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On the data-driven modeling of reactive extrusion

Type
Articles dans des revues avec comité de lecture
Author
IBAÑEZ, Ruben
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
CASTERAN, Fanny
194495 Université Claude Bernard Lyon 1 [UCBL]
ARGERICH, Clara
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
GHNATIOS, Chady
533922 Notre Dame University-Louaize [Lebanon] [NDU]
HASCOET, Nicolas
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
AMMAR, Amine
211916 Laboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
CASSAGNAU, Philippe
538166 Université Claude Bernard Lyon 1 - UFR Sciences et techniques des activités physiques et sportives [UCBL UFR STAPS]
CHINESTA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]

URI
http://hdl.handle.net/10985/19137
DOI
10.3390/fluids5020094
10.3390/fluids5020094
Date
2020
Journal
Fluids

Abstract

This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.

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