@inproceedings{62079,
  abstract     = {{This paper investigates two modeling approaches for the simulation of the deformation and decomposition behavior of preconsolidated rovings above the thermoplastic matrix{\textquoteright} melting temperature. This is crucial for capturing the local material structure after processes introducing highly localized deformation such as mechanical joining processes between metal and fiber reinforced thermoplastics (FRTP). A generic finite element (FE) model is developed, incorporating interfaces discretized through either cohesive zone (CZ) elements or Coulomb friction-based contacts. The material parameters for the FE elements are derived from the initial stiffness of a statistical volume element (SVE) at micro scale modelled with an Arbitrary-Lagrange-Eulerian method for three load cases. The CZ properties calculated are based on the shear viscosity of the composite. The CZ and contact modelling approaches are evaluated using three load cases of the SVE, comparing force-displacement curves. Under simple loading conditions, such as normal pressure tension and bending, both methods produce similar results; however, in complex load cases, the CZ approach shows clear advantages in handling interface interactions and shows robust simulations. The CZ approach thus presents a promising method for simulating roving decomposition in FRTP-metal joining applications above the matrix{\textquoteright} melting temperature.}},
  author       = {{Gröger, Benjamin and Gerritzen, Johannes and Hornig, Andreas and Gude, Maik}},
  booktitle    = {{Sheet Metal 2025}},
  editor       = {{Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}},
  isbn         = {{978-1-64490-354-4}},
  keywords     = {{Finite Element Method (FEM), Process, Thermoplastic Fiber Reinforced Plastic}},
  pages        = {{268–275}},
  publisher    = {{Materials Research Forum LLC, Materials Research Foundations}},
  title        = {{{Modeling approaches for the decomposition behavior of preconsolidated rovings throughout local deformation processes}}},
  doi          = {{10.21741/9781644903551-33}},
  year         = {{2025}},
}

@inproceedings{62080,
  abstract     = {{The failure behavior of fiber reinforced polymers (FRP) is strongly influenced by their microstructure, i.e. fiber arrangement or local fiber volume content. However, this information cannot be directly used for structural analyses, since it requires a discretization on micrometer level. Therefore, current failure theories do not directly account for such effects, but describe the behavior averaged over an entire specimen. This foundation in experimentally accessible loading conditions leads to purely theory based extension to more complex stress states without direct validation possibilities. This work aims at leveraging micro-scale simulations to obtain failure information under arbitrary loading conditions. The results are propagated to the meso-scale, enabling efficient structural analyses, by means of machine learning (ML). It is shown that the ML model is capable of correctly assessing previously unseen stress states and therefore poses an efficient tool of exploiting information from the micro-scale in larger simulations.}},
  author       = {{Gerritzen, Johannes and Hornig, Andreas and Gude, Maik}},
  booktitle    = {{Sheet Metal 2025}},
  editor       = {{Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}},
  isbn         = {{978-1-64490-354-4}},
  keywords     = {{Failure, Fiber Reinforced Plastic, Machine Learning}},
  pages        = {{260–267}},
  publisher    = {{Materials Research Forum LLC, Materials Research Foundations}},
  title        = {{{Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning}}},
  doi          = {{10.21741/9781644903551-32}},
  year         = {{2025}},
}

@article{62081,
  author       = {{Gerritzen, Johannes and Gröger, Benjamin and Zscheyge, Matthias and Hornig, Andreas and Gude, Maik}},
  issn         = {{0264-1275}},
  journal      = {{Materials & Design}},
  publisher    = {{Elsevier BV}},
  title        = {{{3D viscoelastic plastic model coupled with a continuum damage formulation for fiber reinforced polymers}}},
  doi          = {{10.1016/j.matdes.2025.114969}},
  volume       = {{260}},
  year         = {{2025}},
}

@inproceedings{61149,
  abstract     = {{The use of continuous fiber-reinforced thermoplastics (FRTP) in automotive industry increases due to their excellent material properties and possibility of rapid processing. The scale spanning heterogeneity of their material structure and its influence on the material behavior, however, presents significant challenges for most joining technologies, such as self-piercing riveting (SPR). During mechanical joining, the material structure is significantly altered within and around the joining zone, heavily influencing the material behavior. A comprehensive understanding of the underlying phenomena of material alteration during the SPR process is essential as basis for validating numerical simulations. This study examines the material structure at ten stages of a step-setting test of SPR with two FRTP sheets with glass-fiber reinforcement. Utilizing X-ray computed tomography (CT), the damage phenomena within different areas of the setting test are analyzed three-dimensionally and key parameters are quantified. Dominating phenomena during the penetration of the rivet into the laminate are fiber failure (FF), interfiber failure (IFF) and fiber bending, while delamination, fiber kinking and roving splitting are also observed. At the final stages, the bottom layers of the second sheet collapse and form a bulge into the cavity of the die.}},
  author       = {{Dargel, Alrik and Gröger, Benjamin and Schlichter, Malte Christian and Gerritzen, Johannes and Köhler, Daniel and Meschut, Gerson and Gude, Maik and Kupfer, Robert}},
  booktitle    = {{Proceedings of the 8th International Conference on Integrity-Reliability-Failure (IRF2025)}},
  editor       = {{Gomes, J.F. Silva and Meguid, Shaker A.}},
  isbn         = {{9789727523238}},
  keywords     = {{self-piercing riveting, computed tomography, thermoplastic composites, process-structure-interaction}},
  location     = {{Porto}},
  publisher    = {{FEUP}},
  title        = {{{LOCAL DEFORMATION AND FAILURE OF COMPOSITES DURING SELF-PIERCING RIVETING: A CT BASED MICROSTRUCTURE INVESTIGATION}}},
  doi          = {{10.24840/978-972-752-323-8}},
  year         = {{2025}},
}

@article{63828,
  author       = {{Gerritzen, Johannes and Chopra, Kunal and Reschke, Gregor and Hornig, Andreas and Brosius, Alexander and Gude, Maik}},
  issn         = {{2666-3309}},
  journal      = {{Journal of Advanced Joining Processes}},
  publisher    = {{Elsevier BV}},
  title        = {{{Quality assurance of clinched joints using explainable machine learning}}},
  doi          = {{10.1016/j.jajp.2025.100368}},
  volume       = {{13}},
  year         = {{2025}},
}

@article{62073,
  abstract     = {{<jats:p> A numerical modelling strategy for the direct pin pressing process of metallic pins into continuous fibre-reinforced thermoplastic organosheets is developed. The joining process is performed above the thermoplast’s melting temperature, altering the initial material structure of the composite by fibre rearrangement, which in turn influences the load-bearing capacity of the joint. Therefore, the modelling strategy aims at predicting the resultant material structure after pin pressing. The modelling approach considers both the textile architecture and the process parameters (temperature, tool velocity). A sub-meso modelling framework for the fibres based on a multi-filament approach is used. The interaction between fibres and the thermoplastic melt, as well as the matrix flow, is modelled using the Arbitrary Lagrangian Eulerian method. This allows for the prediction of matrix-rich zones and fibre rearrangement around the pin. The promising results show a good agreement of the resultant material structure in terms of compaction and fibre volume content around the pressed pin. Characteristic parameters show an underestimation of the laminate thickness below the pin. Moreover, an evaluation method for evaluating the orientation changes of the virtual multi-filaments is developed and presented to observe and assess fibre rearrangement and fibre volume content in detail during the numerical process simulation. It can be seen that only fibres around the pin are displaced and not in the whole molten area. Furthermore, it can be observed in detail that the initial position of the fibres in relation to the pin determines whether the fibres are displaced in the in-plane or out-of-plane direction. </jats:p>}},
  author       = {{Gröger, B. and Gerritzen, Johannes and Hornig, A. and Gude, M.}},
  issn         = {{1464-4207}},
  journal      = {{Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications}},
  number       = {{12}},
  pages        = {{2286--2298}},
  publisher    = {{SAGE Publications}},
  title        = {{{Developing a numerical modelling strategy for metallic pin pressing processes in fibre reinforced thermoplastics to investigate fibre rearrangement mechanisms during joining}}},
  doi          = {{10.1177/14644207241280035}},
  volume       = {{238}},
  year         = {{2024}},
}

@inproceedings{62078,
  abstract     = {{Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. }},
  author       = {{Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}},
  booktitle    = {{ECCM21 - Proceedings of the 21st European Conference on Composite Materials}},
  isbn         = {{978-2-912985-01-9}},
  keywords     = {{Direct parameter identification, Machine learning, Convolutional neural networks, Strain rate dependency, Fiber reinforced plastics, woven composites, segmentation, synthetic training data, x-ray computed tomography}},
  pages        = {{1252–1259}},
  publisher    = {{European Society for Composite Materials (ESCM)}},
  title        = {{{Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}}},
  doi          = {{10.60691/yj56-np80}},
  volume       = {{3}},
  year         = {{2024}},
}

@article{62076,
  author       = {{Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}},
  issn         = {{0927-0256}},
  journal      = {{Computational Materials Science}},
  publisher    = {{Elsevier BV}},
  title        = {{{A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP}}},
  doi          = {{10.1016/j.commatsci.2024.113274}},
  volume       = {{244}},
  year         = {{2024}},
}

@inproceedings{62082,
  author       = {{Gröger, Benjamin and Gerritzen, Johannes and Eckardt, Simon and Gelencsér, Anton and Kunze, Eckart and Hornig, Andreas and Protz, Richard and Gude, Maik}},
  location     = {{Belfast}},
  title        = {{{Modelling of Composite Manufacturing Processes Incorporating Large Fibre Deformations and Process Parameter Interactions - Example Braiding}}},
  year         = {{2023}},
}

@article{63829,
  abstract     = {{<jats:p>The 3D shear deformation and failure behaviour of a glass fibre reinforced polypropylene in a shear strain rate range of γ˙=2.2×10−4 to 3.4 1s is investigated. An Iosipescu testing setup on a servo-hydraulic high speed testing unit is used to experimentally characterise the in-plane and out-of-plane behaviour utilising three specimen configurations (12-, 13- and 31-direction). The experimental procedure as well as the testing results are presented and discussed. The measured shear stress–shear strain relations indicate a highly nonlinear behaviour and a distinct rate dependency. Two methods are investigated to derive according material characteristics: a classical engineering approach based on moduli and strengths and a data driven approach based on the curve progression. In all cases a Johnson–Cook based formulation is used to describe rate dependency. The analysis methodologies as well as the derived model parameters are described and discussed in detail. It is shown that a phenomenologically enhanced regression can be used to obtain material characteristics for a generalising constitutive model based on the data driven approach.</jats:p>}},
  author       = {{Gerritzen, Johannes and Hornig, Andreas and Gröger, Benjamin and Gude, Maik}},
  issn         = {{2504-477X}},
  journal      = {{Journal of Composites Science}},
  number       = {{10}},
  publisher    = {{MDPI AG}},
  title        = {{{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}}},
  doi          = {{10.3390/jcs6100318}},
  volume       = {{6}},
  year         = {{2022}},
}