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

Kopp, Reed1; Joseph, Joshua1; Ni, Xinchen1; Roy, Nicholas1; Wardle, Brian L.1

Published Nov 20, 2021; Updated Dec 15, 2021 on Dryad. https://doi.org/10.5061/dryad.ffbg79cwb

Data files

Dec 15, 2021 version files 215.89 GB

Abstract

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.