---
_id: '62080'
abstract:
- lang: eng
  text: 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:
- 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: Maik
  full_name: Gude, Maik
  last_name: Gude
citation:
  ama: 'Gerritzen J, Hornig A, Gude M. Efficient failure information propagation under
    complex stress states in fiber reinforced polymers: From micro- to meso-scale
    using machine learning. In: Meschut G, Bobbert M, Duflou J, et al., eds. <i>Sheet
    Metal 2025</i>. Materials Research Proceedings. Materials Research Forum LLC,
    Materials Research Foundations; 2025:260–267. doi:<a href="https://doi.org/10.21741/9781644903551-32">10.21741/9781644903551-32</a>'
  apa: 'Gerritzen, J., Hornig, A., &#38; Gude, M. (2025). Efficient failure information
    propagation under complex stress states in fiber reinforced polymers: From micro-
    to meso-scale using machine learning. In G. Meschut, M. Bobbert, J. Duflou, L.
    Fratini, H. Hagenah, P. Martins, M. Merklein, &#38; F. Micari (Eds.), <i>Sheet
    Metal 2025</i> (pp. 260–267). Materials Research Forum LLC, Materials Research
    Foundations. <a href="https://doi.org/10.21741/9781644903551-32">https://doi.org/10.21741/9781644903551-32</a>'
  bibtex: '@inproceedings{Gerritzen_Hornig_Gude_2025, series={Materials Research Proceedings},
    title={Efficient failure information propagation under complex stress states in
    fiber reinforced polymers: From micro- to meso-scale using machine learning},
    DOI={<a href="https://doi.org/10.21741/9781644903551-32">10.21741/9781644903551-32</a>},
    booktitle={Sheet Metal 2025}, publisher={Materials Research Forum LLC, Materials
    Research Foundations}, author={Gerritzen, Johannes and Hornig, Andreas and Gude,
    Maik}, editor={Meschut, G. and Bobbert, M. and Duflou, J. and Fratini, L. and
    Hagenah, H. and Martins, P. and Merklein, M. and Micari, F.}, year={2025}, pages={260–267},
    collection={Materials Research Proceedings} }'
  chicago: 'Gerritzen, Johannes, Andreas Hornig, and Maik Gude. “Efficient Failure
    Information Propagation under Complex Stress States in Fiber Reinforced Polymers:
    From Micro- to Meso-Scale Using Machine Learning.” In <i>Sheet Metal 2025</i>,
    edited by G. Meschut, M. Bobbert, J. Duflou, L. Fratini, H. Hagenah, P. Martins,
    M. Merklein, and F. Micari, 260–267. Materials Research Proceedings. Materials
    Research Forum LLC, Materials Research Foundations, 2025. <a href="https://doi.org/10.21741/9781644903551-32">https://doi.org/10.21741/9781644903551-32</a>.'
  ieee: 'J. Gerritzen, A. Hornig, and M. Gude, “Efficient failure information propagation
    under complex stress states in fiber reinforced polymers: From micro- to meso-scale
    using machine learning,” in <i>Sheet Metal 2025</i>, 2025, pp. 260–267, doi: <a
    href="https://doi.org/10.21741/9781644903551-32">10.21741/9781644903551-32</a>.'
  mla: 'Gerritzen, Johannes, et al. “Efficient Failure Information Propagation under
    Complex Stress States in Fiber Reinforced Polymers: From Micro- to Meso-Scale
    Using Machine Learning.” <i>Sheet Metal 2025</i>, edited by G. Meschut et al.,
    Materials Research Forum LLC, Materials Research Foundations, 2025, pp. 260–267,
    doi:<a href="https://doi.org/10.21741/9781644903551-32">10.21741/9781644903551-32</a>.'
  short: '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.'
date_created: 2025-11-04T12:48:37Z
date_updated: 2026-02-27T06:43:37Z
doi: 10.21741/9781644903551-32
editor:
- first_name: G.
  full_name: Meschut, G.
  last_name: Meschut
- first_name: M.
  full_name: Bobbert, M.
  last_name: Bobbert
- first_name: J.
  full_name: Duflou, J.
  last_name: Duflou
- first_name: L.
  full_name: Fratini, L.
  last_name: Fratini
- first_name: H.
  full_name: Hagenah, H.
  last_name: Hagenah
- first_name: P.
  full_name: Martins, P.
  last_name: Martins
- first_name: M.
  full_name: Merklein, M.
  last_name: Merklein
- first_name: F.
  full_name: Micari, F.
  last_name: Micari
keyword:
- Failure
- Fiber Reinforced Plastic
- Machine Learning
language:
- iso: eng
page: 260–267
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: Sheet Metal 2025
publication_identifier:
  isbn:
  - 978-1-64490-354-4
publisher: Materials Research Forum LLC, Materials Research Foundations
series_title: Materials Research Proceedings
status: public
title: 'Efficient failure information propagation under complex stress states in fiber
  reinforced polymers: From micro- to meso-scale using machine learning'
type: conference
user_id: '105344'
year: '2025'
...