Nr. 020042, American Institute of Physics (AIP). doi:10.1063/1.5034843

Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
Würth, T.; Freymuth, N.; Zimmerling, C.; Neumann, G.; Kärger, L.
2024. Computer Methods in Applied Mechanics and Engineering, 429, Artkl.Nr.: 117102. doi:10.1016/j.cma.2024.117102

Zeiteffiziente und datenfreie Bauteil- und Prozesssimulation mithilfe von Physics-Informed Neural Networks
Würth, T.; Prietze, A.; Zimmerling, C.; Krauß, C.; Kärger, L.
2023. NAFEMS-Magazin, 68 (4), 39–45

Physics-informed neural networks for data-free surrogate modelling and engineering optimization – An example from composite manufacturing
Würth, T.; Krauß, C.; Zimmerling, C.; Kärger, L.
2023. Materials & Design, 231, Art.-Nr.: 112034. doi:10.1016/j.matdes.2023.112034

Formability Assessment of Variable Geometries Using Machine Learning - Analysis of the Influence of the Database
Zimmerling, C.; Fengler, B.; Kärger, L.
2022. Key Engineering Materials, 926, 2247–2257. doi:10.4028/p-1o0007

Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning
Zimmerling, C.; Poppe, C.; Stein, O.; Kärger, L.
2022. Materials and Design, 214, Art.-Nr.: 110423. doi:10.1016/j.matdes.2022.110423

Estimating optimum process parameters in textile draping of variable part geometries - A reinforcement learning approach
Zimmerling, C.; Poppe, C.; Kärger, L.
2020. Procedia manufacturing, 47, 847–854. doi:10.1016/j.promfg.2020.04.263

Virtual Product Development Using Simulation Methods and AI
Zimmerling, C.; Poppe, C.; Kärger, L.
2019. Lightweight Design worldwide, 12 (6), 12–19. doi:10.1007/s41777-019-0064-x

Virtuelle Produktentwicklung mittels Simulationsmethoden und KI
Zimmerling, C.; Poppe, C.; Kärger, L.
2019. Lightweight design, 12 (6), 12–19. doi:10.1007/s35725-019-0069-8

A machine learning assisted approach for textile formability assessment and design improvement of composite components
Zimmerling, C.; Dörr, D.; Henning, F.; Kärger, L.
2019. Composites / A, 124, Art. Nr.: 105459. doi:10.1016/j.compositesa.2019.05.027

Techniken des Maschinenlernens unterstützen Prozessauslegung und Gestaltung von Faserverbundbauteilen
Zimmerling, C.
2019. Carbon-Composites-Magazin, (1), 19–20

Development of a modular draping test bench for analysis of infiltrated woven fabrics in wet compression molding
Albrecht, F.; Zimmerling, C.; Poppe, C.; Kärger, L.; Henning, F.
2019. Key engineering materials, 809, 35–40. doi:10.4028/www.scientific.net/KEM.809.35

Forming optimisation embedded in a CAE chain to assess and enhance the structural performance of composite components
Kärger, L.; Galkin, S.; Zimmerling, C.; Dörr, D.; Linden, J.; Oeckerath, A.; Wolf, K.
2018. Composite structures, 192, 143–152. doi:10.1016/j.compstruct.2018.02.041

Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming. PhD dissertation
Zimmerling, C.
2023, January 18. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000154623