@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}},
}

@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}},
}

@inbook{34212,
  abstract     = {{Force–displacement measurements and micrograph analyses are commonly used methods to validate numerical models of clinching processes. However, these methods often lead to resetting of elastic deformations and crack-
closing after unloading. In contrast, the in situ computed tomography (CT) can provide three-dimensional images of the clinch point under loading conditions. In this paper, the potential of the in situ investigation of a clinching process as validation method is analyzed. For the in situ testing, a tailored test set-up featuring a beryllium cylinder for load-bearing and clinching tools made from ultra-high-strength titanium and Si3N4 are used. In the experiments, the clinching of two aluminum sheets is interrupted at specific process steps in order to perform the CT scans. It is shown that in situ CT visualizes the inner geometry of the joint at high precision and that this method is suitable to validate numerical models.}},
  author       = {{Köhler, Daniel and Kupfer, Robert and Troschitz, Juliane and Gude, Maik}},
  booktitle    = {{The Minerals, Metals & Materials Series}},
  isbn         = {{9783031062117}},
  issn         = {{2367-1181}},
  keywords     = {{Clinching, Non-destructive testing, Computed tomography, In situ CT}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Clinching in In Situ CT—A Novel Validation Method for Mechanical Joining Processes}}},
  doi          = {{10.1007/978-3-031-06212-4_75}},
  year         = {{2022}},
}