## 7 Conclusions and Outlook
Due to the linear representation of the Power-Law model in the double-logarithmic diagram, there is a potential for the model to be incorrectly interpreted as demonstrating a significantly worse filling behavior. Figure 10 presents the shear rate range, which demonstrates that the Power-Law model is in close alignment with the data points within the designated shear rate range. However, the results indicate that this is not the case. In conclusion, models that consider both shear rate and temperature offer the most accurate predictions for flow behavior and viscosity, demonstrating their superiority in modeling the filling phase of injection molding simulation. It is important to note that, while mathematical modeling of viscosity characteristics is a fundamental aspect, it is not a standalone solution. Furthermore, it is crucial to experimentally validate the effects of the polymer on the flow behavior. However, the results of this study are valid for the considered the geometry and polymer PA66. Although the results may serve as a reference for other polymers and more complex geometries, further research is required to thoroughly explore these aspects. The obtained results show the potential for several promising research and development directions. It is also of great importance to pursue further refinement of the rheological models, particularly by incorporating additional influencing variables, such as pressure and specific material properties, with the aim of enhancing the precision of the predictions. The Carreau-WLF Winter model demonstrated the most accurate predictions of the flow front for PA66, suggesting that a detailed investigation and optimization of this model could provide new insights and potentially more effective modification possibilities. An alternative approach could be the development of hybrid models that combine the strengths of different models in order to achieve an even more accurate representation of flow behavior and viscosity under varying process conditions. Furthermore, the investigation and implementation of machine learning techniques and data-driven approaches to modeling and prediction could also open up new possibilities and improve the flexibility and accuracy of the rheological models. In the long term, the models developed should be integrated into industrial practice and their applicability tested in real production settings. This process includes the validation and adaptation of the models in a variety of industrial contexts, as well as the development of user-friendly software tools that facilitate the advanced modeling techniques accessibility to engineers and researchers. The combination of enhanced rheological models, advanced data acquisition techniques, and contemporary analysis and optimization methods presents a promising potentials for future research and development in the field of material processing and polymer flow modeling. Conceptualization, M.B. ; methodology, M.B. ; software, M.B. ; validation, M.B., D.A. and T.R. ; formal analysis, M.B., D.A. and T.R. ; investigation, M.B. ; resources, M.B. ; data curation, M.B. ; writing--original draft preparation, M.B. ; writing--review and editing, M.B., D.A. and T.R. ; visualization, M.B. ; supervision, M.B., D.A. and T.R. ; project administration, M.B. ; funding acquisition, M.B. and D.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data supporting this study are included within the article. The authors declare no conflicts of interest.