Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials
Name
adma.2021107827_Main_revision_NoChangeTracking_Production_toDSPACE.pdf
Description
Accepted version
Size
1.58 MB
Format
Adobe PDF
Checksum (MD5)
5d8d527ebd6ade9844775f5e7bc0415d
Author(s)
Kopp, Reed
Joseph, Joshua
Ni, Xinchen
Roy, Nicholas
Wardle, Brian L
Date Issued
2022
Journal
Advanced Materials
Publisher
Wiley
Citation
Kopp, Reed, Joseph, Joshua, Ni, Xinchen, Roy, Nicholas and Wardle, Brian L. 2022. "Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials." Advanced Materials, 34 (11).
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/ADMA.202107817
