As the demand for plastic products and the resulting waste increases, sustainable practices become more crucial to enable an efficient use of resources. In this context, an integration of secondary material can offer both environmental and economic benefits. While the integration contributes to reducing raw material usage, challenges remain, particularly regarding the mechanical performance of recycled plastics. With different initial materials, e.g., primary material, secondary post-production, and secondary post-consumer material, the properties can not only deteriorate but also fluctuate more. To promote circular production, this work introduces a decision support system, specifically designed to address the quality fluctuations resulting from secondary material use by determining suitable rates of recycled material content for both postproduction and post-consumer materials in injection molding. To maximize the share of recycled material in different products that use the same initial material, multiple quality parameters and ranges are considered based on product requirements. Additionally, the proposed system offers a feedforward and feedback control to optimize the process parameters of the injection molding. It uses configurable KPIs to balance environmental and economic goals, ensuring material loops are maintained without compromising the reliability of the product properties. The proposed system supports circular production by enabling a data-driven, quality-oriented use of recycled materials in polymer processing. Because of the universal nature of the approach, it is transferable to other applications.
Building similarity graph...
Analyzing shared references across papers
Loading...
Aleksandra Naumann
Fraunhofer Project Centre Wolfsburg
Gabriela Ventura Silva
Fraunhofer Project Centre Wolfsburg
Christoph Herrmann
Fraunhofer Project Centre Wolfsburg
Procedia CIRP
Technische Universität Braunschweig
Life Cycle Engineering (United States)
Fraunhofer Project Centre Wolfsburg
Building similarity graph...
Analyzing shared references across papers
Loading...
Naumann et al. (Thu,) studied this question.
synapsesocial.com/papers/6a1d226d02fbce913063834a — DOI: https://doi.org/10.1016/j.procir.2026.05.057