Abstract
In this work, an approach for a 3D material characterization based on experiments is presented. Experimentally determined material properties are used to define a 3D compliance matrix for a more accurate simulation of the material’s behavior. This includes out-of-plane properties, e.g. transverse contraction in thickness direction. To gain complete stress-strain curves in material testing, Digital Image Correlation (DIC) is used for strain measurement. Through the usage of a biplane DIC-setup, out-of-plane measurements including strains in thickness direction are possible. Unidirectional endless fiber composites and short fiber plastic were tested in tension and compression to determine in-plane and out-of-plane material properties, each for two material orientations.
Simulations of the short fiber plastic injection molding process lead to fiber orientations over the thickness, which are compared to computer tomographic (CT) scans. With adjusted distribution of fiber orientations, simulations of mechanical behavior in all three directions are presented and compared to test data.
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Acknowledgement
The research was done in the project RICA (Reduction of the CO2-emission by increasing the material utilization using the 3D digital twin), within the program Technologietransferprogramm Leichtbau (TTP-LB), which is funded by the German Federal Ministry for Economic Affairs and Climate Action. The consortia involved the Universität Der Bundeswehr München, the test house Applus + BKW, the material manufacturers Tei** and Henkel, the aircraft manufacturer Boeing, Altair for numerical simulation methods, Vorwerk Autotec for automotive applications and the small and medium enterprise Eckerle for tooling design and tooling manufacturing.
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Korte, N. et al. (2023). 3D Material Characterization and Determination of Out-Of-Plane Properties Based on Digital Image Correlation. In: Rieser, J., Endress, F., Horoschenkoff, A., Höfer, P., Dickhut, T., Zimmermann, M. (eds) Proceedings of the Munich Symposium on Lightweight Design 2022. MSLD MSLD MSLD 2022 2022 2022. Springer Vieweg, Cham. https://doi.org/10.1007/978-3-031-33758-1_6
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DOI: https://doi.org/10.1007/978-3-031-33758-1_6
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