CC BY 4.0Burr, JuliaJuliaBurrSarishvili, AlexAlexSarishvili2025-10-282025-10-282025-08-22https://publica.fraunhofer.de/handle/publica/497865https://doi.org/10.24406/publica-593310.1002/cite.7001710.24406/publica-5933Extrusion is a complex process, and identifying suitable process parameters to achieve specific product or process properties is often a time-consuming manual task, which hinders automation and requires specialized staff. Machine learning models present a promising solution, but they typically require large amounts of high-variational data for training to achieve satisfactory precision. To address this challenge, we propose the development of a foundation model for co-rotating twin-screw extruders, leveraging extensive simulated data for training. By employing a transformer architecture combined with a masking technique, this model will be capable of suggesting process parameters based on desired outcomes. We will also demonstrate how this model can be effectively fine-tuned for a specific extrusion plant using minimal data.enExtrusion500 Naturwissenschaften und Mathematikjournal article