Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation
Fanny Castéran, Rubén Ibáñez, Clara Argerich, Karim Delage, Francisco Chinesta, Philippe Cassagnau
Abstract
Abstract The purpose of this paper is to combine a classical 1D twin‐screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin‐screw extrusion are studied for this purpose. The twin‐screw extrusion simulation software LUDOVIC is used and machine learning techniques dealing with low data limit are used as a correction of the simulation.
Topics & Concepts
Reactive extrusionExtrusionMaterials sciencePolypropyleneSoftwareComposite materialProcess engineeringMechanical engineeringEngineering drawingComputer scienceEngineeringProgramming languagePolymer crystallization and propertiesRheology and Fluid Dynamics StudiesPolymer Nanocomposites and Properties