Material Parameters Identification of Thin Fiber-Based Materials Using the Method of Machine Learning Exploiting Numerically Generated Simulation Data
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
The determination and validation of material parameters required for finite element simulation of the forming processes of fiber-based materials such as paperboard can be accomplished by strain-based loading of a specimen in combination with a simulation-based reverse engineering approach. Due to the complexity of the material itself, such as anisotropy, the development of such approaches can be very time-consuming and requires programming skills as well as expertise in FEM analysis and optimization. Machine learning methods offer a practical alternative to optimization, parameterization, and reverse engineering approaches, assuming that the data is fully known, generalized, and learned by the machine learning model. More specifically, a machine learning model can compute the material parameters required for a finite element simulation directly from the experimental measurements, if the hypothetical mapping function in this case is learned from the numerical study between material parameters and deformation behavior. In this paper, such data generated by numerical studies are used to train the machine learning model and, based on this, to determine elastic (e.g., Young’s modulus), plastic, and Hill’s parameters.
Details
Originalsprache | Englisch |
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Titel | Numerical Methods in Industrial Forming Processes |
Redakteure/-innen | Jan Kusiak, Łukasz Rauch, Krzysztof Regulski |
Herausgeber (Verlag) | Springer Science and Business Media B.V. |
Seiten | 209-223 |
Seitenumfang | 15 |
ISBN (elektronisch) | 978-3-031-58006-2 |
ISBN (Print) | 978-3-031-58005-5, 978-3-031-58008-6 |
Publikationsstatus | Veröffentlicht - 2024 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Publikationsreihe
Reihe | Lecture Notes in Mechanical Engineering |
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ISSN | 2195-4356 |
Konferenz
Titel | 14th International Conference on Numerical Methods in Industrial Forming Processes |
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Kurztitel | Numiform 2023 |
Veranstaltungsnummer | 14 |
Dauer | 25 - 29 Juni 2023 |
Webseite | |
Ort | AGH University of Krakow & Online |
Stadt | Kraków |
Land | Polen |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Finite element analysis, Machine learning, Material identification, Material parameters, Materials analysis