Daemi, Sohrab R;
Tan, Chun;
Tranter, Thomas G;
Heenan, Thomas MM;
Wade, Aaron;
Salinas-Farran, Luis;
Llewellyn, Alice V;
... Shearing, Paul R; + view all
(2022)
Computer-Vision-Based Approach to Classify and Quantify Flaws in Li-Ion Electrodes.
Small Methods
, Article e2200887. 10.1002/smtd.202200887.
(In press).
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Abstract
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 LiNiMnCoO2 (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.
| Type: | Article |
|---|---|
| Title: | Computer-Vision-Based Approach to Classify and Quantify Flaws in Li-Ion Electrodes |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/smtd.202200887 |
| Publisher version: | https://doi.org/10.1002/smtd.202200887 |
| Language: | English |
| Additional information: | © 2022 The Authors. Small Methods published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | computer vision, convolutional networks, lithium-ion batteries, mask R-CNN, nano X-ray tomography |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10155966 |
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