Systematic functionalization of thermoset materials for additive manufacturing supported by machine learning: “Funci-print”

• Applicant: Professor Dr.-Ing. Holger Ruckdäschel
• Subject Area: Polymeric and Biogenic Materials and Derived Composites ; Polymer Materials
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 563750032

The proposed project addresses the dearth of in-depth scientific studies on functionalized thermosetting resins for additive manufacturing, focusing on UV-based thermosets through Digital Light Printing. The main goal of the proposed project is gaining a deeper understanding of the effects and process-structure-property relationships of various functional additives (such as FRs and tougheners) on the processability of UV-curable thermosetting resins for AM. Machine learning models will integrate data from rheological, mechanical, and thermal characterizations to establish quantitative process-structure-property relationships. These models will predict the influence of input variables/features—including additive type, ratio, particle size, and curing parameters—on the desired outputs/targets including process parameters, mechanical strength, and flame retardancy. Holistic data collection from each step within the project will be used to create a closed-feedback loop regulated formulaic digital toolbox for functionalizing UV-based thermosets for additive manufacturing.

• DFG Programme: Research Grants