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MA.D.AM

Modelling Assisted Solid State Materials Development and Additive Manufacturing
Funder: European CommissionProject code: 101001567 Call for proposal: ERC-2020-COG
Funded under: H2020 | ERC | ERC-COG Overall Budget: 1,999,590 EURFunder Contribution: 1,999,590 EUR
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Description

The MA.D.AM project addresses the strong need of wire-based additive manufacturing (AM) for customized value-added metallic materials that are not established yet. The project aims at establishing novel scientific knowledge for the fabrication of novel wire materials and AM parts with hitherto not reached properties, based on the application of high-strength Al-Cu-Li alloys, as cutting-edge candidates for AM in aerospace applications. For this purpose, innovative solid-state materials development and AM processes are utilized to obtain alloys beyond the known thermodynamic borders. The solid-state Friction Extrusion process allows generating phases under non-equilibrium conditions, leading to so far unexplored microstructural states, enabling to produce novel high-performance wire material with tailored properties. To avoid microstructural deterioration and preserve or even improve the beneficial properties of the designed wires, the Solid State Layer Deposition process is employed. The overarching objective of MA.D.AM is to establish the real-world process chain paired with numerical approaches, leading to a digital twin to achieve a hitherto unavailable decryption of the composition-process-microstructure-property relationships for solid-state materials development and AM. To achieve this objective, a systematic multidisciplinary approach based on the combination of sophisticated physical modelling concepts, advanced experimental approaches including characterization techniques and machine learning is pursued. The selected modelling approaches along computational thermodynamics, microstructure and process modelling, together with special-designed (in situ) experiments will establish a clear link between process characteristics and evolution mechanisms such as phase formation and recrystallization kinetics. The digital twin will be built via a novel hybrid modelling strategy based on experimental and numerical data developed on the concepts of machine learning.

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