  This study presents a deep learning methodology using 3-dimensional (3D)
convolutional neural networks to detect defects in carbon fiber reinforced
polymer composites through volumetric ultrasonic testing data. Acquiring large
amounts of ultrasonic training data experimentally is expensive and
time-consuming. To address this issue, a synthetic data generation method was
extended to incorporate volumetric data. By preserving the complete volumetric
data, complex preprocessing is reduced, and the model can utilize spatial and
temporal information that is lost during imaging. This enables the model to
utilise important features that might be overlooked otherwise. The performance
of three architectures were compared. The first two architectures were
hand-designed to address the high aspect ratios between the spatial and
temporal dimensions. The first architecture reduced dimensionality in the time
domain and used cubed kernels for feature extraction. The second architecture
used cuboidal kernels to account for the large aspect ratios. The evaluation
included comparing the use of max pooling and convolutional layers for
dimensionality reduction, with the fully convolutional layers consistently
outperforming the models using max pooling. The third architecture was
generated through neural architecture search from a modified 3D Residual Neural
Network (ResNet) search space. Additionally, domain-specific augmentation
methods were incorporated during training, resulting in significant
improvements in model performance for all architectures. The mean accuracy
improvements ranged from 8.2% to 22.4%. The best performing models achieved
mean accuracies of 91.8%, 92.2%, and 100% for the reduction, constant, and
discovered architectures, respectively. Whilst maintaining a model size smaller
than most 2-dimensional (2D) ResNets.
