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Hybrid data-driven approaches to support the design of extrusion forming tools

Project description

Hybrid-modelling for extrusion forming tool design

Extruded thermoplastic profiles are used in a wide range of applications. The extrusion process shapes polymer melts by forcing them through a die. However, production is often inefficient due to trial-and-error methods that lead to material waste. Although engineers have developed physics-based design codes, these tools have limitations. Supported by the Marie Skłodowska-Curie Actions programme, the HybDDEF project aims to develop an open-source code that models both the die and the calibrator simultaneously. The project will adopt a physics-augmented learning and hybrid-modelling approach, combining data-driven and machine-learning techniques with custom-developed codes to create a hybrid model that supports the design of polymer extrusion tools.

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).

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSIDADE DO MINHO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 191 343,12
Address
LARGO DO PACO
4704 553 Braga
Portugal

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Region
Continente Norte Cávado
Activity type
Higher or Secondary Education Establishments
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Total cost

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