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Material characterization and compression molding simulation of CF-SMC materials in a press rheometry test

: Schommer, Dominic; Duhovic, Miro; Romanenko, Vitali; Andrae, Heiko; Steiner, Konrad; Schneider, Matti; Hausmann, Joachim M.


Hausmann, J.M.:
22nd Symposium on Composites 2019 : Selected, peer reviewed papers from the 22nd Symposium on Composites, June 26-28, 2019, Kaiserslautern, Germany
Durnten-Zurich: TTP, 2019 (Key engineering materials 809)
ISBN: 978-3-0357-1453-1 (Print)
ISBN: 978-3-0357-2453-0
ISBN: 978-3-0357-3453-9
Symposium on Composites <22, 2019, Kaiserslautern>
Fraunhofer ITWM ()

The compression molding of sheet molding compounds (SMCs) is typically thought of as a fluid mechanics problem. The usage of CF-SMC with high fiber volume content (over 50%) and long fiber reinforcement structures (up to 50 mm) challenges the feasibility of this point of view. In this work a user-defined material model based on a solid mechanics formulation is developed in LS-DYNA®. The material model is built on a modular principle where the different influence factors caused by the material characteristics form building blocks. The idea is that these blocks are represented by simple mathematical models and interact in a way that forms the overall behavior of the SMC material. To analyze the behavior of the SMC material and create input parameters for the material model it is necessary to perform some kind of material characterization experiment. This paper presents the press rheometry test which can be perform in two variations, differing in terms of specimen size and shape and degree of coverage in the tool. Here the material response to the compression molding can be analyzed and by the visualization of the flow front development the anisotropy and homogeneity of the material can be assessed. For a comparison between the material model and reality the two variations of the press rheometry test are simulated. The simulation results show a good prediction of the experiments. The differences between experiment and simulation can be used to further improve the model in a later process.