The energy distribution of antimonyberyllium photoneutrons

Lalovic, M.; Werle, H.

doi:10.1016/0022-3107(70)90058-4

Cited by 11 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…X-rays on the other hand, with energies still well below the relevant photodisintegration 5 thresholds (e.g. 1.67 MeV for the stable isotope beryllium-9 [33]) can penetrate even larger car structures made of either CFRP or metal well enough to reconstruct highly resolved volumes -without inducing radioactive reactions. Exemplary CT cross sections from an analyzed CFRP sample can be seen in Figure 2.…”

Section: Computed Tomographymentioning

confidence: 99%

Automated detection of micrometer-cracks and delamination in CT volumes of previously stressed CFRP pressure rods

Schromm¹,

Holtmann²,

Koch³

et al. 2020

eJNDT

View full text Add to dashboard Cite

Industrial tasks that produce hundreds of terabytes of data per year require efficient evaluation tools for the purpose of saving valuable resources such as time or highly trained personnel. Therefore, processes with high automation potential need to be identified and put into practice. Before deploying a new tool, however, thorough investigation is required. It needs to be tested whether the achievable degree of automation of such a new tool yields results which are on one hand reliable and on the other hand of sufficient quality. This work evaluates the applicability of artificial neural networks (ANNs) to detecting micrometer-cracks and delaminations in reconstructed computed tomography (CT) volumes of previously stressed carbon fibre reinforced polymer (CFRP) pressure rods. Common network architectures, varying network parameters and different training sets have been investigated and compared in order to determine the combination that performs best. The constellation (network, hyperparameters, training data) that performed best reached an average precision (AP) of 0.87. Based on the rather small data set of approx. 1.6E3 images and the unstructured nature and great diversity of the investigated features, this result can be regarded as very good. The detected feature sizes ranged from approx. 100 micrometer to a centimeter in length and from tens of microns to a few hundred microns in width. The results suggest that artificial neural networks have the potential to be used reliably for the automatic detection of micrometer-cracks and delaminations in CT volumes of CFRP pressure rods, provided the data set used for training is large and diverse enough and the network is being updated when new data is available.

show abstract

Section: Computed Tomographymentioning

confidence: 99%