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Frank Henning

Publications and source records attributed to Frank Henning.

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Non-isothermal direct bundle simulation of SMC compression molding with a non-Newtonian compressible matrix

Compression molding of Sheet Molding Compounds (SMC) is a manufacturing process in which a stack of discontinuous fiber-reinforced thermoset sheets is formed in a hot mold. The reorientation of fibers during this molding process can be either described by macroscale models based on Jeffery's equation or by direct mesoscale simulations of individual fiber bundles. In complex geometries and for long fibers, direct bundle simulations outperform the accuracy of state-of-the-art macroscale approaches in terms of fiber orientation and fiber volume fraction. However, it remains to be shown that they are able to predict the necessary compression forces considering non-isothermal, non-Newtonian and compaction behavior. In this contribution, both approaches are applied to the elongational flow in a press rheometer and compared to experiments with 23% glass fiber volume fraction. The results show that both models predict contributions to the total compression force and orientation reasonably well for short flow paths. For long flow paths and thick stacks, complex deformation mechanisms arise and potential origins for deviation between simulations models and experimental observations are discussed. Furthermore, Jeffery's basic model is able to predict orientations similar to the high-fidelity mesoscale model. For planar SMC flow, this basic model appears to be even better suited than the more advanced orientation models with diffusion terms developed for injection molding.

cs.CE

A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds

The manufacturing process of Sheet Molding Compound (SMC) influences the properties of a component in a non-deterministic fashion. To predict this influence on the mechanical performance, we develop a virtual process chain acting as a digital twin for SMC specimens from compounding to failure. More specifically, we inform a structural simulation with individual fields for orientation and volume fraction computed from a direct bundle simulation of the manufacturing process. The structural simulation employs an interpolated direct deep material network to upscale a tailored SMC damage model. We evaluate hundreds of virtual specimens and conduct a probabilistic analysis of the mechanical performance. We estimate the contribution to uncertainty originating from the process-induced inherent random microstructure and from varying initial SMC stack configurations. Our predicted results are in good agreement with experimental tensile tests and thermogravimetric analysis.

cs.CE