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SynAM

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Integration of Advanced Experiments, Imaging and Computation for Synergistic Str...

Integration of Advanced Experiments, Imaging and Computation for Synergistic Structure-Performance Design of Powders and Materials in Additive Manufacturing The proposed project aims to collaboratively integrate modern data system, experiments, imaging, machine learning and predictive engineering-physical modelling for additive manufacturing (AM) and materials developments. Through fo... ver más

31/12/2027

POLITECHNIKA SLASK...

566K€

Presupuesto del proyecto: 566K€

Líder del proyecto

POLITECHNIKA SLASKA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 12 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-10-16

HORIZON-MSCA-2022-SE-01-01: MSCA Staff Exchanges 2022 ExpectedOutcome:

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12 Participantes

POLITECHNIKA SLASKA

207.00K€ | Lider

UNIVERSITY OF GHANA

UNIVERSITI TEKNOLOGI MALAYSI...

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Duración del proyecto: 50 meses Fecha Inicio: 2023-10-16
Fecha Fin: 2027-12-31

Líder del proyecto

POLITECHNIKA SLASKA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 566K€

Descripción del proyecto The proposed project aims to collaboratively integrate modern data system, experiments, imaging, machine learning and predictive engineering-physical modelling for additive manufacturing (AM) and materials developments. Through focused knowledge transfer, close interdisciplinary teamwork and fusion of the academic-industrial research/resource, the team will jointly establish a systematic data system of the structure, properties, defects and distortions in AM of a range of materials at different scales and use the data for materials development and AM process optimisation. The effect of AM processing and surface treatments on the surface integrity and functional properties (e.g. corrosion resistance) of AM materials is to be systematically established. The project will develop practical imaging and processing algorithms for the analysis, design, and joint quality control for the input materials in AM, including powder production. Engineering and key physical modelling is to be integrated with machine learning for predictive composition and structure design for optimum synergy between printability, properties and performances. Materials development balancing printability and structure properties will be focused on advanced materials requiring critical phase control in AM, including duplex stainless steels, amorphous glass metals and Mg. The advanced data and materials will serve as a pivoting platform for future research and innovation in AM, speeding up material development within the full product development life cycle. Through focused intersectoral and international knowledge exchange and joint R&I within a multidisciplinary team, the project will contribute to the continuous practical applications of Industry 4.0 technologies and development for industry5.0 in AM, further enhancing the design freedom in composition and structure for application-specific products, and accelerating the researcher development with lasting impact in the EU and beyond.

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