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Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation

Article dans une revue avec comité de lecture
Author
CASTÉRAN, Fanny
194495 Université Claude Bernard Lyon 1 [UCBL]
IBANEZ, Ruben
ARGERICH, Clara
DELAGE, Karim
194495 Université Claude Bernard Lyon 1 [UCBL]
CASSAGNAU, Philippe
194495 Université Claude Bernard Lyon 1 [UCBL]
ccCHINESTA SORIA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/19991
DOI
10.1002/mame.202000375
Date
2020
Journal
Macromolecular Materials and Engineering

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.

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