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.