Petrich, L;
Westhoff, D;
Feinauer, J;
Finegan, DP;
Daemi, SR;
Shearing, PR;
Schmidt, V;
(2017)
Crack detection in lithium-ion cells using machine learning.
Computational Materials Science
, 136
pp. 297-305.
10.1016/j.commatsci.2017.05.012.
Preview |
Text
Shearing_CrackDetection.pdf - Accepted Version Download (1MB) | Preview |
Abstract
It is an open question how the particle microstructure of a lithium-ion electrode influences a potential thermal runaway. In order to investigate this, information on the structural changes, in particular cracked particles, caused by the failure are desirable. For a reliable analysis of these changes a reasonably large amount of data is necessary, which necessitates automatic extraction of particle cracks from tomographic 3D image data. In this paper, a classification model is proposed which is able to decide whether a pair of particles is the result of breakage, of the image segmentation, or neither. The classifier is developed using simulated data based on a 3D stochastic particle model. Its validity is tested by applying the methodology to hand-labelled data from a real electrode. For this dataset, an overall accuracy of 73% is achieved.
| Type: | Article |
|---|---|
| Title: | Crack detection in lithium-ion cells using machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.commatsci.2017.05.012 |
| Publisher version: | http://dx.doi.org/10.1016/j.commatsci.2017.05.012 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Thermal runaway; Lithium-ion battery; Crack detection; Machine learning; 3D microstructure; Stochastic modelling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1564721 |
Archive Staff Only
 |
View Item |
