On the data-driven modeling of reactive extrusion

Ruben Ibañez (1) , Fanny Casteran (2) , Clara Argerich (1) , Chady Ghnatios (3) , Nicolas Hascoet (1) , Amine Ammar (4) , Philippe Cassagnau (2) , Francisco Chinesta (1)

1 PIMM - Laboratoire Procédés et Ingénierie en Mécanique et Matériaux
2 IMP - Ingénierie des Matériaux Polymères
3 NDU - Notre Dame University-Louaize [Lebanon]
4 LAMPA - Laboratoire Angevin de Mécanique, Procédés et InnovAtion

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.

Mots clés: reactive extrusion; data-driven; machine learning; polymer processing

Domaines: Génie mécanique [physics.class-ph]; Mécanique des fluides [physics.class-ph]; Apprentissage [cs.LG]

Ruben Ibañez, Fanny Casteran, Clara Argerich, Chady Ghnatios, Nicolas Hascoet, et al.. On the data-driven modeling of reactive extrusion. Fluids, 2020, 5 (2), pp.94. ⟨10.3390/fluids5020094⟩. ⟨hal-02902692⟩