  Detection and characterization of hidden defects, impurities, and damages in
layered composites like Fibre laminates, e.g., Fibre Metal Laminates (FML), as
well as in monolithic materials, e.g., aluminum die casting materials, is still
a challenge. This work discusses methods and challenges in data-driven modeling
of automated damage and defect detectors using X-ray single- and
multi-projection (CT) images. Three main issues are identified: Data and
feature variance, data feature labeling (for supervised machine learning), and
the missing ground truth. It will be shown that only simulation of data can
deliver a ground truth data set and accurate labeling. Noise has significant
impact on the feature detection and will be discussed. Data-driven feature
detectors are implemented with semantic pixel- or z-profile Convolutional
Neural Networks and LSTM Auto-encoders. Data is measured with three different
devices: A low-quality and low-cost (Low-Q), a mid- and a high-quality
(micro-CT, Mid-/High-Q) device. The goals of this work are the training of
robust and generalized feature detectors with synthetic data and the transition
from High- and Mid-Q laboratory measuring technologies towards in-field usable
technologies and methods.
