Unravelling in-situ constituent properties in fibre-reinforced composites: digital volume correlation and machine learning applied to synchrotron computed tomography images KU Leuven
Numerical Analysis and Applied Mathematics (NUMA), Structural Composites and Alloys, Integrity and Nondestructive Testing (SCALINT)
Models are only as good as their input. Imagine that those inputs are lab- or operator-dependent, prone to large scatter, specimen size-dependent and measured in unrepresentative conditions. This would inevitably lead to unreliable model predictions as well as a lack of understanding of the features controlling the behaviour. Unfortunately, this situation applies to the state of the art in micromechanical modelling of fibre-reinforced ...