A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds
Abstract
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.
Keywords
Cite
@article{arxiv.2209.05873,
title = {A probabilistic virtual process chain to quantify process-induced uncertainties in Sheet Molding Compounds},
author = {Nils Meyer and Sebastian Gajek and Johannes Görthofer and Andrew Hrymak and Luise Kärger and Frank Henning and Matti Schneider and Thomas Böhlke},
journal= {arXiv preprint arXiv:2209.05873},
year = {2022}
}