超声波频率数据集 - 域信号和机器学习输出,用于参数化离子电池电极的涂层和化过程
Erdogan Guk1,2, Mona Faraji Niri1,2, Hamidreza Farhadi Tolie1,2
1Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, United Kingdom.
Data in brief
|January 23, 2026
概括
这项研究发布了用于离子电池电极制造的超声波频域数据集. 这些数据集使线内质量控制和理解生产过程中的微观结构变化成为可能.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程优化 制造过程优化
- 非破坏性测试是指非破坏性测试.
背景情况:
- 在线,非破坏性诊断对于控制卷对卷电池制造中的电极质量至关重要,以减少成本和材料浪费.
- 现有的超声波脉冲回声数据集有限,特别是在电极层面,阻碍了过程意识的质量控制开发.
研究的目的:
- 传播离子电池电极的超声波频域数据集的开放访问.
- 提供一致的制造过程元数据和机器学习输出.
- 为了使电池制造业能够开发内联,基于物理的质量控制模型.
主要方法:
- 在电池电极的涂层和化过程中获取超声波频域数据.
- 在数据集中包含快利变换 (FFT) 频率和大小.
- 数据与制造过程元数据的调整,包括实验因素,质量,厚度和密度的设计.
主要成果:
- 在电极层面发布了第一个精选的频域超声波数据集.
- 该数据集包括FT频率和大小,以及关键制造参数.
- 能够检查制造过程中产生的微观结构变化而无需破坏.
结论:
- 传播的数据集支持对信号处理管道进行基准测试.
- 促进了电池电极制造在线,基于物理的质量控制模型的开发.
- 促进在电池生产中降低成本和提高材料效率方面的进步.
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