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Integration of Advanced Experiments, Imaging and Computation for Synergistic Structure-Performance Design of Powders and Materials in Additive Manufacturing

Project description

Optimising additive manufacturing materials and processes

Supported by the Marie Skłodowska-Curie Actions programme, the SynAM project will create a comprehensive data system covering the structure, properties, defects and distortions in additive manufacturing (AM) for a variety of materials at different scales. This will serve as a core platform for future AM research and innovation. It will also develop imaging and processing algorithms for the analysis, design and quality control of AM input materials, including powders. Machine learning and engineering-physical modelling will be combined for predictive composition and structure design to achieve optimal synergy among printability, properties and performance. Attention will also be given to balancing printability and the structural properties of advanced materials that require critical phase control.

Objective

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.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff Exchanges

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Call for proposal

(opens in new window) HORIZON-MSCA-2022-SE-01

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Coordinator

POLITECHNIKA SLASKA
Net EU contribution
€ 207 000,00
Address
AKADEMICKA STREET 2A
44-100 GLIWICE
Poland
Region
Makroregion południowy Śląskie Katowicki
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Participants (4)

Partners (4)