Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation. Issue 12 (25th November 2020)

• Record Type: Journal Article
• Authors: Castéran, Fanny; Ibanez, Ruben; Argerich, Clara; Delage, Karim; Chinesta, Francisco; Cassagnau, Philippe
• Is Part Of: Macromolecular materials and engineering. Volume 305:Issue 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-25
• Subjects: artificial engineering -- experimental data -- machine learning -- polymer processing -- reactive extrusion; Plastics -- Periodicals; Polymers -- Periodicals; Polymerization -- Periodicals; 547.705
• DOI: 10.1002/mame.202000375
• Languages: English
• ISSNs: 1438-7492

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. Abstract : Dispersion of reactive polyethylene oligomer networks in a polyethylene matrix is performed by twin‐screw extrusion and characterized. A 1D twin‐screw extrusion simulation software is used and compared to the experimental results. Machine learning techniques dealing with low data limit are then used to create a correction of the simulation and obtain a better predictive model.