7 Publications
2025 | Conference Paper | LibreCat-ID: 62079
Gröger, Benjamin, et al. “Modeling Approaches for the Decomposition Behavior of Preconsolidated Rovings throughout Local Deformation Processes.” Sheet Metal 2025, edited by G. Meschut et al., Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 268–275, doi:10.21741/9781644903551-33.
LibreCat
| DOI
2025 | Conference Paper | LibreCat-ID: 62080
Gerritzen, Johannes, et al. “Efficient Failure Information Propagation under Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale Using Machine Learning.” Sheet Metal 2025, edited by G. Meschut et al., Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267, doi:10.21741/9781644903551-32.
LibreCat
| DOI
2025 | Journal Article | LibreCat-ID: 62081
Gerritzen, Johannes, et al. “3D Viscoelastic Plastic Model Coupled with a Continuum Damage Formulation for Fiber Reinforced Polymers.” Materials & Design, vol. 260, 114969, Elsevier BV, 2025, doi:10.1016/j.matdes.2025.114969.
LibreCat
| DOI
2024 | Journal Article | LibreCat-ID: 62073
Gröger, B., et al. “Developing a Numerical Modelling Strategy for Metallic Pin Pressing Processes in Fibre Reinforced Thermoplastics to Investigate Fibre Rearrangement Mechanisms during Joining.” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 238, no. 12, SAGE Publications, 2024, pp. 2286–98, doi:10.1177/14644207241280035.
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 62078
Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” ECCM21 - Proceedings of the 21st European Conference on Composite Materials, vol. 3, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:10.60691/yj56-np80.
LibreCat
| DOI
2024 | Journal Article | LibreCat-ID: 62076
Gerritzen, Johannes, et al. “A Methodology for Direct Parameter Identification for Experimental Results Using Machine Learning — Real World Application to the Highly Non-Linear Deformation Behavior of FRP.” Computational Materials Science, vol. 244, 113274, Elsevier BV, 2024, doi:10.1016/j.commatsci.2024.113274.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 62082
Gröger, Benjamin, et al. Modelling of Composite Manufacturing Processes Incorporating Large Fibre Deformations and Process Parameter Interactions - Example Braiding. 2023.
LibreCat
7 Publications
2025 | Conference Paper | LibreCat-ID: 62079
Gröger, Benjamin, et al. “Modeling Approaches for the Decomposition Behavior of Preconsolidated Rovings throughout Local Deformation Processes.” Sheet Metal 2025, edited by G. Meschut et al., Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 268–275, doi:10.21741/9781644903551-33.
LibreCat
| DOI
2025 | Conference Paper | LibreCat-ID: 62080
Gerritzen, Johannes, et al. “Efficient Failure Information Propagation under Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale Using Machine Learning.” Sheet Metal 2025, edited by G. Meschut et al., Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267, doi:10.21741/9781644903551-32.
LibreCat
| DOI
2025 | Journal Article | LibreCat-ID: 62081
Gerritzen, Johannes, et al. “3D Viscoelastic Plastic Model Coupled with a Continuum Damage Formulation for Fiber Reinforced Polymers.” Materials & Design, vol. 260, 114969, Elsevier BV, 2025, doi:10.1016/j.matdes.2025.114969.
LibreCat
| DOI
2024 | Journal Article | LibreCat-ID: 62073
Gröger, B., et al. “Developing a Numerical Modelling Strategy for Metallic Pin Pressing Processes in Fibre Reinforced Thermoplastics to Investigate Fibre Rearrangement Mechanisms during Joining.” Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, vol. 238, no. 12, SAGE Publications, 2024, pp. 2286–98, doi:10.1177/14644207241280035.
LibreCat
| DOI
2024 | Conference Paper | LibreCat-ID: 62078
Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” ECCM21 - Proceedings of the 21st European Conference on Composite Materials, vol. 3, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:10.60691/yj56-np80.
LibreCat
| DOI
2024 | Journal Article | LibreCat-ID: 62076
Gerritzen, Johannes, et al. “A Methodology for Direct Parameter Identification for Experimental Results Using Machine Learning — Real World Application to the Highly Non-Linear Deformation Behavior of FRP.” Computational Materials Science, vol. 244, 113274, Elsevier BV, 2024, doi:10.1016/j.commatsci.2024.113274.
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 62082
Gröger, Benjamin, et al. Modelling of Composite Manufacturing Processes Incorporating Large Fibre Deformations and Process Parameter Interactions - Example Braiding. 2023.
LibreCat