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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202107817
|2 doi
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|a pubmed24n1111.xml
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Kopp, Reed
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Deep Learning Unlocks X-ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 21.03.2022
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|a Date Revised 21.03.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a 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
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|a Journal Article
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|a 3D multiclass damage
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| 650 |
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4 |
|a deep learning
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| 650 |
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|a heterogeneous materials
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| 650 |
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|a machine learning
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| 650 |
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|a material characterization
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| 700 |
1 |
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|a Joseph, Joshua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ni, Xinchen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Roy, Nicholas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wardle, Brian L
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 11 vom: 01. März, Seite e2107817
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
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|g volume:34
|g year:2022
|g number:11
|g day:01
|g month:03
|g pages:e2107817
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| 856 |
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|u http://dx.doi.org/10.1002/adma.202107817
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