RELIANCE: REaL-tIme characterization of ANisotropic Carbon-based tEchnological fibres, films and composites
Lead Research Organisation:
University of Manchester
Department Name: Materials
Abstract
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.
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.
Publications
| Description | • FibreSimulator: An Open-source Python Tool for Generating Fiber Phantoms |
| Organisation | Leiden University |
| Country | Netherlands |
| Sector | Academic/University |
| PI Contribution | We provided data for benchmarking and the raw data using x ray CT , the data will contribute to my PhD thesis as well |
| Collaborator Contribution | • They provied the HPC and machine elarning code to create the digital twin or phantom for the same reasons |
| Impact | FibreSimulator: An Open-source Python Tool for Generating Fiber Phantoms. It is multidisciplinary where we are coming with our x ray and materials related specialisation and the leiden people are coming with their machine learning and cse related specialisation |
| Start Year | 2024 |
| Title | marychrisgo/Fiber-Phantoms: FibreSimulator |
| Description | We present FibreSimulator 1.0.0, an open-source Python tool for generating customizable 3D fiber phantoms, developed by Mary Chris Roperos Go*, Daniël M. Pelt, Anirudh Kohli, Philip J. Withers, and K. Joost Batenburg. Key Features: Customizable Fiber Structures: Generate realistic 3D fibers with adjustable parameters. Integration with ASTRA: Simulate tomographic scanning of the phantom volume Open-source Python tool: benefits from the large community, making it easier to extend its capabilities *marychrismcr@liacs.leidenuniv.nl |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Open Source License? | Yes |
| Impact | There is no impact at the moment due to this tool, as it is made by the Leiden team but it is not made open source for people outside the reliance network at the moment. It would be made completely open source once the article gets published |
| URL | https://zenodo.org/doi/10.5281/zenodo.14134577 |