  Additive Manufacturing (AM) has emerged as a manufacturing process that
allows the direct production of samples from digital models. To ensure that
quality standards are met in all manufactured samples of a batch, X-ray
computed tomography (X-CT) is often used combined with automated anomaly
detection. For the latter, deep learning (DL) anomaly detection techniques are
increasingly, as they can be trained to be robust to the material being
analysed and resilient towards poor image quality. Unfortunately, most recent
and popular DL models have been developed for 2D image processing, thereby
disregarding valuable volumetric information.
  This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet) and
unsupervised (VAE, ceVAE, gmVAE, vqVAE) DL models for porosity analysis of AM
samples from X-CT images and extends them to accept 3D input data with a
3D-patch pipeline for lower computational requirements, improved efficiency and
generalisability. The supervised models were trained using the Focal Tversky
loss to address class imbalance that arises from the low porosity in the
training datasets. The output of the unsupervised models is post-processed to
reduce misclassifications caused by their inability to adequately represent the
object surface. The findings were cross-validated in a 5-fold fashion and
include: a performance benchmark of the DL models, an evaluation of the
post-processing algorithm, an evaluation of the effect of training supervised
models with the output of unsupervised models. In a final performance benchmark
on a test set with poor image quality, the best performing supervised model was
UNet++ with an average precision of 0.751 $\pm$ 0.030, while the best
unsupervised model was the post-processed ceVAE with 0.830 $\pm$ 0.003. The
VAE/ceVAE models demonstrated superior capabilities, particularly when
leveraging post-processing techniques.
