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Project

Micro-structural modelling and parameter identification of glass-fibre reinforced polymers through digital volume correlation and finite element analysis.

Driven by the need for lighter materials with better performance, models for the mechanical properties of fibre-reinforced composites have reached a significant level of maturity. The key bottleneck has now become the measurement and detailed understanding of the matrix and interface properties that those models require. While some incremental progress has been achieved, those measurements remain time-consuming,expensive, lab- or operator-dependent and often prone to large scatter. InSituPro therefore develops a method for measuring in-situ matrix and interfacial properties (see Figure 1) and uses this method to develop a fundamental understanding of key properties of the matrix and interface. This will be achieved by computed tomography (CT) of particle-filled glass fibre composites at voxel sizes below 500 nm. These experiments allow digital volume correlation (DVC) to achieve 3D strain resolutions down to 8-15 µm, which will enable the proposed data reduction schemes to extract the microscale matrix properties, the interfacial normal and shear strength and toughness, the friction coefficient for interfacial sliding, and fatigue debond growth rates. Since InSituPro tests composites with hundreds of fibres, the properties are measured in situ in conditions representative for real composites. This new methodology will enable fundamental questions related to the in-situ behaviour of the constituents to be answered. The newly developed understanding will empower a breakthrough in micromechanical modelling, which would catalyse the design and implementation of novel and improved composite materials.

Date:23 Mar 2022 →  Today
Keywords:digital volume correlation, characterisation, machine learning, glass fibre reinforced polymers, parameter identification, constitutive modelling
Disciplines:Kinetics, Physical chemistry of materials, Solid state and soft matter chemistry, Theory and design of materials, Continuum mechanics, Kinematics and dynamics, Data visualisation and imaging, Data visualisation and high-throughput image analysis, Visual data analysis, Machine learning and decision making, Computer vision
Project type:PhD project