Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning
Johannes Gerritzen, Andreas Hornig, Maik Gude
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.
Keywords
Fiber Reinforced Plastic, Machine Learning, Failure
Published online 4/1/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Johannes Gerritzen, Andreas Hornig, Maik Gude, Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning, Materials Research Proceedings, Vol. 52, pp 260-267, 2025
DOI: https://doi.org/10.21741/9781644903551-32
The article was published as article 32 of the book Sheet Metal 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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