Data from: Deep learning unlocks X‐ray microtomography segmentation of multiclass microdamage in heterogeneous materials

Four-dimensional quantitative characterization of heterogeneous materials
using in situ synchrotron radiation computed tomography can reveal 3D
sub-micron features, particularly damage, evolving under load, leading to
improved materials. However, dataset size and complexity increasingly
require time-intensive and subjective semi-automatic segmentations. Here,
we present the first deep learning (DL) convolutional neural network (CNN)
segmentation of multiclass microscale damage in heterogeneous bulk
materials, teaching on advanced aerospace-grade composite damage using
≈65,000 (trained) human-segmented tomograms. The trained CNN machine
segments complex and sparse (<<1% of volume) composite
damage classes to ≈99.99% agreement, unlocking both objectivity and
efficiency, with nearly 100% of the human time eliminated, which
traditional rule-based algorithms do not approach. The trained machine is
found to perform as well or better than the human due to
‘machine-discovered’ human segmentation error, with machine improvements
manifesting primarily as new damage discovery and segmentation
augmentation/extension in artifact-rich tomograms. Interrogating a
high-level network hyperparametric space on two material configurations,
we find DL to be a disruptive approach to quantitative structure-property
characterization, enabling high-throughput knowledge creation (accelerated
by two orders of magnitude) via generalizable, ultra-high-resolution
feature segmentation.