Detection and characterisation of hidden damages in layered composites like Fibre laminates, e.g., Fibre Metal Laminates (FML), is still a challenge. X-ray imaging can be divided into two-dimensional transmission or reflection and three-dimensional tomography imaging using reconstruction algorithms to compute a three-dimensional view from slice images. Damages or defects can be classified roughly in layer delaminations, extended cracks, micro cracks (fibres and solid material layer), deformations, and impurities during manufacturing. Detection of such kind of damages and defects by visual inspection is a challenge, even using 3D CT data, and moreover using single 2D transmission images. For damage characterisation, micro-focus CT X-ray  scanner are used, providing a high resolution below 100 μm, but with the disadvantage of high scanning times (up to several hours).

Anomaly detectors based on advanced data-driven Machine Learning methods (here using Convolutional Neural Networks)  can be used to mark Regions-of-Interest (ROI) in images automatically (feature selection process). ROI feature extraction is the first stage of an automated damage diagnostic system providing damage detection, classification, and localisation. But data-driven methods require typically a sufficient large set of training examples (with respect to diversity and generality), which cannot be provided commonly in engineering and damage diagnostics (e.g., an impact damage can only be "created" one time and is not reversible). 

In this work, the challenges, limits, and detection accuracy of automated ROI damage feature detection from low-quality and low-resolution 2D X-ray image data using data-driven anomaly detectors are investigated and evaluated comparatively with high-quality and high-resolution 3D X-ray images obtained with a state-of-the-art X-ray microscope for advanced material characterization. In addition to experimental data, X-ray simulation is used to create an augmented training and test data set. The simulation is carried out with software based on the Beer-Lambert law to compute the absorption of light (i.e. photons) by 3D objects (here polygon meshes) and ray-tracing software.