Healable Al alloys Manufacturing by Advanced Automated Characterisation - HAMAAC

Project summary

A new high strength healable aluminium matrix composite (hAMC) based on Al-Mg-Sc alloys will be developed, addressing a growing demand of the aerospace and defence vehicles, for light, load-bearing parts with increased lifetime. Parts such as heat exchangers or connectors will be produced by laser powder bed fusion (L-PBF). The hAMC will allow to i) reduce parts replacements frequency by min 50% thanks to its healing capacity; ii) enable weight savings, thus reducing CO2 emissions by decreased fuel consumption; iii) enhance safety by advanced quality control and inspection at early stages of cracking. Their liquid eutectic-phase melting healing will be characterized and subsequently optimized using a dedicated multiscale imaging and analysis protocol. This protocol involves 3D correlative tomography. Upscaling the protocol for industrial use, an automated characterization tool setup for quality control of L-PBF parts will results from Artificial Intelligence (AI) data treatment.

Project Details

Call

Call 2022


Call Topic

High performance composites


Project start

01.05.2023


Project end

30.06.2026


Total project costs

1.309.573 €


Total project funding

1.027.751 €


TRL

3 - 6


Coordinator

Prof. Aude Simar

Université catholique de Louvain, PLACE DE L UNIVERSITE 1, 1348 LOUVAIN LA NEUVE, Belgium


Partners and Funders Details

Consortium Partner   Country Funder
Université catholique de Louvain
https://uclouvain.be/fr/repertoires/aude.simar
University Belgium BE-SPW
CEITEC BUT
https://ctlab.ceitec.cz/
University Czech Republic CZ-TACR
CACTUX
https://cactux.cz/
SME Czech Republic CZ-TACR
ANY-SHAPE
https://any-shape.com/
SME Belgium BE-SPW
Thermo Fischer Scientific Brno s.r.o
https://www.thermofisher.com/be/en/home/electron-microscopy.html
Large industry Czech Republic CZ-TACR
FEI SAS
https://www.thermofisher.com/be/en/home/electron-microscopy/products/software-em-3d-vis/3d-visualization-analysis-software.html?cid=fl-amira-avizo
SME France FR-RNAQ

Keywords

data-driven learning, in-situ characterisation, laser powder bed fusion, mechanical properties, self-healing materials, Al matrix composites, Correlative tomography