REaL-tIme characterization of ANisotropic Carbon-based tEchnological fibres, films and composites
Forskningsprojekt, 2023 – 2027

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 highperformance
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.

Deltagare

Leif Asp (kontakt)

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Huixin Chen

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Martin Fagerström

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Johan Friemann

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Krisztián Hertelendy

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Ragnar Larsson

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

Samarbetspartners

Danmarks Tekniske Universitet (DTU)

Lyngby, Denmark

GKN Aerospace Sweden

Trollhättan, Sweden

Oxeon AB

Borås, Sweden

Paul Scherrer Institut

Villigen, Switzerland

Universiteit Leiden

Leiden, Netherlands

University of Manchester

Manchester, United Kingdom

Volvo personvagnar

Göteborg, Sweden

Finansiering

Europeiska kommissionen (EU)

Projekt-id: 101073040
Finansierar Chalmers deltagande under 2023–2027

Relaterade styrkeområden och infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Infrastruktur

Chalmers materialanalyslaboratorium

Infrastruktur

Materialvetenskap

Styrkeområden

Chalmers e-Commons

Infrastruktur

Mer information

Senast uppdaterat

2023-12-15