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.

使用 X 射线单投影和多投影（CT）图像进行自动损伤和缺陷检测的基于数据驱动建模的方法和挑战，主要包括数据和特征的变异性，数据特征标注（用于监督机器学习），以及缺乏地面实况数据等。

利用低质量 X 射线摄影术、通过模拟进行的合成 X 射线数据增强，以及机器学习自动检测铝合金压铸材料和纤维金属层压板中的隐藏损伤和杂质

Automated Detection of hidden Damages and Impurities in Aluminum Die  Casting Materials and Fibre-Metal Laminates using Low-quality X-ray  Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine  Learning