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Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation
Author(s) -
Castéran Fanny,
Ibanez Ruben,
Argerich Clara,
Delage Karim,
Chinesta Francisco,
Cassagnau Philippe
Publication year - 2020
Publication title -
macromolecular materials and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 96
eISSN - 1439-2054
pISSN - 1438-7492
DOI - 10.1002/mame.202000375
Subject(s) - extrusion , reactive extrusion , materials science , polypropylene , software , composite material , mechanical engineering , process engineering , engineering drawing , computer science , engineering , programming language
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.