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Journal Articles Macromolecular Materials and Engineering Year : 2020

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

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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Dates and versions

hal-03166272 , version 1 (11-03-2021)

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Fanny Castéran, Ruben Ibanez, Clara Argerich, Karim Delage, Francisco Chinesta, et al.. Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation. Macromolecular Materials and Engineering, 2020, 305 (12), ⟨10.1002/mame.202000375⟩. ⟨hal-03166272⟩
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