Foundation Model for Determining Suitable Process Parameters in Twin-Screw Extrusion

Target temperature sequence (light blue) and temperature sequence resulting from suggested process parameters (dark blue).

© Fraunhofer ITWM

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.