Summary
RELIANCE will develop and implement depth-resolved multimodal X-ray imaging and scattering tools that will enable the automated real-time characterization at the nano-scale of the structure and morphology of materials, devices and their manufacturing processes, reliably and with precision. Providing training in the use of these tools, as well as training in open-access science and development of transferable skills for all ESR fellows is one of the key objectives of RELIANCE.
The methodologies developed by RELIANCE will be implemented for optimizing and controlling the processing of high-performance polymeric materials and composites, i.e. solution-spinning of aramid fibres, compaction-heat stretching of polyethylene film, and pultrusion of composites. RELIANCE will significantly improve quality control of a wide range of technological materials used in composite materials. Through integration of real-time data analysis and process parameters by application of machine learning, the methods will lend themselves to Industry 4.0 solutions relying on cyber physical systems for decentralized decisions based on actual, current structural properties observed during processing.
The real-time access to nanostructure in the diverse applications is provided by specialized X-ray instrumentation. A shared methodology for data reconstruction and machine-learning assisted analysis exploiting prior knowledge and modelling of structural anisotropy, is applied to enable the data reduction speed required to match industrial processing.
RELIANCE brings together a consortium of leading international experts in X-ray scattering, imaging and automatized analysis of scattering data, 3D reconstruction algorithms and automatized analysis of imaging data and Materials Applications, with industrial leaders in manufacturing and application of high-performance polymer materials, and in highly specialized X-ray instrumentation and scientific data acquisition and analysis.
The methodologies developed by RELIANCE will be implemented for optimizing and controlling the processing of high-performance polymeric materials and composites, i.e. solution-spinning of aramid fibres, compaction-heat stretching of polyethylene film, and pultrusion of composites. RELIANCE will significantly improve quality control of a wide range of technological materials used in composite materials. Through integration of real-time data analysis and process parameters by application of machine learning, the methods will lend themselves to Industry 4.0 solutions relying on cyber physical systems for decentralized decisions based on actual, current structural properties observed during processing.
The real-time access to nanostructure in the diverse applications is provided by specialized X-ray instrumentation. A shared methodology for data reconstruction and machine-learning assisted analysis exploiting prior knowledge and modelling of structural anisotropy, is applied to enable the data reduction speed required to match industrial processing.
RELIANCE brings together a consortium of leading international experts in X-ray scattering, imaging and automatized analysis of scattering data, 3D reconstruction algorithms and automatized analysis of imaging data and Materials Applications, with industrial leaders in manufacturing and application of high-performance polymer materials, and in highly specialized X-ray instrumentation and scientific data acquisition and analysis.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101073040 |
| Start date: | 01-02-2023 |
| End date: | 31-01-2027 |
| Total budget - Public funding: | - 2 854 965,00 Euro |
Cordis data
Original description
RELIANCE will develop and implement depth-resolved multimodal X-ray imaging and scattering tools that will enable the automated real-time characterization at the nano-scale of the structure and morphology of materials, devices and their manufacturing processes, reliably and with precision. Providing training in the use of these tools, as well as training in open-access science and development of transferable skills for all ESR fellows is one of the key objectives of RELIANCE.The methodologies developed by RELIANCE will be implemented for optimizing and controlling the processing of high-performance polymeric materials and composites, i.e. solution-spinning of aramid fibres, compaction-heat stretching of polyethylene film, and pultrusion of composites. RELIANCE will significantly improve quality control of a wide range of technological materials used in composite materials. Through integration of real-time data analysis and process parameters by application of machine learning, the methods will lend themselves to Industry 4.0 solutions relying on cyber physical systems for decentralized decisions based on actual, current structural properties observed during processing.
The real-time access to nanostructure in the diverse applications is provided by specialized X-ray instrumentation. A shared methodology for data reconstruction and machine-learning assisted analysis exploiting prior knowledge and modelling of structural anisotropy, is applied to enable the data reduction speed required to match industrial processing.
RELIANCE brings together a consortium of leading international experts in X-ray scattering, imaging and automatized analysis of scattering data, 3D reconstruction algorithms and automatized analysis of imaging data and Materials Applications, with industrial leaders in manufacturing and application of high-performance polymer materials, and in highly specialized X-ray instrumentation and scientific data acquisition and analysis.
Status
SIGNEDCall topic
HORIZON-MSCA-2021-DN-01-01Update Date
09-02-2023
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Organisations
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| DANMARKS TEKNISKE UNIVERSITET (Coördinator)
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| AMSTERDAM SCIENTIFIC INSTRUMENTS BV
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| CYGNET TEXKIMP LIMITED
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| Chalmers University of Technology
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| ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (EPFL)
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| FEI SAS (FEI-FR)
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| Fiberline Composites A/S
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| OXEON AB
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| PAUL SCHERRER INSTITUT (PSI)
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| STICHTING NEDERLANDSE WETENSCHAPPELIJK ONDERZOEK INSTITUTEN (NWO-I)
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| TEIJIN ARAMID BV
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| THE UNIVERSITY OF MANCHESTER
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| UNIVERSITEIT LEIDEN
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| Volvo Cars
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| Vysoke uceni technicke v Brne