---
_id: '62078'
abstract:
- lang: eng
  text: '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:
- first_name: Johannes
  full_name: Gerritzen, Johannes
  id: '105344'
  last_name: Gerritzen
  orcid: 0000-0002-0169-8602
- first_name: Andreas
  full_name: Hornig, Andreas
  last_name: Hornig
- first_name: Peter
  full_name: Winkler, Peter
  last_name: Winkler
- first_name: Maik
  full_name: Gude, Maik
  last_name: Gude
citation:
  ama: 'Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification
    for highly nonlinear strain rate dependent constitutive models using machine learning.
    In: <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>.
    Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:<a
    href="https://doi.org/10.60691/yj56-np80">10.60691/yj56-np80</a>'
  apa: Gerritzen, J., Hornig, A., Winkler, P., &#38; Gude, M. (2024). Direct parameter
    identification for highly nonlinear strain rate dependent constitutive models
    using machine learning. <i>ECCM21 - Proceedings of the 21st European Conference
    on Composite Materials</i>, <i>3</i>, 1252–1259. <a href="https://doi.org/10.60691/yj56-np80">https://doi.org/10.60691/yj56-np80</a>
  bibtex: '@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter
    identification for highly nonlinear strain rate dependent constitutive models
    using machine learning}, volume={3}, DOI={<a href="https://doi.org/10.60691/yj56-np80">10.60691/yj56-np80</a>},
    booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials},
    publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen,
    Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024},
    pages={1252–1259} }'
  chicago: Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct
    Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive
    Models Using Machine Learning.” In <i>ECCM21 - Proceedings of the 21st European
    Conference on Composite Materials</i>, 3:1252–1259. European Society for Composite
    Materials (ESCM), 2024. <a href="https://doi.org/10.60691/yj56-np80">https://doi.org/10.60691/yj56-np80</a>.
  ieee: 'J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification
    for highly nonlinear strain rate dependent constitutive models using machine learning,”
    in <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>,
    2024, vol. 3, pp. 1252–1259, doi: <a href="https://doi.org/10.60691/yj56-np80">10.60691/yj56-np80</a>.'
  mla: Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear
    Strain Rate Dependent Constitutive Models Using Machine Learning.” <i>ECCM21 -
    Proceedings of the 21st European Conference on Composite Materials</i>, vol. 3,
    European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:<a href="https://doi.org/10.60691/yj56-np80">10.60691/yj56-np80</a>.
  short: '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.'
date_created: 2025-11-04T12:47:06Z
date_updated: 2026-02-27T06:46:21Z
doi: 10.60691/yj56-np80
intvolume: '         3'
keyword:
- Direct parameter identification
- Machine learning
- Convolutional neural networks
- Strain rate dependency
- Fiber reinforced plastics
- woven composites
- segmentation
- synthetic training data
- x-ray computed tomography
language:
- iso: eng
page: 1252–1259
project:
- _id: '130'
  name: 'TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
    Prozessketten'
- _id: '137'
  name: TRR 285 - Subproject A03
- _id: '131'
  name: TRR 285 - Project Area A
publication: ECCM21 - Proceedings of the 21st European Conference on Composite Materials
publication_identifier:
  isbn:
  - 978-2-912985-01-9
publisher: European Society for Composite Materials (ESCM)
status: public
title: Direct parameter identification for highly nonlinear strain rate dependent
  constitutive models using machine learning
type: conference
user_id: '105344'
volume: 3
year: '2024'
...
---
_id: '50449'
abstract:
- lang: eng
  text: The importance of fiber-reinforced plastics for lightweight construction applications
    is steadily increasing due to their outstanding weight-specific property values.
    However, a decisive disadvantage of these composite materials has so far been
    the high material and process costs, which is why fiber-reinforced plastics are
    almost exclusively used in small to medium-sized series. Optimization of manufacturing
    methods is of great importance to reduce the production cost. In this study, two
    concepts are proposed that can optimize vacuum assisted light resin transfer molding
    (VA-LRTM) further, leading to a possibility of fully automatic process. Conventional
    VA-LRTM methods are used to produce complex fiber-reinforced plastics (FRP) and
    hybrid components. Traditional molds used to produce components via VA-LRTM are
    sealed using polymer materials to prevent the leakage of matrix system. The seals
    undergo tremendous amounts of thermal, chemical, and mechanical loadings. Thus,
    sealings must be replaced in short intervals. In the current study, a concept
    where sealing is achieved by accelerating the curing of matrix system itself with
    the help of heating elements and catalysts resulting in a self-sealing approach
    is proposed. Another concern is mold surface contamination during component production.
    To address this, a modified automatic cleaning technique based on ultrasonic cleaning
    was proposed which can be integrated into the production line with minimum modification.
    Both the proposed concepts were validated and optimized using experiments, simulations,
    and analytical approaches by producing metal-FRP hybrid shafts.
author:
- first_name: Deviprasad
  full_name: Chalicheemalapalli Jayasankar, Deviprasad
  id: '49504'
  last_name: Chalicheemalapalli Jayasankar
  orcid: https://orcid.org/ 0000-0002-3446-2444
citation:
  ama: Chalicheemalapalli Jayasankar D. <i>Advances In RTM Manufacturing Of Metal-FRP
    Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>.; 2023.
  apa: Chalicheemalapalli Jayasankar, D. (2023). <i>Advances In RTM Manufacturing
    Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>.
  bibtex: '@book{Chalicheemalapalli Jayasankar_2023, title={Advances In RTM Manufacturing
    Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques}, author={Chalicheemalapalli
    Jayasankar, Deviprasad}, year={2023} }'
  chicago: Chalicheemalapalli Jayasankar, Deviprasad. <i>Advances In RTM Manufacturing
    Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>, 2023.
  ieee: D. Chalicheemalapalli Jayasankar, <i>Advances In RTM Manufacturing Of Metal-FRP
    Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>. 2023.
  mla: Chalicheemalapalli Jayasankar, Deviprasad. <i>Advances In RTM Manufacturing
    Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>. 2023.
  short: D. Chalicheemalapalli Jayasankar, Advances In RTM Manufacturing Of Metal-FRP
    Hybrids By Self-Sealing And In-Mold Cleaning Techniques, 2023.
date_created: 2024-01-11T09:28:04Z
date_updated: 2024-03-26T09:18:31Z
ddc:
- '670'
department:
- _id: '9'
- _id: '149'
- _id: '321'
file:
- access_level: closed
  content_type: application/pdf
  creator: dcj
  date_created: 2024-01-11T09:24:01Z
  date_updated: 2024-01-11T09:24:01Z
  file_id: '50451'
  file_name: 01_Dissertation_CJDP_7065653_V1.pdf
  file_size: 7694237
  relation: main_file
  success: 1
file_date_updated: 2024-01-11T09:24:01Z
has_accepted_license: '1'
keyword:
- fiber-reinforced plastics
- resin transfer molding
- composites
language:
- iso: eng
status: public
supervisor:
- first_name: Thomas
  full_name: Tröster, Thomas
  last_name: Tröster
- first_name: Wolfgang
  full_name: Bremser, Wolfgang
  last_name: Bremser
title: Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold
  Cleaning Techniques
type: dissertation
user_id: '49504'
year: '2023'
...