Modelling Deformation Mechanisms Decomposition, Separation and Compaction in Mechanical Joining Processes of Fiber Reinforced Thermoplastics on Meso Scale
B. Gröger, J. Gerritzen, A. Hornig, M. Gude, Key Engineering Materials 1050 (2026) 227–234.

Modeling approaches for the decomposition behavior of preconsolidated rovings throughout local deformation processes
B. Gröger, J. Gerritzen, A. Hornig, M. Gude, in: G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, F. Micari (Eds.), Sheet Metal 2025, Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 268–275.

Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning
J. Gerritzen, A. Hornig, M. Gude, in: G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins, M. Merklein, F. Micari (Eds.), Sheet Metal 2025, Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267.

3D viscoelastic plastic model coupled with a continuum damage formulation for fiber reinforced polymers
J. Gerritzen, B. Gröger, M. Zscheyge, A. Hornig, M. Gude, Materials &;amp; Design 260 (2025).

Quality assurance of clinched joints using explainable machine learning
J. Gerritzen, K. Chopra, G. Reschke, A. Hornig, A. Brosius, M. Gude, Journal of Advanced Joining Processes 13 (2025).

Local Deformation and Failure of Composites during Self-Piercing Riveting: A CT-Based Microstructure Investigation
A. Dargel, B. Gröger, M.C. Schlichter, J. Gerritzen, D. Köhler, G. Meschut, M. Gude, R. Kupfer, in: J.F.S. Gomes, S.A. Meguid (Eds.), Proceedings of the 8th International Conference on Integrity-Reliability-Failure (I), FEUP, Porto, 2025.

Developing a numerical modelling strategy for metallic pin pressing processes in fibre reinforced thermoplastics to investigate fibre rearrangement mechanisms during joining
B. Gröger, J. Gerritzen, A. Hornig, M. Gude, Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 238 (2024) 2286–2298.

Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of the 21st European Conference on Composite Materials, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259.

A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, M. Gude, Computational Materials Science 244 (2024).

Modelling of Composite Manufacturing Processes Incorporating Large Fibre Deformations and Process Parameter Interactions - Example Braiding
B. Gröger, J. Gerritzen, S. Eckardt, A. Gelencsér, E. Kunze, A. Hornig, R. Protz, M. Gude, (2023).

A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters
J. Gerritzen, A. Hornig, B. Gröger, M. Gude, Journal of Composites Science 6 (2022).