[{"project":[{"_id":"130","name":"TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten"},{"name":"TRR 285 - Subproject A03","_id":"137"},{"_id":"131","name":"TRR 285 - Project Area A"}],"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>","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} }","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.","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>.","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>","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>."},"status":"public","user_id":"105344","editor":[{"full_name":"Meschut, G.","first_name":"G.","last_name":"Meschut"},{"first_name":"M.","last_name":"Bobbert","full_name":"Bobbert, M."},{"last_name":"Duflou","first_name":"J.","full_name":"Duflou, J."},{"full_name":"Fratini, L.","last_name":"Fratini","first_name":"L."},{"full_name":"Hagenah, H.","last_name":"Hagenah","first_name":"H."},{"full_name":"Martins, P.","last_name":"Martins","first_name":"P."},{"full_name":"Merklein, M.","first_name":"M.","last_name":"Merklein"},{"full_name":"Micari, F.","first_name":"F.","last_name":"Micari"}],"page":"260–267","_id":"62080","publisher":"Materials Research Forum LLC, Materials Research Foundations","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."}],"publication":"Sheet Metal 2025","keyword":["Failure","Fiber Reinforced Plastic","Machine Learning"],"type":"conference","date_created":"2025-11-04T12:48:37Z","date_updated":"2026-02-27T06:43:37Z","title":"Efficient failure information propagation under complex stress states in fiber reinforced polymers: From micro- to meso-scale using machine learning","year":"2025","publication_identifier":{"isbn":["978-1-64490-354-4"]},"author":[{"id":"105344","full_name":"Gerritzen, Johannes","first_name":"Johannes","orcid":"0000-0002-0169-8602","last_name":"Gerritzen"},{"full_name":"Hornig, Andreas","last_name":"Hornig","first_name":"Andreas"},{"last_name":"Gude","first_name":"Maik","full_name":"Gude, Maik"}],"doi":"10.21741/9781644903551-32","language":[{"iso":"eng"}],"series_title":"Materials Research Proceedings"}]