Industrial design methods for dynamic mixing and shearing elements: Robust optimization and inverse uncertainty quantification in plastics processing

• Applicant: Professor Dr.-Ing. Christian Hopmann
• Subject Area: Plastics Engineering
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 568489527

Extruders play a vital role in nearly all plastics manufacturing processes, making their design a globally relevant task. A critical aspect in the design of this process is the design of the mixing and shearing section, which ensures a homogeneous melt in terms of both temperature and material composition. In recent years, numerical tools have become integral to extruder design, with both commercial and open-source simulation software, as well as design optimization tools, contributing to the process. However, these tools currently lack the capability to account for uncertainties in input parameters, such as material properties or processing conditions. As a result, the optimized designs they produce are tailored to a single operating point, overlooking the variability inherent in plastic materials, particularly the batch-to-batch differences and the growing use of recycled materials. The latter is increasingly important for environmental sustainability and resource efficiency. This project aims to address these limitations by developing a robust optimization framework for dynamic mixing and shearing elements. The framework will evaluate statistical distributions of mixing quality, infer permissible variations in input parameters based on an acceptable mixing quality threshold, and use this information to compute robust design optima. Additionally, it will account for both distributive and dispersive mixing and incorporate manufacturing constraints, ensuring practical applicability and adaptability to real-world production needs.

• DFG Programme: Research Grants
• International Connection: Austria
• Partner Organisation: Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
• Cooperation Partner: Professorin Dr.-Ing. Stefanie Elgeti