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Deep Learning Unlocks X-ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials.
Kopp, Reed; Joseph, Joshua; Ni, Xinchen; Roy, Nicholas; Wardle, Brian L.
Affiliation
  • Kopp R; Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Joseph J; MIT Quest for Intelligence, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Ni X; Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Roy N; Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Wardle BL; MIT Quest for Intelligence, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Adv Mater ; 34(11): e2107817, 2022 Mar.
Article in En | MEDLINE | ID: mdl-34800056
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Adv Mater Journal subject: BIOFISICA / QUIMICA Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Alemania

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Adv Mater Journal subject: BIOFISICA / QUIMICA Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Alemania