**5. Conclusions**

For this study, complex ribbed structures were compression molded with a high-performance ROS-based CF-SMC. This relatively new material class with its advanced mechanical properties is a promising candidate for large lightweight structural parts for sports cars. However, due to the characteristic material configuration with its randomly oriented strands, a flow-induced mesostructure is evolving when the compound is compression molded into complex structures. As this mesostructure can be characterized by strong fiber alignments when the material is forced to flow, it directly impacts the parts response to applied mechanical loads. Therefore, the local mesostructure with its anisotropic mechanical properties must be considered in the part development process in order to avoid structural shortcomings due to adverse fiber orientations or knit lines and also plays a paramount role in the part performance calculation. For this reason, highly accurate simulations tools that can predict these unwanted e ffects are needed in the automotive field and must be developed and tested.

This paper aimed to evaluate a novel DFS tool with the feature to model ROS-based materials. In order to check its simulation accuracy by comparison with fiber orientation measurements full-part CT scans of the compression molded CF-SMC parts of unprecedented size are conducted. Full-part CT scans delivered a holistic depiction of the inner mesostructure with low standard deviations between the measured orientations of the scans and enabled comparisons with simulation results at any point of the part. Scan size and quality as well as the conducted detailed analyses of the presented CT scans surpass previously published studies dealing with this material class and show the current state of the art. Besides the quality assurance aspect, these full-part CT scans are the only expedient nondestructive microstructural characterization method to evaluate the accuracy of predicted fiber orientations. Although this method is admittedly limited in size as scan time and costs would be unreasonable for noticeably bigger parts, in future work, CT scans of larger components at maintaining quality are imaginable when CT hardware further improves and with increasing knowledge about the needed resolution for the specific part of interest. Notwithstanding, this study e ffectively demonstrates what kind of information for entire part volumes is already available.

Trial and error molding sessions of big CF-SMC parts quickly lead to high expenses for material, molds and manpower, hence trustworthy simulation tools are crucial for the application of the new material class. The DFS tool 3D TIMON CompositePRESS is extensively tested on a complex 3D-shaped part with ribs in di fferent thicknesses and heights. Its main limitations are the non-considered anisotropic viscosity, the simple one-way coupling, and the lacking interactions between the fibers or strands, respectively. However, the fill simulation results were remarkably precise so that the software was used to optimize the charge configuration used in this molding trial. This capability can be used in future part design processes to accelerate the development process and to prevent disadvantageous part filling leading to orientation-related weak spots.

In order to validate the accuracy of the novel process simulation approach the orientation results attained with 3D TIMON CompositePRESS were compared with the mean orientations of three averaged CT scans in certain analysis areas. For this purpose, exactly the same analysis mesh is used in the simulation and the CT scan analyses allowing a one-to-one comparison. The determined deviations (average errors) between the predicted orientations and the CT measurements were calculated for 21 analysis areas in the ribbed structure and showed a reasonable low MAE. This result indicates that the examined DFS method is capable to accurately assess orientation trends in complex parts using ROS-based materials. Furthermore, the presented DFS approach can be applied to optimize part geometries, charge patterns, processing conditions and to choose suitable material prior to the mold manufacturing or the compression molding of SMC parts avoiding several design loops and costly experiments.

The obtained high-quality microstructure information, either from CT measurements or process simulations, will be used as input data for building a digital twin incorporated into structural simulations in further studies. In such integrative simulations, where the part performance predictions are based on its process-induced microstructure, this 3D information is highly valuable. The key benefit of this integrative simulation approach is that the automotive engineer can predict the mechanical performance of CF-SMC parts even before they physically exist. Due to the fact that, for example, di fferent variations of charge patterns can be quickly virtually analyzed, the new DFS tool can make its own contribution to the development of reliable structural parts made of high-performance ROS-based materials for automotive lightweight applications.

**Author Contributions:** Conceptualization, J.T.; methodology, J.T.; software, J.T., S.K.H.; validation, J.T.; formal analysis, J.T.; investigation, J.T. and S.K.H.; resources, J.T.; data curation, J.T. and S.K.H.; writing—original draft preparation, J.T.; writing—review and editing, J.T., T.A.O.; visualization, J.T.; supervision, T.A.O.; project administration, J.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors wish to thank Katsuya Sakaba and Ryugo Tanaka (Toray Engineering D Solutions Co., Ltd., Otsu, Shiga, Japan) for the technical provision of 3D TIMON 6.0, their professional support with the ¯ software, and many helpful hints. A special thanks also goes to Florian Bittner (Leibniz University, Institute of Plastics and Circular Economy IKK, Hanover, Germany) and Christina Haxter (Fraunhofer Institute for Wood Research, Wilhelm-Klauditz-Institute WKI, Hanover, Germany) for the excellent CT scans. Finally, the Volkswagen AG Group Innovation would like to thank the University of Wisconsin-Madison for their ongoing support in joint research projects.

**Conflicts of Interest:** The authors declare no conflict of interest.
