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

Abstract Extrusion 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.