Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes Article Swipe
Sohrab R. Daemi
,
Chao Tan
,
Thomas G. Tranter
,
Thomas M. M. Heenan
,
Aaron Wade
,
Luis Salinas‐Farran
,
Alice V. Llewellyn
,
Xuekun Lu
,
Alessia Matruglio
,
Daniel Brett
,
Rhodri Jervis
,
Paul R. Shearing
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/smtd.202200887
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/smtd.202200887
X‐ray computed tomography (X‐ray CT) is a non‐destructive characterization technique that in recent years has been adopted to study the microstructure of battery electrodes. However, the often manual and laborious data analysis process hinders the extraction of useful metrics that can ultimately inform the mechanisms behind cycle life degradation. This work presents a novel approach that combines two convolutional neural networks to first locate and segment each particle in a nano‐CT LiNiMnCoO 2 (NMC) electrode dataset, and successively classifies each particle according to the presence of flaws or cracks within its internal structure. Metrics extracted from the computer vision segmentation are validated with respect to traditional threshold‐based segmentation, confirming that flawed particles are correctly identified as single entities. Successively, slices from each particle are analyzed by a pre‐trained classifier to detect the presence of flaws or cracks. The models are used to quantify microstructural evolution in uncycled and cycled NMC811 electrodes, as well as the number of flawed particles in a NMC622 electrode. As a proof‐of‐concept, a 3‐phase segmentation is also presented, whereby each individual flaw is segmented as a separate pixel label. It is anticipated that this analysis pipeline will be widely used in the field of battery research and beyond.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/smtd.202200887
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/smtd.202200887
- OA Status
- hybrid
- Cited By
- 11
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295733445
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- OpenAlex ID
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https://openalex.org/W4295733445Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/smtd.202200887Digital Object Identifier
- Title
-
Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion ElectrodesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-11Full publication date if available
- Authors
-
Sohrab R. Daemi, Chao Tan, Thomas G. Tranter, Thomas M. M. Heenan, Aaron Wade, Luis Salinas‐Farran, Alice V. Llewellyn, Xuekun Lu, Alessia Matruglio, Daniel Brett, Rhodri Jervis, Paul R. ShearingList of authors in order
- Landing page
-
https://doi.org/10.1002/smtd.202200887Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/smtd.202200887Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/smtd.202200887Direct OA link when available
- Concepts
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Segmentation, Artificial intelligence, Computer science, Convolutional neural network, Electrode, Pipeline (software), Battery (electricity), Classifier (UML), Pixel, Deep learning, Pattern recognition (psychology), Computer vision, Physics, Programming language, Power (physics), Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 4, 2024: 4, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5058730172, https://openalex.org/A5053233463 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I4210156703, https://openalex.org/I45129253 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile.value | 0.72148051 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |