Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials

Abstract Four‐dimensional quantitative characterization of heterogeneous materials using in situ synchrotron radiation computed tomography can reveal 3D sub‐micrometer 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, the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale damage in heterogeneous bulk materials is presented, 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, DL is found to be a disruptive approach to quantitative structure–property characterization, enabling high‐throughput knowledge creation (accelerated by two orders of magnitude) via generalizable, ultrahigh‐resolution feature segmentation.