A dataset of over one thousand computed tomography scans of battery cells

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Bibliographic Details
Main Authors:	Condon, Amariah, Buscarino, Bailey, Moch, Eric, Sehnert, William J., Miles, Owen, Herring, Patrick K., Attia, Peter M.
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
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author	Condon, Amariah Buscarino, Bailey Moch, Eric Sehnert, William J. Miles, Owen Herring, Patrick K. Attia, Peter M.
author_facet	Condon, Amariah Buscarino, Bailey Moch, Eric Sehnert, William J. Miles, Owen Herring, Patrick K. Attia, Peter M.
contents	Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_02527
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	A dataset of over one thousand computed tomography scans of battery cells Condon, Amariah Buscarino, Bailey Moch, Eric Sehnert, William J. Miles, Owen Herring, Patrick K. Attia, Peter M. Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.
title	A dataset of over one thousand computed tomography scans of battery cells
topic	Computer Vision and Pattern Recognition Computational Engineering, Finance, and Science
url	https://arxiv.org/abs/2403.02527

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