Condon Amariah, Buscarino Bailey, Moch Eric, Sehnert William J, Miles Owen, Herring Patrick K, Attia Peter M
Glimpse, 444 Somerville Avenue, Somerville, MA 02143, United States.
Data Brief. 2024 Jun 10;55:110614. doi: 10.1016/j.dib.2024.110614. eCollection 2024 Aug.
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.
电池技术对于全球电气化努力日益重要。然而，电池对微小的制造差异高度敏感，这些差异可能引发可靠性或安全问题。电池质量控制的一项重要技术是计算机断层扫描（CT），它广泛用于各种临床和工业应用中的无损三维检测。然而，从历史上看，CT扫描在大规模制造中的应用一直受到其低通量以及处理其大文件大小困难的限制。在这项工作中，我们展示了一个包含一千多个已生产的商用电池CT扫描图像的数据集。该数据集涵盖了各种化学组成（锂离子和钠离子）以及各种电池外形（圆柱形、软包和棱柱形）。我们总共评估了七种不同的电池类型。通过这个数据集可以观察到制造的可变性和电池缺陷的存在。这个数据集可能会引起从事电池技术、计算机视觉或两者研究的科学家和工程师的兴趣。
