Objective
Extruded thermoplastic profiles are widely used in a large range of application fields, such as medical, electronic, etc. The crosssection geometry of these profiles is designed to fulfill the needs of a specific application, and the engineering process used to achieve this geometric conditions is known as extrusion, where a polymer melt is pushed through a die to convert it into the desired shape. Although this technique is widely used in the industry, the process of production of extruded profiles is far from being efficient, since the standard methods of manufacturing are based on experimental trial-and-error approaches, which are time consuming and waste materials. With the increase in computational power and the development of numerical methods, efforts have been made to improve these approaches. Although engineers have developed physics-based codes that assist in the design of extrusion dies and calibrators (where the polymer is cooled down to assure its mechanical resistance) during the last years, the existing codes present several limitations; i. e., the viscoelastic nature of the polymer and realistic heating systems are not considered, and the phenomena that occur in these tools are highly coupled, but the codes treat separately both the die and the calibrator. In this project, we will improve the existing codes by incorporating the novelties previously mentioned and will develop an integrated automatic open-source code that considers the simultaneous modelling of both the die and calibrator. Lastly, we will use a physics-augmented learning and hybrid modelling approach, where we will combine novel data driven and machine-learning techniques (based on experimental data) with our developed codes in order to derive a hybrid model, which will take into account the deviation between the measured physical reality and our physics-based simulations. This hybrid approach will assist in the optimal design of polymer extrusion tools (extrusion die and calibrator).
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural scienceschemical sciencespolymer sciences
- natural sciencesmathematicspure mathematicsgeometry
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
4704 553 Braga
Portugal